{"title":"Optimization Books","description":"","products":[{"product_id":"introduction-to-online-convex-optimization-second-edition-adaptive-computation-and-machine-learning-adaptive-computation-and-machine-learning-series-9780262046985","title":"Introduction to Online Convex Optimization Second","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cb\u003eNew edition of a graduate-level textbook on that focuses on online convex optimization, a machine learning framework that views optimization as a process.\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003eIn many practical applications, the environment is so complex that it is not feasible to lay out a comprehensive theoretical model and use classical algorithmic theory and\/or mathematical optimization. \u003ci\u003eIntroduction to Online Convex Optimization\u003c\/i\u003e presents a robust machine learning approach that contains elements of mathematical optimization, game theory, and learning theory: an optimization method that learns from experience as more aspects of the problem are observed. This view of optimization as a process has led to some spectacular successes in modeling and systems that have become part of our daily lives. \u003cbr\u003e\u003cbr\u003eBased on the “Theoretical Machine Learning” course taught by the author at Princeton University, the second edition of this widely used graduate level text features:\u003cbr\u003e\u003cli\u003eThoroughly updat\u003c\/li\u003e","brand":"MIT Press Ltd","offers":[{"title":"Default Title","offer_id":48733458497879,"sku":"9780262046985","price":51.3,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780262046985.jpg?v=1720000156"},{"product_id":"applications-of-lie-groups-to-differential-equations-9780387950006","title":"Applications of Lie Groups to Differential","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e1 Introduction to Lie Groups.- 1.1. Manifolds.- 1.2. Lie Groups.- 1.3. Vector Fields.- 1.4. Lie Algebras.- 1.5. Differential Forms.- Notes.- Exercises.- 2 Symmetry Groups of Differential Equations.- 2.1. Symmetries of Algebraic Equations.- 2.2. Groups and Differential Equations.- 2.3. Prolongation.- 2.4. Calculation of Symmetry Groups.- 2.5. Integration of Ordinary Differential Equations.- 2.6. Nondegeneracy Conditions for Differential Equations.- Notes.- Exercises.- 3 Group-Invariant Solutions.- 3.1. Construction of Group-Invariant Solutions.- 3.2. Examples of Group-Invariant Solutions.- 3.3. Classification of Group-Invariant Solutions.- 3.4. Quotient Manifolds.- 3.5. Group-Invariant Prolongations and Reduction.- Notes.- Exercises.- 4 Symmetry Groups and Conservation Laws.- 4.1. The Calculus of Variations.- 4.2. Variational Symmetries.- 4.3. Conservation Laws.- 4.4. Noether's Theorem.- Notes.- Exercises.- 5 Generalized Symmetries.- 5.1. Generalized Symmetries of Differential Equations.- 5.2. Récursion Operators, Master Symmetries and Formal Symmetries.- 5.3. Generalized Symmetries and Conservation Laws.- 5.4. The Variational Complex.- Notes.- Exercises.- 6 Finite-Dimensional Hamiltonian Systems.- 6.1. Poisson Brackets.- 6.2. Symplectic Structures and Foliations.- 6.3. Symmetries, First Integrals and Reduction of Order.- Notes.- Exercises.- 7 Hamiltonian Methods for Evolution Equations.- 7.1. Poisson Brackets.- 7.2. Symmetries and Conservation Laws.- 7.3. Bi-Hamiltonian Systems.- Notes.- Exercises.- References.- Symbol Index.- Author Index.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e1 Introduction to Lie Groups.- 1.1. Manifolds.- Change of Coordinates.- Maps Between Manifolds.- The Maximal Rank Condition.- Submanifolds.- Regular Submanifolds.- Implicit Submanifolds.- Curves and Connectedness.- 1.2. Lie Groups.- Lie Subgroups.- Local Lie Groups.- Local Transformation Groups.- Orbits.- 1.3. Vector Fields.- Flows.- Action on Functions.- Differentials.- Lie Brackets.- Tangent Spaces and Vectors Fields on Submanifolds.- Frobenius’ Theorem.- 1.4. Lie Algebras.- One-Parameter Subgroups.- Subalgebras.- The Exponential Map.- Lie Algebras of Local Lie Groups.- Structure Constants.- Commutator Tables.- Infinitesimal Group Actions.- 1.5. Differential Forms.- Pull-Back and Change of Coordinates.- Interior Products.- The Differential.- The de Rham Complex.- Lie Derivatives.- Homotopy Operators.- Integration and Stokes’ Theorem.- Notes.- Exercises.- 2 Symmetry Groups of Differential Equations.- 2.1. Symmetries of Algebraic Equations.- Invariant Subsets.- Invariant Functions.- Infinitesimal Invariance.- Local Invariance.- Invariants and Functional Dependence.- Methods for Constructing Invariants.- 2.2. Groups and Differential Equations.- 2.3. Prolongation.- Systems of Differential Equations.- Prolongation of Group Actions.- Invariance of Differential Equations.- Prolongation of Vector Fields.- Infinitesimal Invariance.- The Prolongation Formula.- Total Derivatives.- The General Prolongation Formula.- Properties of Prolonged Vector Fields.- Characteristics of Symmetries.- 2.4. Calculation of Symmetry Groups.- 2.5. Integration of Ordinary Differential Equations.- First Order Equations.- Higher Order Equations.- Differential Invariants.- Multi-parameter Symmetry Groups.- Solvable Groups.- Systems of Ordinary Differential Equations.- 2.6. Nondegeneracy Conditions for Differential Equations.- Local Solvability.- In variance Criteria.- The Cauchy—Kovalevskaya Theorem.- Characteristics.- Normal Systems.- Prolongation of Differential Equations.- Notes.- Exercises.- 3 Group-Invariant Solutions.- 3.1. Construction of Group-Invariant Solutions.- 3.2. Examples of Group-Invariant Solutions.- 3.3. Classification of Group-Invariant Solutions.- The Adjoint Representation.- Classification of Subgroups and Subalgebras.- Classification of Group-Invariant Solutions.- 3.4. Quotient Manifolds.- Dimensional Analysis.- 3.5. Group-Invariant Prolongations and Reduction.- Extended Jet Bundles.- Differential Equations.- Group Actions.- The Invariant Jet Space.- Connection with the Quotient Manifold.- The Reduced Equation.- Local Coordinates.- Notes.- Exercises.- 4 Symmetry Groups and Conservation Laws.- 4.1. The Calculus of Variations.- The Variational Derivative.- Null Lagrangians and Divergences.- Invariance of the Euler Operator.- 4.2. Variational Symmetries.- Infinitesimal Criterion of Invariance.- Symmetries of the Euler—Lagrange Equations.- Reduction of Order.- 4.3. Conservation Laws.- Trivial Conservation Laws.- Characteristics of Conservation Laws.- 4.4. Noether’s Theorem.- Divergence Symmetries.- Notes.- Exercises.- 5 Generalized Symmetries.- 5.1. Generalized Symmetries of Differential Equations.- Differential Functions.- Generalized Vector Fields.- Evolutionary Vector Fields.- Equivalence and Trivial Symmetries.- Computation of Generalized Symmetries.- Group Transformations.- Symmetries and Prolongations.- The Lie Bracket.- Evolution Equations.- 5.2. Récursion Operators, Master Symmetries and Formal Symmetries.- Frechet Derivatives.- Lie Derivatives of Differential Operators.- Criteria for Recursion Operators.- The Korteweg—de Vries Equation.- Master Symmetries.- Pseudo-differential Operators.- Formal Symmetries.- 5.3. Generalized Symmetries and Conservation Laws.- Adjoints of Differential Operators.- Characteristics of Conservation Laws.- Variational Symmetries.- Group Transformations.- Noether’s Theorem.- Self-adjoint Linear Systems.- Action of Symmetries on Conservation Laws.- Abnormal Systems and Noether’s Second Theorem.- Formal Symmetries and Conservation Laws.- 5.4. The Variational Complex.- The D-Complex.- Vertical Forms.- Total Derivatives of Vertical Forms.- Functionals and Functional Forms.- The Variational Differential.- Higher Euler Operators.- The Total Homotopy Operator.- Notes.- Exercises.- 6 Finite-Dimensional Hamiltonian Systems.- 6.1. Poisson Brackets.- Hamiltonian Vector Fields.- The Structure Functions.- The Lie-Poisson Structure.- 6.2. Symplectic Structures and Foliations.- The Correspondence Between One-Forms and Vector Fields.- Rank of a Poisson Structure.- Symplectic Manifolds.- Maps Between Poisson Manifolds.- Poisson Submanifolds.- Darboux’ Theorem.- The Co-adjoint Representation.- 6.3. Symmetries, First Integrals and Reduction of Order.- First Integrals.- Hamiltonian Symmetry Groups.- Reduction of Order in Hamiltonian Systems.- Reduction Using Multi-parameter Groups.- Hamiltonian Transformation Groups.- The Momentum Map.- Notes.- Exercises.- 7 Hamiltonian Methods for Evolution Equations.- 7.1. Poisson Brackets.- The Jacobi Identity.- Functional Multi-vectors.- 7.2. Symmetries and Conservation Laws.- Distinguished Functionals.- Lie Brackets.- Conservation Laws.- 7.3. Bi-Hamiltonian Systems.- Recursion Operators.- Notes.- Exercises.- References.- Symbol Index.- Author Index.","brand":"Springer-Verlag New York Inc.","offers":[{"title":"Default Title","offer_id":48733727064407,"sku":"9780387950006","price":41.79,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780387950006.jpg?v=1720001407"},{"product_id":"optimization-for-chemical-and-biochemical-engineering-9781107106833","title":"Optimization for Chemical and Biochemical","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eDiscover the subject of optimization in a new light with this modern and unique treatment. Includes a thorough exposition of applications and algorithms in sufficient detail for practical use, while providing you with all the necessary background in a self-contained manner. Features a deeper consideration of optimal control, global optimization, optimization under uncertainty, multiobjective optimization, mixed-integer programming and model predictive control. Presents a complete coverage of formulations and instances in modelling where optimization can be applied for quantitative decision-making. As a thorough grounding to the subject, covering everything from basic to advanced concepts and addressing real-life problems faced by modern industry, this is a perfect tool for advanced undergraduate and graduate courses in chemical and biochemical engineering.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e'This book offers a very clear, uncluttered presentation of key ideas of optimisation in rigorous form and with plenty of examples from a decade of research and educational experience. It offers an exceptional resource for educators and students of optimisation methods, as well as a valuable reference text to practitioners.' Alexei Lapkin, University of Cambridge\u003cbr\u003e'This excellent book brings together important and up-to-date elements of the theory and practice of optimisation with application to chemical and biochemical engineering. It's an ideal reference for students on advanced courses or for researchers in the field.' Nilay Shah, Imperial College\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003ePart I. Overview of Optimization: 1. Introduction to optimization; Part II. From General Mathematical Background to General Nonlinear Programming Problems (NLP): 2. General concepts; 3. Convexity; 4. Quadratic functions; 5. Minimization in one dimension; 6. Unconstrained multivariate gradient-based minimization; 7. Constrained nonlinear programming problems (NLP); 8. Penalty and barrier function methods; 9. Interior point methods (IPMs), a detailed analysis; Part III. Formulation and Solution of Linear Programming (LP) Problem Models: 10. Introduction to LP models; 11. Numerical solution of LP problems using the simplex method; 12. A sampler of LP problem formulations; 13. Regression revisited, using LP to fit linear models; 14. Network flow problems; 15, LP and sensitivity analysis, in brief; Part IV. Further Topics in Optimization: 16. Multiobjective optimilzation problem (MOP); 17. Stochastic optimization problem (SOP); 18. Mixed integer programming; 19. Global optimization; 20. Optical control problems (dynamic optimization); 21. System identification and model predictive control.","brand":"Cambridge University Press","offers":[{"title":"Default Title","offer_id":48738239054167,"sku":"9781107106833","price":73.99,"currency_code":"GBP","in_stock":true}]},{"product_id":"understanding-process-dynamics-and-control-9781107035584","title":"Understanding Process Dynamics and Control","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003ePresenting a fresh look at process control, this new text demonstrates state-space approach shown in parallel with the traditional approach to explain the strategies used in industry today. Modern time-domain and traditional transform-domain methods are integrated throughout and explain the advantages and limitations of each approach; the fundamental theoretical concepts and methods of process control are applied to practical problems. To ensure understanding of the mathematical calculations involved, MATLAB is included for numeric calculations and MAPLE for symbolic calculations, with the math behind every method carefully explained so that students develop a clear understanding of how and why the software tools work. Written for a one-semester course with optional advanced-level material, features include solved examples, cases that include a number of chemical reactor examples, chapter summaries, key terms, and concepts, as well as over 240 end-of-chapter problems, focused computati\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e'Provides a fresh perspective through the integrated coverage of modern state-space and traditional transfer function approaches. The mathematical derivations are detailed and accessible, aiding clear understanding of the basic as well as the more advanced topics.' Prodromos Daoutidis, University of Minnesota\u003cbr\u003e'Breaking new ground in the crowded field of process control textbooks, this book provides the foundation for teaching a modern undergraduate process control course in the twenty-first century. It is exceptionally well written and organized, and includes numerous examples, making it a must-have for all process control researchers, students and engineers.' Panagiotis D. Christofides, University of California, Los Angeles\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003eContents; Preface; 1. Introduction; 2. Dynamic Models for Chemical Process Systems; 3. First Order Systems; 4. Connections of First Order Systems; 5. Second Order Systems; 6. Linear Higher Order Systems; 7. Eigenvalue Analysis – Asymptotic Stability; 8. Transfer Function Analysis of the Input\/Output Behavior; 9. Frequency Response; 10. The Feedback Control System; 11. Block Diagram Reduction and Transient Response Calculation in a Feedback Control System; 12. Steady-State and Stability Analysis of the Closed Loop System; 13. State Space Description and Analysis of the Closed Loop System; 14. Systems with Dead Time; 15. Parametric Analysis of Closed Loop Dynamics – Root Locus Diagrams; 16. Optimal Selection of Controller Parameters; 17. Bode and Nyquist Stability Criteria – Gain and Phase Margins; 18. Multiple-Input-Multiple-Output Systems; 19. Synthesis of Model-Based Feedback Controllers; 20. Cascade, Ratio and Feedforward Control; Appendix A; Appendix B.","brand":"Cambridge University Press","offers":[{"title":"Default Title","offer_id":48738239807831,"sku":"9781107035584","price":90.24,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781107035584.jpg?v=1723811851"},{"product_id":"integer-linear-programming-in-computational-and-systems-biology-9781108421768","title":"Integer Linear Programming in Computational and","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eInteger linear programming (ILP) is a versatile modeling and optimization technique that is increasingly used in non-traditional ways in biology, with the potential to transform biological computation. However, few biologists know about it. This how-to and why-do text introduces ILP through the lens of computational and systems biology. It uses in-depth examples from genomics, phylogenetics, RNA, protein folding, network analysis, cancer, ecology, co-evolution, DNA sequencing, sequence analysis, pedigree and sibling inference, haplotyping, and more, to establish the power of ILP. This book aims to teach the logic of modeling and solving problems with ILP, and to teach the practical ''work flow'' involved in using ILP in biology. Written for a wide audience, with no biological or computational prerequisites, this book is appropriate for entry-level and advanced courses aimed at biological and computational students, and as a source for specialists. Numerous exercises and accompanying so\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e'In his classic accessible teaching style, Gusfield teaches us why integer linear programming (ILP) is the most useful mathematical idea you've probably never heard of. Read this book to learn how what you don't know can hurt you, and why ILP should be your new favorite method.' Trey Ideker, University of California, San Diego\u003cbr\u003e'Once again, Dan Gusfield has written an accessible book that shows that algorithmic rigor need not be sacrificed when solving real-world problems. He explains integer linear programming in the context of real-world biology. In doing so, the reader has an enriched understanding of both algorithmic details and the challenges in modern biology.' Russ Altman, Stanford University, California\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003ePreface; Part I: 1. A fly-over introduction; 2. Biological networks and graphs; 3. Character compatibility; 4. Near-cliques; 5. Parsimony in phylogenetics; 6. RNA folding; 7. Protein problems; 8. Tanglegrams; 9. TSP in genomics; 10. Molecular sequence analysis; 11. Metabolic networks and engineering; 12. ILP idioms; Part II: 13. Communities and cuts; 14. Corrupted data and extensions in phylogenetics; 15. More tanglegrams and trees; 16. Return to Steiner-trees; 17. Exploiting protein networks; 18. More strings and sequences; 19. Max-likelihood pedigrees; 20. Haplotyping; 21. Extended exercises; 22. What's next?; Epilogue: opinionated comments.","brand":"Cambridge University Press","offers":[{"title":"Default Title","offer_id":48738287354199,"sku":"9781108421768","price":49.49,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781108421768.jpg?v=1723811892"},{"product_id":"design-optimization-using-matlab-and-solidworks-9781108491600","title":"Design Optimization using MATLAB and SOLIDWORKS","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eA unique text integrating numerics, mathematics and applications to provide a hands-on approach to using optimization techniques, this mathematically accessible textbook emphasises conceptual understanding and importance of theorems rather than elaborate proofs. It allows students to develop fundamental optimization methods before delving into MATLAB''s optimization toolbox, and to link MATLAB''s results with the results from their own code. Following a practical approach, the text demonstrates several applications, from error-free analytic examples to truss (size) optimization, and 2D and 3D shape optimization, where numerical errors are inevitable. The principle of minimum potential energy is discussed to highlight the deep relationship between engineering and optimization. MATLAB code in every chapter illustrates key concepts and the text demonstrates the coupling between MATLAB and SOLIDWORKS for design optimization. A wide variety of optimization problems are covered including con\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e'Design Optimization using MATLAB and SOLIDWORKS by Dr. Suresh provides an excellent review of various optimization methods, especially for structural problems. Its introduction to MATLAB would help students who have had little experience with this software to become familiar with it quickly and apply it to some of the basic optimization problems.' Hamid Torab, Gannon University\u003cbr\u003e'Dr. Suresh's text brings his contributions to shape optimization into the classroom by connecting optimization, MATLAB, SOLIDWORKS, and SOLIDLAB into a single textbook. This text enables the reader to build upon this research accomplishment. I look forward to seeing what my students can achieve with this textbook at their fingertips.' Cameron Turner, Clemson University\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003ePreface; Table of Contents; 1. Introduction; 2. Modeling; 3. Introduction to MATLAB; 4. Unconstrained Optimization: Theory; 5. Unconstrained Optimization: Algorithms; 6. MATLAB Optimization Toolbox; 7. Constrained Optimization; 8. Special Classes of Problems; 9. Truss Analysis; 10. Size Optimization of Trusses; 11. Gradient Computation; 12. Finite Element Analysis in 2D; 13. Shape Optimization in 2D; 14. Finite Element Analysis in 3D; 15. SOLIDLAB: A SOLIDWORKS-MATLAB Interface; 16. Shape Optimization using SOLIDLAB; 17. Appendix; 18. References.","brand":"Cambridge University Press","offers":[{"title":"Default Title","offer_id":48738312028503,"sku":"9781108491600","price":75.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781108491600.jpg?v=1723811916"},{"product_id":"dynamic-systems-and-control-engineering-9781108831055","title":"Dynamic Systems and Control Engineering","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eUsing a step-by-step approach, this textbook provides a modern treatment of the fundamental concepts, analytical techniques, and software tools used to perform multi-domain modeling, system analysis and simulation, linear control system design and implementation, and advanced control engineering. Chapters follow a progressive structure, which builds from modeling fundamentals to analysis and advanced control while showing the interconnections between topics, and solved problems and examples are included throughout. Students can easily recall key topics and test understanding using Review Note and Concept Quiz boxes, and over 200 end-of-chapter homework exercises with accompanying Concept Keys are included. Focusing on practical understanding, students will gain hands-on experience of many modern MATLAB tools, including Simulink and physical modeling in Simscape.  With a solutions manual, MATLAB code, and Simulink\/Simscape files available online, this is ideal for senior undergraduates \u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e'Lucid and easy to read. It methodically explains classic control theory, from modeling of multi-domain systems to digital control. The detailed examples and end-of-chapter problems make it an excellent choice as a textbook for students in different engineering and science disciplines. MATLAB® and Simulink® instructions are a big plus.' Pezhman Hassanpour, California State Polytechnic University\u003cbr\u003e'Dynamic Systems and Control Engineering by Jalili and Candelino is one of the most organized and easily understood basic texts in this area. They have taken what is a nebulous subject for many students and made it less daunting through their use of numerous examples across several disciplines. The text is laid out well, logical from the basic systems modeling, to their analyses, and their control. They have taken a progressive approach to build on previous knowledge as the topics become more advanced. Their straightforward mathematical models are reinforced through MATLAB® and Simulink®, with basic user guides for the software. This allows the student to learn about controls by doing controls.' Robert Rabb, Penn State University\u003cbr\u003e'I have enjoyed reading this book very much for several reasons. This is the most complete text on dynamic systems and automatic control, with a rich set of examples and deep analytic treatment, along with an application viewpoint. The authors have rich experience in research and teaching in dynamic systems and control engineering in several world-class universities worldwide.' Reza N. Jazar, Royal Melbourne Institute of Technology University\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003ePart I. Modeling of Multi-Domain Dynamic Systems: 1. Introduction to Dynamic Systems; 2. Modeling of Mechanical Systems; 3. Modeling of Electrical Systems; 4. Modeling of Multi-Domain Systems; Part II. Analysis of Multi-Domain Dynamic Systems: 5. Dynamic System Response; 6. System Response Characteristics; 7. System Transfer Function Analysis; Part III. Introduction to Feedback Systems: 8. Analysis of Feedback Control Systems; 9. Root Locus Techniques; 10. Frequency Domain Methods; 11. Implementation of Feedback Control Systems; Part IV. Analysis and Feedback Control of Modern Systems: 12. State-Space Representation and Analysis; 13. State-Space Control System Design; 14. Advanced Topics in Control Engineering.","brand":"Cambridge University Press","offers":[{"title":"Default Title","offer_id":48738338472279,"sku":"9781108831055","price":89.99,"currency_code":"GBP","in_stock":true}]},{"product_id":"advanced-optimization-for-process-systems-engineering-9781108831659","title":"Advanced Optimization for Process Systems","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eBased on the author''s forty years of teaching experience, this unique textbook covers both basic and advanced concepts of optimization theory and methods for process systems engineers. Topics covered include continuous, discrete and logic optimization (linear, nonlinear, mixed-integer and generalized disjunctive programming), optimization under uncertainty (stochastic programming and flexibility analysis), and decomposition techniques (Lagrangean and Benders decomposition). Assuming only a basic background in calculus and linear algebra, it enables easy understanding of mathematical reasoning, and numerous examples throughout illustrate key concepts and algorithms. End-of-chapter exercises involving theoretical derivations and small numerical problems, as well as in modeling systems like GAMS, enhance understanding and help put knowledge into practice. Accompanied by two appendices containing web links to modeling systems and models related to applications in PSE, this is an essential\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e'Authored by Ignacio Grossmann, the creator and key developer of the field of mixed integer nonlinear programming, this outstanding textbook provides a thorough and comprehensive treatment of fundamental concepts, optimization models and effective solution strategies for discrete and continuous optimization. It is an essential, 'must-have' reference for all students, researchers and practitioners in process systems engineering.' Lorenz Biegler, Carnegie Mellon University\u003cbr\u003e'From the globally recognized leading authority in the field of process systems engineering, this long-awaited book will definitely become the standard reference for anyone interested in optimization. It is very well thought and written, with excellent presentation of the material. The theory is described in a very effective, rigorous, and clear way, with appropriate explanations and examples used throughout, covering traditional topics such as linear and nonlinear optimization concepts and mixed-integer linear programming, along with more advanced topics, such as disjunctive programming, global optimization, and stochastic programming. A real gem and a must read!' Stratos Pistikopoulos, Texas A \u0026amp; M University\u003cbr\u003e'Excellent coverage of the basic concepts and approaches developed in the area of process systems engineering in the last forty years. A unique book that can be easily adapted to advanced undergraduate and graduate-level classes to provide overall guidance to different tools that can be used to model and optimize complex engineering problems. I am certainly looking forward to using it in my class on mathematical modeling and optimization principles.' Marianthi Ierapetritou, University of Delaware\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003ePreface; 1. Optimization in process systems engineering; 2. Solving nonlinear equations; 3. Basic theoretical concepts in optimization; 4. Nonlinear programming algorithms; 5. Linear programming; 6. Mixed-integer programming models; 7. Systematic modeling of constraints with logic; 8. Mixed-integer linear programming; 9 Mixed-integer nonlinear programming; 10. Generalized disjunctive programming; 11. Constraint programming; 12. Nonconvex optimization; 13. Lagrangean decomposition; 14. Stochastic programming; 15. Flexibility analysis; Appendix A. Modeling systems and optimization software; Appendix B. Optimization models for process systems engineering; References; Index.","brand":"Cambridge University Press","offers":[{"title":"Default Title","offer_id":48738338832727,"sku":"9781108831659","price":71.24,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781108831659.jpg?v=1723811946"},{"product_id":"classical-and-modern-optimization-9781800610866","title":"Classical And Modern Optimization","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eThe quest for the optimal is ubiquitous in nature and human behavior. The field of mathematical optimization has a long history and remains active today, particularly in the development of machine learning.Classical and Modern Optimization presents a self-contained overview of classical and modern ideas and methods in approaching optimization problems. The approach is rich and flexible enough to address smooth and non-smooth, convex and non-convex, finite or infinite-dimensional, static or dynamic situations. The first chapters of the book are devoted to the classical toolbox: topology and functional analysis, differential calculus, convex analysis and necessary conditions for differentiable constrained optimization. The remaining chapters are dedicated to more specialized topics and applications.Valuable to a wide audience, including students in mathematics, engineers, data scientists or economists, Classical and Modern Optimization contains more than 200 exercises to assist with self-study or for anyone teaching a third- or fourth-year optimization class.","brand":"World Scientific Europe Ltd","offers":[{"title":"Default Title","offer_id":48741753291095,"sku":"9781800610866","price":58.5,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781800610866.jpg?v=1720058691"},{"product_id":"introduction-to-geometric-control-9783031020728","title":"Introduction to Geometric Control","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eThis text is an enhanced, English version of the Russian edition, published in early 2021 and is appropriate for an introductory course in geometric control theory. The concise presentation provides an accessible treatment of the subject for advanced undergraduate and graduate students in theoretical and applied mathematics, as well as to experts in classic control theory for whom geometric methods may be introduced. Theory is accompanied by characteristic examples such as stopping a train, motion of mobile robot, Euler elasticae, Dido's problem, and rolling of the sphere on the plane. Quick foundations to some recent topics of interest like control on Lie groups and sub-Riemannian geometry are included. Prerequisites include only a basic knowledge of calculus, linear algebra, and ODEs; preliminary knowledge of control theory is not assumed. The applications problems-oriented approach discusses core subjects and encourages the reader to solve related challenges independently. Highly-motivated readers can acquire working knowledge of geometric control techniques and progress to studying control problems and more comprehensive books on their own. Selected sections provide exercises to assist in deeper understanding of the material.\u003c\/p\u003e\u003cp\u003eControllability and optimal control problems are considered for nonlinear nonholonomic systems on smooth manifolds, in particular, on Lie groups. For the controllability problem, the following questions are considered: controllability of linear systems, local controllability of nonlinear systems, Nagano–Sussmann Orbit theorem, Rashevskii–Chow theorem, Krener's theorem. For the optimal control problem, Filippov's theorem is stated, invariant formulation of Pontryagin maximum principle on manifolds is given, second-order optimality conditions are discussed, and the sub-Riemannian problem is studied in detail. Pontryagin maximum principle is proved for sub-Riemannian problems, solution to the sub-Riemannian problems on the Heisenberg group, the group of motions of the plane, and the Engel group is described.\u003cbr\u003e\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e1. Introduction.- 2. Controllability problem.- 3. Optimal control problem.- 4. Solution to optimal control problems.- 5. Conclusion.- A. Elliptic integrals, functions and equation of pendulum.- Bibliography and further reading.- Index.","brand":"Springer International Publishing AG","offers":[{"title":"Default Title","offer_id":48743065420119,"sku":"9783031020728","price":43.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9783031020728.jpg?v=1720063957"},{"product_id":"introduction-to-combinatorial-optimization-9783031105944","title":"Introduction to Combinatorial Optimization","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eIntroductory courses in combinatorial optimization are popular at the upper undergraduate\/graduate levels in computer science, industrial engineering, and business management\/OR, owed to its wide applications in these fields. There are several published textbooks that treat this course and the authors have used many of them in their own teaching experiences.  This present text fills a gap and is organized with a stress on methodology and relevant content, providing a step-by-step approach for the student to become proficient in solving combinatorial optimization problems. Applications and problems are considered via recent technology developments including wireless communication, cloud computing, social networks, and machine learning, to name several, and the reader is led to the frontiers of combinatorial optimization. Each chapter presents common problems, such as minimum spanning tree, shortest path, maximum matching, network flow, set-cover, as well as key algorithms, such as greedy algorithm, dynamic programming, augmenting path, and divide-and-conquer. Historical notes, ample exercises in every chapter, strategically placed graphics, and an extensive bibliography are amongst the gems of this textbook.\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e“This book introduces combinatorial optimization with a methodology-oriented organization. It targets undergraduate and graduate students and contains a good mix of theoretical results (with proof) and examples, which helps the reader acquire ideas and concepts. The chapters end with a list of exercises for the students.” (Francisco Chicano, Mathematical Reviews, January, 2024)\u003cbr\u003e“The book can appropriately be used as a textbook in a graduate course. All the algorithms are clearly explained and presented. It is a very valuable book for successful application of real problems from combinatorial optimization. … this book is an excellent contribution to the field of combinatorial optimization, and it is highly recommended to the students and researchers in optimization.” (Samir Kumar Neogy, zbMATH 1512.90001, 2023)\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e1. Introduction.-2. Divide-and-Conquer.- 3. Dynamic Programming and Shortest Path.- 4. Greedy Algorithm and Spanning Tree.- 5. Incremental Method and Maximum Network Flow.- 6. Linear Programming.- 7. Primal-Dual Methods and Minimum Cost Flow.- 8. NP-hard Problems and Approximation Algorithms.- 9. Restriction and Steiner Tree.- 10. Greedy Approximation and Submodular Optimization.- 11. Relaxation and Rounding. 12. Nonsubmodular Optimization.- Bibliography.","brand":"Springer International Publishing AG","offers":[{"title":"Default Title","offer_id":48743069811031,"sku":"9783031105944","price":38.24,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9783031105944.jpg?v=1720063980"},{"product_id":"introduction-to-combinatorial-optimization-9783031116841","title":"Introduction to Combinatorial Optimization","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eIntroductory courses in combinatorial optimization are popular at the upper undergraduate\/graduate levels in computer science, industrial engineering, and business management\/OR, owed to its wide applications in these fields. There are several published textbooks that treat this course and the authors have used many of them in their own teaching experiences.  This present text fills a gap and is organized with a stress on methodology and relevant content, providing a step-by-step approach for the student to become proficient in solving combinatorial optimization problems. Applications and problems are considered via recent technology developments including wireless communication, cloud computing, social networks, and machine learning, to name several, and the reader is led to the frontiers of combinatorial optimization. Each chapter presents common problems, such as minimum spanning tree, shortest path, maximum matching, network flow, set-cover, as well as key algorithms, such as greedy algorithm, dynamic programming, augmenting path, and divide-and-conquer. Historical notes, ample exercises in every chapter, strategically placed graphics, and an extensive bibliography are amongst the gems of this textbook.\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e“This book introduces combinatorial optimization with a methodology-oriented organization. It targets undergraduate and graduate students and contains a good mix of theoretical results (with proof) and examples, which helps the reader acquire ideas and concepts. The chapters end with a list of exercises for the students.” (Francisco Chicano, Mathematical Reviews, January, 2024)\u003cbr\u003e“The book can appropriately be used as a textbook in a graduate course. All the algorithms are clearly explained and presented. It is a very valuable book for successful application of real problems from combinatorial optimization. … this book is an excellent contribution to the field of combinatorial optimization, and it is highly recommended to the students and researchers in optimization.” (Samir Kumar Neogy, zbMATH 1512.90001, 2023)\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e1. Introduction.-2. Divide-and-Conquer.- 3. Dynamic Programming and Shortest Path.- 4. Greedy Algorithm and Spanning Tree.- 5. Incremental Method and Maximum Network Flow.- 6. Linear Programming.- 7. Primal-Dual Methods and Minimum Cost Flow.- 8. NP-hard Problems and Approximation Algorithms.- 9. Restriction and Steiner Tree.- 10. Greedy Approximation and Submodular Optimization.- 11. Relaxation and Rounding. 12. Nonsubmodular Optimization.- Bibliography.","brand":"Springer International Publishing AG","offers":[{"title":"Default Title","offer_id":48743070630231,"sku":"9783031116841","price":38.24,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9783031116841.jpg?v=1720063981"},{"product_id":"design-of-heuristic-algorithms-for-hard-optimization-with-python-codes-for-the-travelling-salesman-problem-9783031137136","title":"Design of Heuristic Algorithms for Hard","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eThis open access book demonstrates all the steps required to design heuristic algorithms for difficult optimization. The classic problem of the travelling salesman is used as a common thread to illustrate all the techniques discussed. This problem is ideal for introducing readers to the subject because it is very intuitive and its solutions can be graphically represented. The book features a wealth of illustrations that allow the concepts to be understood at a glance.\u003cbr\u003e \u003cbr\u003e The book approaches the main metaheuristics from a new angle, deconstructing them into a few key concepts presented in separate chapters: construction, improvement, decomposition, randomization and learning methods. Each metaheuristic can then be presented in simplified form as a combination of these concepts. This approach avoids giving the impression that metaheuristics is a non-formal discipline, a kind of cloud sculpture. Moreover, it provides concrete applications of the travelling salesman problem, which illustrate in just a few lines of code how to design a new heuristic and remove all ambiguities left by a general framework. Two chapters reviewing the basics of combinatorial optimization and complexity theory make the book self-contained. As such, even readers with a very limited background in the field will be able to follow all the content.\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003ePart I: Combinatorial Optimization, Complexity Theory and Problem Modelling.- 1. Elements of Graphs and Complexity Theory.- 2. A Short List of Combinatorial Optimization Problems.- 3. Problem Modelling.- Part II: Basic Heuristic Techniques.- 4. Constructive Methods.- 5. Local Search.- 6. Decomposition Methods.- Part III: Popular Metaheuristics.- 7. Randomized Methods.- 8. Construction Learning.- 9. Local Search Learning.- 10. Population Management.- 11. Heuristics Design.- 12. Codes.","brand":"Springer International Publishing AG","offers":[{"title":"Default Title","offer_id":48743072104791,"sku":"9783031137136","price":42.74,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9783031137136.jpg?v=1720063985"},{"product_id":"combinatorial-models-for-scheduling-sports-tournaments-9783031372827","title":"Combinatorial Models for Scheduling Sports","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eThis book introduces solutions for sports scheduling problems in a variety of settings. In particular the book covers timetabling, the traveling tournament problem, carryover minimization, breaks minimization, tournament design, tournament planning, and referee assignment. A rich selection of applications to sports such as football, baseball, basketball, cricket or hockey are employed to illustrate the methods and techniques. In a step-by-step tutorial format the book describes the use of graph theory concepts, local search operators and integer programming in the context of sports scheduling. \u003c\/p\u003e  The methods presented in this book are essential to sports scheduling in all its dimensions, from tournaments that are followed by millions of people across the world, with broadcast rights that amount to hundreds of millions of dollars in some competitions, to amateur leagues that require coordination and logistical efforts due to the large number of tournaments and competitors.\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003eLeagues, tournaments, and schedules.-  Combinatorial structures.- Metaheuristics and local search.- Integer programming approaches.- Case studies. ","brand":"Springer International Publishing AG","offers":[{"title":"Default Title","offer_id":48743084065111,"sku":"9783031372827","price":89.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9783031372827.jpg?v=1720064041"},{"product_id":"calculus-ii-practice-problems-methods-and-solutions-9783031453526","title":"Calculus II: Practice Problems, Methods, and","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eThis study guide is designed for students taking a Calculus II course. The textbook includes examples, questions, and practice problems that will help students to review and sharpen their knowledge of the subject and enhance their performance in the classroom. The material covered in the book includes applications of integration, sequences and series and their applications, polar coordinate systems, and complex numbers. Offering detailed solutions, multiple methods for solving problems, and clear explanations of concepts, this hands-on guide will improve students’ problem-solving skills and foster a solid understanding of calculus, which will benefit them in all of their calculus-based courses\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eChapter 1: Problems: Applications of integration.- Chapter 2: Solutions of Problems: Applications of integration.- Chapter 3: Problems: Sequences and series and their applications.- Chapter 4: Solutions of Problems: Sequences and series and their applications.- Chapter 5: Problems: Polar coordinate system.- Chapter 6: Solutions of Problems: Polar coordinate system.- Chapter 7: Problems: Complex numbers.- Chapter 8: Solutions of Problems: Complex numbers.\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e","brand":"Springer International Publishing AG","offers":[{"title":"Default Title","offer_id":48743086686551,"sku":"9783031453526","price":42.74,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9783031453526.jpg?v=1722247509"},{"product_id":"optimization-and-approximation-9783319648422","title":"Optimization and Approximation","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eThis book provides a basic, initial resource, introducing science and engineering students to the field of optimization. It covers three main areas: mathematical programming, calculus of variations and optimal control, highlighting the ideas and concepts and offering insights into the importance of optimality conditions in each area. It also systematically presents affordable approximation methods. Exercises at various levels have been included to support the learning process.\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e“This book, consisting of eight chapters, provides an introduction to optimization aimed at engineering and science students. ... This book is equally suitable to those without prior knowledge in the field as well as those already familiar with the key concepts as a useful reference. The book concludes with a very useful appendix containing hints or full solutions to the exercises presented throughout the book.” (Efstratios Rappos, zbMATH 1375.90002, 2018)\u003cp\u003e\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e1 Overview. Part I Mathematical Programming. - 2 Linear Programming.- 3 Nonlinear programming.- 4 Numerical approximation.- Part II Variational problems.- 5 Basic theory for variational problems 6 Numerical approximation of variational problems.- Part III Optimal Control.- 7 Basic facts about optimal control . 8 Numerical approximation of basic optimal control problems, and dynamic programming. Part IV Appendix.- 9 Hints and solutions to exercises. \u003cp\u003e\u003c\/p\u003e","brand":"Springer International Publishing AG","offers":[{"title":"Default Title","offer_id":48743100809559,"sku":"9783319648422","price":999.99,"currency_code":"GBP","in_stock":false}]},{"product_id":"an-introduction-to-metaheuristics-for-optimization-9783319930725","title":"An Introduction to Metaheuristics for","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eThe authors stress the relative simplicity, efficiency, flexibility of use, and suitability of various approaches used to solve difficult optimization problems. The authors are experienced, interdisciplinary lecturers and researchers and in their explanations they demonstrate many shared foundational concepts among the key methodologies. \u003c\/p\u003e  \u003cp\u003eThis textbook is a suitable introduction for undergraduate and graduate students, researchers, and professionals in computer science, engineering, and logistics.\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e“I would recommend this book for students in the area of operations research, but also for students and professionals from other fields (like natural sciences or social sciences) who would like not only to apply metaheuristics to solve the problems … but also to understand how they work.” (Marcin Anholcer, zbMATH 1427.90001, 2020)\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eProblems, Algorithms, Computational Complexity.- Search Space.- Tabu Search.- Simulated Annealing.- Ant Colony Optimization (ACO).- Non-PSO Optimization.- Firefly Algorithm, Cuckoo Algorithm, Lévy Flights.- Evolutionary Algorithms: Foundations.- Evolutionary Algorithms: Advanced.- Phase Transition in Optimization Problems.- Performance and Limitations of Metaheuristics.- Statistical Analysis of Research Spaces.\u003c\/p\u003e","brand":"Springer International Publishing AG","offers":[{"title":"Default Title","offer_id":48743109656919,"sku":"9783319930725","price":40.49,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9783319930725.jpg?v=1720064151"},{"product_id":"perturbation-theory-for-linear-operators-9783540586616","title":"Perturbation Theory for Linear Operators","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eFrom the reviews: \"[…] An excellent textbook in the theory of linear operators in Banach and Hilbert spaces. It is a thoroughly worthwhile reference work both for graduate students in functional analysis as well as for researchers in perturbation, spectral, and scattering theory. […] I can recommend it for any mathematician or physicist interested in this field.\" Zentralblatt MATH\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e\"The monograph by T. Kato is an excellent textbook in the theory of linear operators in Banach and Hilbert spaces. It is a thoroughly worthwhile reference work both for graduate students in functional analysis as well as for researchers in perturbation, spectral, and scattering theory.\u003cbr\u003eIn chapters 1, 3, 5 operators in finite-dimensional vector spaces, Banach spaces and Hilbert spaces are introduced. Stability and perturbation theory are studied in finite-dimensional spaces (chapter 2) and in Banach spaces (chapter 4). Sesquilinear forms in Hilbert spaces are considered in detail (chapter 6), analytic and asymptotic perturbation theory is described (chapter 7 and 8). The fundamentals of semigroup theory are given in chapter 9. The supplementary notes appearing in the second edition of the book gave mainly additional information concerning scattering theory described in chapter 10.\u003cbr\u003eThe first edition is now 30 years old. The revised edition is 20 years old. Nevertheless it is a standard textbook for the theory of linear operators. It is user-friendly in the sense that any sought after definitions, theorems or proofs may be easily located. In the last two decades much progress has been made in understanding some of the topics dealt with in the book, for instance in semigroup and scattering theory. However the book has such a high didactical and scientific standard that I can recomment it for any mathematician or physicist interested in this field.\u003cbr\u003e\u003ci\u003eZentralblatt MATH, 836\u003c\/i\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003eOne Operator theory in finite-dimensional vector spaces.- § 1. Vector spaces and normed vector spaces.- 1. Basic notions.- 2. Bases.- 3. Linear manifolds.- 4. Convergence and norms.- 5. Topological notions in a normed space.- 6. Infinite series of vectors.- 7. Vector-valued functions.- § 2. Linear forms and the adjoint space.- 1. Linear forms.- 2. The adjoint space.- 3. The adjoint basis.- 4. The adjoint space of a normed space.- 5. The convexity of balls.- 6. The second adjoint space.- § 3. Linear operators.- 1. Definitions. Matrix representations.- 2. Linear operations on operators.- 3. The algebra of linear operators.- 4. Projections. Nilpotents.- 5. Invariance. Decomposition.- 6. The adjoint operator.- § 4. Analysis with operators.- 1. Convergence and norms for operators.- 2. The norm of Tn.- 3. Examples of norms.- 4. Infinite series of operators.- 5. Operator-valued functions.- 6. Pairs of projections.- § 5. The eigenvalue problem.- 1. Definitions.- 2. The resolvent.- 3. Singularities of the resolvent.- 4. The canonical form of an operator.- 5. The adjoint problem.- 6. Functions of an operator.- 7. Similarity transformations.- § 6. Operators in unitary spaces.- 1. Unitary spaces.- 2. The adjoint space.- 3. Orthonormal families.- 4. Linear operators.- 5. Symmetric forms and symmetric operators.- 6. Unitary, isometric and normal operators.- 7. Projections.- 8. Pairs of projections.- 9. The eigenvalue problem.- 10. The minimax principle.- Two Perturbation theory in a finite-dimensional space.- § 1. Analytic perturbation of eigenvalues.- 1. The problem.- 2. Singularities of the eigenvalues.- 3. Perturbation of the resolvent.- 4. Perturbation of the eigenprojections.- 5. Singularities of the eigenprojections.- 6. Remarks and examples.- 7. The case of T(x) linear in x.- 8. Summary.- § 2. Perturbation series.- 1. The total projection for the ?-group.- 2. The weighted mean of eigenvalues.- 3. The reduction process.- 4. Formulas for higher approximations.- 5. A theorem of Motzkin-Taussky.- 6. The ranks of the coefficients of the perturbation series.- § 3. Convergence radii and error estimates.- 1. Simple estimates.- 2. The method of majorizing series.- 3. Estimates on eigenvectors.- 4. Further error estimates.- 5. The special case of a normal unperturbed operator.- 6. The enumerative method.- § . Similarity transformations of the eigenspaces and eigenvectors.- 1. Eigenvectors.- 2. Transformation functions.- 3. Solution of the differential equation.- 4. The transformation function and the reduction process.- 5. Simultaneous transformation for several projections.- 6. Diagonalization of a holomorphic matrix function.- § 5. Non-analytic perturbations.- 1. Continuity of the eigenvalues and the total projection.- 2. The numbering of the eigenvalues.- 3. Continuity of the eigenspaces and eigenvectors.- 4. Differentiability at a point.- 5. Differentiability in an interval.- 6. Asymptotic expansion of the eigenvalues and eigenvectors.- 7. Operators depending on several parameters.- 8. The eigenvalues as functions of the operator.- § 6. Perturbation of symmetric operators.- 1. Analytic perturbation of symmetric operators.- 2. Orthonormal families of eigenvectors.- 3. Continuity and differentiability.- 4. The eigenvalues as functions of the symmetric operator.- 5. Applications. A theorem of Lidskii.- Three Introduction to the theory of operators in Banach spaces.- § 1. Banach spaces.- 1. Normed spaces.- 2. Banach spaces.- 3. Linear forms.- 4. The adjoint space.- 5. The principle of uniform boundedness.- 6. Weak convergence.- 7. Weak* convergence.- 8. The quotient space.- § 2. Linear operators in Banach spaces.- 1. Linear operators. The domain and range.- 2. Continuity and boundedness.- 3. Ordinary differential operators of second order.- § 3. Bounded operators.- 1. The space of bounded operators.- 2. The operator algebra ?(X).- 3. The adjoint operator.- 4. Projections.- § 4. Compact operators.- 1. Definition.- 2. The space of compact operators.- 3. Degenerate operators. The trace and determinant.- § 5. Closed operators.- 1. Remarks on unbounded operators.- 2. Closed operators.- 3. Closable operators.- 4. The closed graph theorem.- 5. The adjoint operator.- 6. Commutativity and decomposition.- § 6. Resolvents and spectra.- 1. Definitions.- 2. The spectra of bounded operators.- 3. The point at infinity.- 4. Separation of the spectrum.- 5. Isolated eigenvalues.- 6. The resolvent of the adjoint.- 7. The spectra of compact operators.- 8. Operators with compact resolvent.- Four Stability theorems.- §1. Stability of closedness and bounded invertibility.- 1. Stability of closedness under relatively bounded perturbation.- 2. Examples of relative boundedness.- 3. Relative compactness and a stability theorem.- 4. Stability of bounded in vertibility.- § 2. Generalized convergence of closed operators.- 1. The gap between subspaces.- 2. The gap and the dimension.- 3. Duality.- 4. The gap between closed operators.- 5. Further results on the stability of bounded in vertibility.- 6. Generalized convergence.- § 3. Perturbation of the spectrum.- 1. Upper semicontinuity of the spectrum.- 2. Lower semi-discontinuity of the spectrum.- 3. Continuity and analyticity of the resolvent.- 4. Semicontinuity of separated parts of the spectrum.- 5. Continuity of a finite system of eigenvalues.- 6. Change of the spectrum under relatively bounded perturbation.- 7. Simultaneous consideration of an infinite number of eigenvalues.- 8. An application to Banach algebras. Wiener’s theorem.- § 4. Pairs of closed linear manifolds.- 1. Definitions.- 2. Duality.- 3. Regular pairs of closed linear manifolds.- 4. The approximate nullity and deficiency.- 5. Stability theorems.- § 5. Stability theorems for semi-Fredholm operators.- 1. The nullity, deficiency and index of an operator.- 2. The general stability theorem.- 3. Other stability theorems.- 4. Isolated eigenvalues.- 5. Another form of the stability theorem.- 6. Structure of the spectrum of a closed operator.- § 6. Degenerate perturbations.- 1. The Weinstein-Aronszajn determinants.- 2. The W-A formulas.- 3. Proof of the W-A formulas.- 4. Conditions excluding the singular case.- Five Operators in Hilbert spaces.- § 1. Hilbert space.- 1. Basic notions.- 2. Complete orthonormal families.- § 2. Bounded operators in Hilbert spaces.- 1. Bounded operators and their adjoints.- 2. Unitary and isometric operators.- 3. Compact operators.- 4. The Schmidt class.- 5. Perturbation of orthonormal families.- § 3. Unbounded operators in Hilbert spaces.- 1. General remarks.- 2. The numerical range.- 3. Symmetric operators.- 4. The spectra of symmetric operators.- 5. The resolvents and spectra of selfadjoint operators.- 6. Second-order ordinary differential operators.- 7. The operators T*T.- 8. Normal operators.- 9. Reduction of symmetric operators.- 10. Semibounded and accretive operators.- 11. The square root of an m-accretive operator.- § 4. Perturbation of self adjoint operators.- 1. Stability of selfadjointness.- 2. The case of relative bound 1.- 3. Perturbation of the spectrum.- 4. Semibounded operators.- 5. Completeness of the eigenprojections of slightly non-selfadjoint operators.- § 5. The Schrödinger and Dirac operators.- 1. Partial differential operators.- 2. The Laplacian in the whole space.- 3. The Schrödinger operator with a static potential.- 4. The Dirac operator.- Six Sesquilinear forms in Hilbert spaces and associated operators.- § 1. Sesquilinear and quadratic forms.- 1. Definitions.- 2. Semiboundedness.- 3. Closed forms.- 4. Closable forms.- 5. Forms constructed from sectorial operators.- 6. Sums of forms.- 7. Relative boundedness for forms and operators.- § 2. The representation theorems.- 1. The first representation theorem.- 2. Proof of the first representation theorem.- 3. The Friedrichs extension.- 4. Other examples for the representation theorem.- 5. Supplementary remarks.- 6. The second representation theorem.- 7. The polar decomposition of a closed operator.- § 3. Perturbation of sesquilinear forms and the associated operators.- 1. The real part of an m-sectorial operator.- 2. Perturbation of an m-sectorial operator and its resolvent.- 3. Symmetric unperturbed operators.- 4. Pseudo-Friedrichs extensions.- § 4. Quadratic forms and the Schrödinger operators.- 1. Ordinary differential operators.- 2. The Dirichlet form and the Laplace operator.- 3. The Schrödinger operators in R3.- 4. Bounded regions.- § 5. The spectral theorem and perturbation of spectral families.- 1. Spectral families.- 2. The selfadjoint operator associated with a spectral family.- 3. The spectral theorem.- 4. Stability theorems for the spectral family.- Seven Analytic perturbation theory.- § 1. Analytic families of operators.- 1. Analyticity of vector- and operator-valued functions.- 2. Analyticity of a family of unbounded operators.- 3. Separation of the spectrum and finite systems of eigenvalues.- 4. Remarks on infinite systems of eigenvalues.- 5. Perturbation series.- 6. A holomorphic family related to a degenerate perturbation.- § 2. Holomorphic families of type (A).- 1. Definition.- 2. A criterion for type (A).- 3. Remarks on holomorphic families of type (A).- 4. Convergence radii and error estimates.- 5. Normal unperturbed operators.- § 3. Selfadjoint holomorphic families.- 1. General remarks.- 2. Continuation of the eigenvalues.- 3. The Mathieu, Schrödinger, and Dirac equations.- 4. Growth rate of the eigenvalues.- 5. Total eigenvalues considered simultaneously.- § 4. Holomorphic families of type (B).- 1. Bounded-holomorphic families of sesquilinear forms.- 2. Holomorphic families of forms of type (a) and holomorphic families of operators of type (B).- 3. A criterion for type (B).- 4. Holomorphic families of type (B0).- 5. The relationship between holomorphic families of types (A) and (B).- 6. Perturbation series for eigenvalues and eigenprojections.- 7. Growth rate of eigenvalues and the total system of eigenvalues.- 8. Application to differential operators.- 9. The two-electron problem.- § 5. Further problems of analytic perturbation theory.- 1. Holomorphic families of type (C).- 2. Analytic perturbation of the spectral family.- 3. Analyticity of |H(x)| and |H(x)|?.- § 6. Eigenvalue problems in the generalized form.- 1. General considerations.- 2. Perturbation theory.- 3. Holomorphic families of type (A).- 4. Holomorphic families of type (B).- 5. Boundary perturbation.- Eight Asymptotic perturbation theory.- § 1. Strong convergence in the generalized sense.- 1. Strong convergence of the resolvent.- 2. Generalized strong convergence and spectra.- 3. Perturbation of eigenvalues and eigenvectors.- 4. Stable eigenvalues.- § 2. Asymptotic expansions.- 1. Asymptotic expansion of the resolvent.- 2. Remarks on asymptotic expansions.- 3. Asymptotic expansions of isolated eigenvalues and eigenvectors.- 4. Further asymptotic expansions.- § 3. Generalized strong convergence of sectorial operators.- 1. Convergence of a sequence of bounded forms.- 2. Convergence of sectorial forms “from above”.- 3. Nonincreasing sequences of symmetric forms.- 4. Convergence from below.- 5. Spectra of converging operators.- § 4. Asymptotic expansions for sectorial operators.- 1. The problem. The zeroth approximation for the resolvent.- 2. The 1\/2-order approximation for the resolvent.- 3. The first and higher order approximations for the resolvent.- 4. Asymptotic expansions for eigenvalues and eigenvectors.- § 5. Spectral concentration.- 1. Unstable eigenvalues.- 2. Spectral concentration.- 3. Pseudo-eigenvectors and spectral concentration.- 4. Asymptotic expansions.- Nine Perturbation theory for semigroups of operators.- § 1. One-parameter semigroups and groups of operators.- 1. The problem.- 2. Definition of the exponential function.- 3. Properties of the exponential function.- 4. Bounded and quasi-bounded semigroups.- 5. Solution of the inhomogeneous differential equation.- 6. Holomorphic semigroups.- 7. The inhomogeneous differential equation for a holomorphic semigroup.- 8. Applications to the heat and Schrödinger equations.- § 2. Perturbation of semigroups.- 1. Analytic perturbation of quasi-bounded semigroups.- 2. Analytic perturbation of holomorphic semigroups.- 3. Perturbation of contraction semigroups.- 4. Convergence of quasi-bounded semigroups in a restricted sense.- 5. Strong convergence of quasi-bounded semigroups.- 6. Asymptotic perturbation of semigroups.- § 3. Approximation by discrete semigroups.- 1. Discrete semigroups.- 2. Approximation of a continuous semigroup by discrete semigroups.- 3. Approximation theorems.- 4. Variation of the space.- Ten Perturbation of continuous spectra and unitary equivalence.- §1. The continuous spectrum of a selfadjoint operator.- 1. The point and continuous spectra.- 2. The absolutely continuous and singular spectra.- 3. The trace class.- 4. The trace and determinant.- § 2. Perturbation of continuous spectra.- 1. A theorem of Weyl-von Neumann.- 2. A generalization.- § 3. Wave operators and the stability of absolutely continuous spectra.- 1. Introduction.- 2. Generalized wave operators.- 3. A sufficient condition for the existence of the wave operator.- 4. An application to potential scattering.- § 4. Existence and completeness of wave operators.- 1. Perturbations of rank one (special case).- 2. Perturbations of rank one (general case).- 3. Perturbations of the trace class.- 4. Wave operators for functions of operators.- 5. Strengthening of the existence theorems.- 6. Dependence of W± (H2, H1) on H1 and H2.- § 5. A stationary method.- 1. Introduction.- 2. The ? operations.- 3. Equivalence with the time-dependent theory.- 4. The ? operations on degenerate operators.- 5. Solution of the integral equation for rank A = 1.- 6. Solution of the integral equation for a degenerate A.- 7. Application to differential operators.- Supplementary Notes.- Supplementary Bibliography.- Notation index.- Author index.","brand":"Springer-Verlag Berlin and Heidelberg GmbH \u0026 Co. KG","offers":[{"title":"Default Title","offer_id":48743131251031,"sku":"9783540586616","price":49.99,"currency_code":"GBP","in_stock":true}]},{"product_id":"optimal-transport-old-and-new-9783540710493","title":"Optimal Transport: Old and New","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eAt the close of the 1980s, the independent contributions of Yann Brenier, Mike Cullen and John Mather launched a revolution in the venerable field of optimal transport founded by G. Monge in the 18th century, which has made breathtaking forays into various other domains of mathematics ever since. The author presents a broad overview of this area, supplying complete and self-contained proofs of all the fundamental results of the theory of optimal transport at the appropriate level of generality. Thus, the book encompasses the broad spectrum ranging from basic theory to the most recent research results. \u003c\/p\u003e \u003cp\u003e\u003c\/p\u003e \u003cp\u003ePhD students or researchers can read the entire book without any prior knowledge of the field. A comprehensive bibliography with notes that extensively discuss the existing literature underlines the book’s value as a most welcome reference text on this subject. \u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eFrom the reviews:\u003c\/p\u003e\u003cp\u003e\"The book is aimed to old and new problems of optimal transport. … This meticulous work is based on very large bibliography … that is converted into a very valuable monograph that presents many statements and theorems written specifically for this approach, complete and self-contained proofs of the most important results, and extensive bibliographical notes.\" (Mihail Voicu, Zentralblatt MATH, Vol. 1156, 2009)\u003c\/p\u003e\u003cp\u003e“This book wins the challenge to give a new and broad perspective on the multifacet topic of the optimal mass transport. … Besides extensive and accurate references therein the reader will find comments on related questions barely touched upon in the main text as well as lively presentations on how ideas and results have developed. This book should prove useful both to the expert and to the beginner looking for a reference text on the subject.” (Dario Cordero Erausquin, Mathematical Reviews, Issue 2010 f)\u003c\/p\u003e\u003cp\u003e“The book is an in-depth, modern, clear exposition of the advanced theory of optimal transport, and it tries to put together in a unified way almost all the recent developments of the theory. … the book is extremely well written and very pleasant to read. … I strongly recommend this excellent book to every researcher or graduate student in the field of optimal transport. … of interest to many mathematicians in different areas, who are simply interested in having an overview of the subject.” (Alessio Figalli, Bulletin of the American Mathematical Society, Vol. 47 (4), February, 2010)\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003eCouplings and changes of variables.- Three examples of coupling techniques.- The founding fathers of optimal transport.- Qualitative description of optimal transport.- Basic properties.- Cyclical monotonicity and Kantorovich duality.- The Wasserstein distances.- Displacement interpolation.- The Monge—Mather shortening principle.- Solution of the Monge problem I: global approach.- Solution of the Monge problem II: Local approach.- The Jacobian equation.- Smoothness.- Qualitative picture.- Optimal transport and Riemannian geometry.- Ricci curvature.- Otto calculus.- Displacement convexity I.- Displacement convexity II.- Volume control.- Density control and local regularity.- Infinitesimal displacement convexity.- Isoperimetric-type inequalities.- Concentration inequalities.- Gradient flows I.- Gradient flows II: Qualitative properties.- Gradient flows III: Functional inequalities.- Synthetic treatment of Ricci curvature.- Analytic and synthetic points of view.- Convergence of metric-measure spaces.- Stability of optimal transport.- Weak Ricci curvature bounds I: Definition and Stability.- Weak Ricci curvature bounds II: Geometric and analytic properties.","brand":"Springer-Verlag Berlin and Heidelberg GmbH \u0026 Co. KG","offers":[{"title":"Default Title","offer_id":48743131283799,"sku":"9783540710493","price":113.99,"currency_code":"GBP","in_stock":true}]},{"product_id":"convex-analysis-and-minimization-algorithms-ii-advanced-theory-and-bundle-methods-9783642081620","title":"Convex Analysis and Minimization Algorithms II:","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eFrom the reviews: \"The account is quite detailed and is written in a manner that will appeal to analysts and numerical practitioners alike...they contain everything from rigorous proofs to tables of numerical calculations.... one of the strong features of these books...that they are designed not for the expert, but for those who whish to learn the subject matter starting from little or no background...there are numerous examples, and counter-examples, to back up the theory...To my knowledge, no other authors have given such a clear geometric account of convex analysis.\" \"This innovative text is well written, copiously illustrated, and accessible to a wide audience\"\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003eFrom the reviews: \"The account is quite detailed and is written in a manner that will appeal to analysts and numerical practitioners alike...they contain everything from rigorous proofs to tables of numerical calculations.... one of the strong features of these books. . . [is] that they are designed not for the expert, but for those who wish to learn the subject matter starting from little or no background...there are numerous examples, and counter-examples, to back up the theory...To my knowledge, no other authors have given such a clear geometric account of convex analysis.\" \"This innovative text is well written, copiously illustrated, and accessible to a wide audience\"\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003eIX. Inner Construction of the Subdifferential.- X. Conjugacy in Convex Analysis.- XI. Approximate Subdifferentials of Convex Functions.- XII. Abstract Duality for Practitioners.- XIII. Methods of ?-Descent.- XIV. Dynamic Construction of Approximate Subdifferentials: Dual Form of Bundle Methods.- XV. Acceleration of the Cutting-Plane Algorithm: Primal Forms of Bundle Methods.- Bibliographical Comments.- References.","brand":"Springer-Verlag Berlin and Heidelberg GmbH \u0026 Co. KG","offers":[{"title":"Default Title","offer_id":48743133577559,"sku":"9783642081620","price":104.49,"currency_code":"GBP","in_stock":true}]},{"product_id":"design-sensitivity-analysis-and-optimization-of-electromagnetic-systems-9789811302299","title":"Design Sensitivity Analysis and Optimization of","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e \u003c\/p\u003e\u003cp\u003eThis book presents a comprehensive introduction to design sensitivity analysis theory as applied to electromagnetic systems. It treats the subject in a unified manner, providing numerical methods and design examples. The specific focus is on continuum design sensitivity analysis, which offers significant advantages over discrete design sensitivity methods. Continuum design sensitivity formulas are derived from the material derivative in continuum mechanics and the variational form of the governing equation. Continuum sensitivity analysis is applied to Maxwell equations of electrostatic, magnetostatic and eddy-current systems, and then the sensitivity formulas for each system are derived in a closed form; an integration along the design interface.\u003c\/p\u003e\u003cp\u003eThe book also introduces the recent breakthrough of the topology optimization method, which is accomplished by coupling the level set method and continuum design sensitivity. This topology optimization method enhances the possibility of the global minimum with minimised computational time, and in addition the evolving shapes during the iterative design process are easily captured in the level set equation. Moreover, since the optimization algorithm is transformed into a well-known transient analysis algorithm for differential equations, its numerical implementation becomes very simple and convenient.\u003c\/p\u003e\u003cp\u003e Despite the complex derivation processes and mathematical expressions, the obtained sensitivity formulas are very straightforward for numerical implementation. This book provides detailed explanation of the background theory and the derivation process, which will help readers understand the design method and will set the foundation for advanced research in the future.\u003cbr\u003e\u003c\/p\u003e\u003cp\u003e \u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e1. Introduction. 1.1 Optimal Design Process. 1.2 Design Steps of Electromagnetic System. 1.3 Design Variables. 1.4 Equations and Characteristics of Electromagnetic Systems. 1.4.1 Maxwell’s Equations and Governing Equations. 1.4.2 Characteristics of Electromagnetic Systems. 1.5 Design Sensitivity Analysis. 1.5.1 Finite Difference Method. 1.5.2 Discrete Method. 1.5.3 Continuum Method.\u003c\/p\u003e\u003cp\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003e2. Variational Formulation of Electromagnetic Systems. 2.1 Variational Formulation of Electrostatic System. 2.1.1 Differential State Equation. 2.1.2 Variational State Equation. 2.2 Variational Formulation of Magnetostatic System. 2.2.1 Differential State Equation. 2.2.2 Variational State Equation. 2.3 Variational Formulation of Eddy Current System. 2.3.1 Differential State Equation. 2.3.2 Variational State Equation. 2.4 Variational Formulation of DC Conductor System. 2.4.1 Differential State Equation. 2.4.2 Variational State Equation.\u003c\/p\u003e\u003cp\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003e3. Continuum Shape Design Sensitivity of Electrostatic System. 3.1 Material Derivative and Formula. 3.1.1 Material Derivative. 3.1.2 Material Derivative Formula. 3.2 Shape Sensitivity of Outer Boundary. 3.2.1 Problem Definition and Objective Function. 3.2.2 Lagrange Multiplier Method for Sensitivity Derivation. 3.2.3 Adjoint Variable Method for Sensitivity Analysis. 3.2.4 Boundary Expression of Shape Sensitivity. 3.2.5 Analytical Example. 3.2.6 Numerical Examples. 3.3 Shape Sensitivity of Outer Boundary for System Energy. 3.3.1 Problem Definition. 3.3.2 Lagrange Multiplier Method for Energy Sensitivity. 3.3.3 Adjoint Variable Method for Sensitivity Analysis. 3.3.4 Boundary Expression of Shape Sensitivity. 3.3.5 Source Condition and Capacitance Sensitivity. 3.3.6 Analytical Example. 3.3.7 Numerical Examples. 3.4 Shape Sensitivity of Interface. 3.4.1 Problem Definition and Objective Function. 3.4.2 Lagrange Multiplier Method for Sensitivity Derivation. 3.4.3 Adjoint Variable Method for Sensitivity Analysis. 3.4.4 Boundary Expression of Shape Sensitivity. 3.4.5 Analytical Example. 3.4.6 Numerical Example. 3.5 Shape Sensitivity of Interface for System Energy. 3.5.1 Problem Definition. 3.5.2 Lagrange Multiplier Method for Energy Sensitivity. 3.5.3 Adjoint Variable Method for Sensitivity Analysis. 3.5.4 Boundary Expression of Shape Sensitivity. 3.5.5 Source Condition and Capacitance Sensitivity. 3.5.6 Analytical Example. 3.5.7 Numerical Examples.\u003c\/p\u003e  \u003cp\u003e \u003c\/p\u003e  \u003cp\u003e4. Continuum Shape Design Sensitivity of Magnetostatic System. 4.1 Interface Shape Sensitivity. 4.1.1 Problem Definition and Objective Function. 4.1.2 Lagrange Multiplier Method for Sensitivity Derivation. 4.1.3 Adjoint Variable Method for Sensitivity Analysis. 4.1.4 Boundary Expression of Shape Sensitivity. 4.1.5 Interface Problems. 4.1.6 Analytical Example. 4.1.7 Numerical Examples. 4.2 Interface Shape Sensitivity for System Energy. 4.2.1 Problem Definition. 4.2.2 Lagrange Multiplier Method for Energy Sensitivity. 4.2.3 Adjoint Variable Method for Sensitivity Analysis. 4.2.4 Boundary Expression of Shape Sensitivity. 4.2.5 Interface Problems. 4.2.6 Source Condition and Inductance Sensitivity. 4.2.7 Analytical Examples. 4.2.8 Numerical Examples.\u003c\/p\u003e  \u003cp\u003e \u003c\/p\u003e  \u003cp\u003e5. Continuum Shape Design Sensitivity of Eddy Current System. 5.1 Interface Shape Sensitivity. 5.1.1 Problem Definition and Objective Function. 5.1.2 Lagrange Multiplier Method for Sensitivity Derivation. 5.1.3 Adjoint Variable Method for Sensitivity Analysis. 5.1.4 Boundary Expression of Shape Sensitivity. 5.1.5 Interface Problems. 5.1.6 Numerical Examples. 5.2 Interface Shape Sensitivity for System Power. 5.2.1 Problem Definition. 5.2.2 Adjoint Variable Method for Power Sensitivity. 5.2.3 Boundary Expression of Shape Sensitivity. 5.2.4 Sensitivities of Resistance and Inductance. 5.2.5 Numerical Examples.\u003c\/p\u003e  \u003cp\u003e \u003c\/p\u003e  \u003cp\u003e6. Continuum Shape Design Sensitivity of DC Conductor System. 6.1 Shape Sensitivity of Outer Boundary. 6.1.1 Problem Definition and Objective Function. 6.1.2 Lagrange Multiplier Method for Sensitivity Derivation. 6.1.3 Adjoint Variable Method for Sensitivity Analysis. 6.1.4 Boundary Expression of Shape Sensitivity. 6.2 Shape Sensitivity of Outer Boundary for Joule loss power. 6.2.1 Problem Definition. 6.2.2 Boundary Expression of Shape Sensitivity. 6.2.3 Resistance Sensitivity. 6.2.4 Analytical Examples. 6.2.5 Numerical Examples.\u003c\/p\u003e  \u003cp\u003e \u003c\/p\u003e  7. Level Set Method and Continuum Sensitivity. 7.1 Level Set Method. 7.2 Coupling of Continuum Sensitivity and Level Set Method. 7.3 Numerical Considerations.\u003cp\u003e\u003c\/p\u003e  \u003cp\u003e \u003c\/p\u003e  8. Hole and Dot Sensitivity for Topology Optimization. 8.1 Hole Sensitivity. 8.1.1 Hole Sensitivity in Dielectric Material. 8.1.2 Hole Sensitivity in Magnetic Material. 8.1.3 Numerical Examples. 8.2 Dot Sensitivity. 8.2.1 Dot Sensitivity in Dielectric Material. 8.2.2 Dot Sensitivity in Magnetic Material. 8.2.3 Numerical Examples. \u003cp\u003e\u003c\/p\u003e\u003cp\u003e \u003c\/p\u003e  \u003cp\u003eAppendix A. More Examples of Electrostatic System. A.1 Outer Boundary Design. A.2 Outer Boundary Design for System Energy. A.3 Interface Design. A.4 Interface Design for System Energy. \u003c\/p\u003e\u003cp\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003eAppendix B. More Examples of Magnetostatic System. B.1 Interface Design. B.2 Interface Design for System Energy.\u003c\/p\u003e\u003cp\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003eAppendix C. More Examples of Eddy Current System. C.1 Interface Design for System Power. \u003c\/p\u003e\u003cp\u003e\u003cbr\u003e\u003c\/p\u003e  \u003cp\u003eAppendix D. More Examples of DC Conductor System. D.1 Outer Boundary Design for Joule Loss Power.\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e","brand":"Springer Verlag, Singapore","offers":[{"title":"Default Title","offer_id":48743286604119,"sku":"9789811302299","price":999.99,"currency_code":"GBP","in_stock":false}]},{"product_id":"optimal-design-of-experiments-9780470744611","title":"Optimal Design of Experiments","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eThis is an engaging and informative book on the modern practice of experimental design. The authors'' writing style is entertaining, the consulting dialogs are extremely enjoyable, and the technical material is presented brilliantly but not overwhelmingly. The book is a joy to read. Everyone who practices or teaches DOE should read this book. -\u003c\/i\u003e \u003cb\u003eDouglas C. Montgomery\u003c\/b\u003e, \u003cb\u003eRegents Professor, Department of Industrial Engineering, Arizona State University\u003c\/b\u003e  \u003cp\u003e\u003ci\u003eIt''s been said: ''Design for the experiment, don''t experiment for the design.'' This book ably demonstrates this notion by showing how tailor-made, optimal designs can be effectively employed to meet a client''s actual needs. It should be required reading for anyone interested in using the design of experiments in industrial settings.\u003cbr\u003e \u003c\/i\u003e\u003cb\u003eChristopher J. Nachtsheim\u003c\/b\u003e, \u003cb\u003eFrank A Donaldson Chair in Operations Management, Carlson School of Management, University of Minnesota\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThis book demonstra\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cb\u003ePreface.\u003c\/b\u003e  \u003c\/p\u003e\u003cp\u003e\u003cb\u003eAcknowledgments.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 A simple comparative experiment.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 Key concepts.\u003c\/p\u003e \u003cp\u003e1.2 The setup of a comparative experiment.\u003c\/p\u003e \u003cp\u003e1.3 Summary.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 An optimal screening experiment.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 Key concepts.\u003c\/p\u003e \u003cp\u003e2.2 Case: an extraction experiment.\u003c\/p\u003e \u003cp\u003e2.2.1 Problem and design.\u003c\/p\u003e \u003cp\u003e2.2.2 Data analysis.\u003c\/p\u003e \u003cp\u003e2.3 Peek into the black box.\u003c\/p\u003e \u003cp\u003e2.3.1 Main-effects models.\u003c\/p\u003e \u003cp\u003e2.3.2 Models with two-factor interaction effects.\u003c\/p\u003e \u003cp\u003e2.3.3 Factor scaling.\u003c\/p\u003e \u003cp\u003e2.3.4 Ordinary least squares estimation.\u003c\/p\u003e \u003cp\u003e2.3.5 Significance tests and statistical power calculations.\u003c\/p\u003e \u003cp\u003e2.3.6 Variance inflation.\u003c\/p\u003e \u003cp\u003e2.3.7 Aliasing.\u003c\/p\u003e \u003cp\u003e2.3.8 Optimal design.\u003c\/p\u003e \u003cp\u003e2.3.9 Generating optimal experimental designs.\u003c\/p\u003e \u003cp\u003e2.3.10 The extraction experiment revisited.\u003c\/p\u003e \u003cp\u003e2.3.11 Principles of successful screening: sparsity, hierarchy, and heredity.\u003c\/p\u003e \u003cp\u003e2.4 Background reading.\u003c\/p\u003e \u003cp\u003e2.4.1 Screening.\u003c\/p\u003e \u003cp\u003e2.4.2 Algorithms for finding optimal designs.\u003c\/p\u003e \u003cp\u003e2.5 Summary.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Adding runs to a screening experiment.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Key concepts.\u003c\/p\u003e \u003cp\u003e3.2 Case: an augmented extraction experiment.\u003c\/p\u003e \u003cp\u003e3.2.1 Problem and design.\u003c\/p\u003e \u003cp\u003e3.2.2 Data analysis.\u003c\/p\u003e \u003cp\u003e3.3 Peek into the black box.\u003c\/p\u003e \u003cp\u003e3.3.1 Optimal selection of a follow-up design.\u003c\/p\u003e \u003cp\u003e3.3.2 Design construction algorithm.\u003c\/p\u003e \u003cp\u003e3.3.3 Foldover designs.\u003c\/p\u003e \u003cp\u003e3.4 Background reading.\u003c\/p\u003e \u003cp\u003e3.5 Summary.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 A response surface design with a categorical factor.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Key concepts.\u003c\/p\u003e \u003cp\u003e4.2 Case: a robust and optimal process experiment.\u003c\/p\u003e \u003cp\u003e4.2.1 Problem and design.\u003c\/p\u003e \u003cp\u003e4.2.2 Data analysis.\u003c\/p\u003e \u003cp\u003e4.3 Peek into the black box.\u003c\/p\u003e \u003cp\u003e4.3.1 Quadratic effects.\u003c\/p\u003e \u003cp\u003e4.3.2 Dummy variables for multilevel categorical factors.\u003c\/p\u003e \u003cp\u003e4.3.3 Computing D-efficiencies.\u003c\/p\u003e \u003cp\u003e4.3.4 Constructing Fraction of Design Space plots.\u003c\/p\u003e \u003cp\u003e4.3.5 Calculating the average relative variance of prediction.\u003c\/p\u003e \u003cp\u003e4.3.6 Computing I-efficiencies.\u003c\/p\u003e \u003cp\u003e4.3.7 Ensuring the validity of inference based on ordinary least squares.\u003c\/p\u003e \u003cp\u003e4.3.8 Design regions.\u003c\/p\u003e \u003cp\u003e4.4 Background reading.\u003c\/p\u003e \u003cp\u003e4.5 Summary.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 A response surface design in an irregularly shaped design region.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Key concepts.\u003c\/p\u003e \u003cp\u003e5.2 Case: the yield maximization experiment.\u003c\/p\u003e \u003cp\u003e5.2.1 Problem and design.\u003c\/p\u003e \u003cp\u003e5.2.2 Data analysis.\u003c\/p\u003e \u003cp\u003e5.3 Peek into the black box.\u003c\/p\u003e \u003cp\u003e5.3.1 Cubic factor effects.\u003c\/p\u003e \u003cp\u003e5.3.2 Lack-of-fit test.\u003c\/p\u003e \u003cp\u003e5.3.3 Incorporating factor constraints in the design construction algorithm.\u003c\/p\u003e \u003cp\u003e5.4 Background reading.\u003c\/p\u003e \u003cp\u003e5.5 Summary.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 A \"mixture\" experiment with process variables.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Key concepts.\u003c\/p\u003e \u003cp\u003e6.2 Case: the rolling mill experiment.\u003c\/p\u003e \u003cp\u003e6.2.1 Problem and design.\u003c\/p\u003e \u003cp\u003e6.2.2 Data analysis.\u003c\/p\u003e \u003cp\u003e6.3 Peek into the black box.\u003c\/p\u003e \u003cp\u003e6.3.1 The mixture constraint.\u003c\/p\u003e \u003cp\u003e6.3.2 The effect of the mixture constraint on the model.\u003c\/p\u003e \u003cp\u003e6.3.3 Commonly used models for data from mixture experiments.\u003c\/p\u003e \u003cp\u003e6.3.4 Optimal designs for mixture experiments.\u003c\/p\u003e \u003cp\u003e6.3.5 Design construction algorithms for mixture experiments.\u003c\/p\u003e \u003cp\u003e6.4 Background reading.\u003c\/p\u003e \u003cp\u003e6.5 Summary.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 A response surface design in blocks.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Key concepts.\u003c\/p\u003e \u003cp\u003e7.2 Case: the pastry dough experiment.\u003c\/p\u003e \u003cp\u003e7.2.1 Problem and design.\u003c\/p\u003e \u003cp\u003e7.2.2 Data analysis.\u003c\/p\u003e \u003cp\u003e7.3 Peek into the black box.\u003c\/p\u003e \u003cp\u003e7.3.1 Model.\u003c\/p\u003e \u003cp\u003e7.3.2 Generalized least squares estimation.\u003c\/p\u003e \u003cp\u003e7.3.3 Estimation of variance components.\u003c\/p\u003e \u003cp\u003e7.3.4 Significance tests.\u003c\/p\u003e \u003cp\u003e7.3.5 Optimal design of blocked experiments.\u003c\/p\u003e \u003cp\u003e7.3.6 Orthogonal blocking.\u003c\/p\u003e \u003cp\u003e7.3.7 Optimal versus orthogonal blocking.\u003c\/p\u003e \u003cp\u003e7.4 Background reading.\u003c\/p\u003e \u003cp\u003e7.5 Summary.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 A screening experiment in blocks.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 Key concepts.\u003c\/p\u003e \u003cp\u003e8.2 Case: the stability improvement experiment.\u003c\/p\u003e \u003cp\u003e8.2.1 Problem and design.\u003c\/p\u003e \u003cp\u003e8.2.2 Afterthoughts about the design problem.\u003c\/p\u003e \u003cp\u003e8.2.3 Data analysis.\u003c\/p\u003e \u003cp\u003e8.3 Peek into the black box.\u003c\/p\u003e \u003cp\u003e8.3.1 Models involving block effects.\u003c\/p\u003e \u003cp\u003e8.3.2 Fixed block effects.\u003c\/p\u003e \u003cp\u003e8.4 Background reading.\u003c\/p\u003e \u003cp\u003e8.5 Summary.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Experimental design in the presence of covariates.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e9.1 Key concepts.\u003c\/p\u003e \u003cp\u003e9.2 Case: the polypropylene experiment.\u003c\/p\u003e \u003cp\u003e9.2.1 Problem and design.\u003c\/p\u003e \u003cp\u003e9.2.2 Data analysis.\u003c\/p\u003e \u003cp\u003e9.3 Peek into the black box.\u003c\/p\u003e \u003cp\u003e9.3.1 Covariates or concomitant variables.\u003c\/p\u003e \u003cp\u003e9.3.2 Models and design criteria in the presence of covariates.\u003c\/p\u003e \u003cp\u003e9.3.3 Designs robust to time trends.\u003c\/p\u003e \u003cp\u003e9.3.4 Design construction algorithms.\u003c\/p\u003e \u003cp\u003e9.3.5 To randomize or not to randomize.\u003c\/p\u003e \u003cp\u003e9.3.6 Final thoughts.\u003c\/p\u003e \u003cp\u003e9.4 Background reading.\u003c\/p\u003e \u003cp\u003e9.5 Summary.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 A split-plot design.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e10.1 Key concepts.\u003c\/p\u003e \u003cp\u003e10.2 Case: the wind tunnel experiment.\u003c\/p\u003e \u003cp\u003e10.2.1 Problem and design.\u003c\/p\u003e \u003cp\u003e10.2.2 Data analysis.\u003c\/p\u003e \u003cp\u003e10.3 Peek into the black box.\u003c\/p\u003e \u003cp\u003e10.3.1 Split-plot terminology.\u003c\/p\u003e \u003cp\u003e10.3.2 Model.\u003c\/p\u003e \u003cp\u003e10.3.3 Inference from a split-plot design.\u003c\/p\u003e \u003cp\u003e10.3.4 Disguises of a split-plot design.\u003c\/p\u003e \u003cp\u003e10.3.5 Required number of whole plots and runs.\u003c\/p\u003e \u003cp\u003e10.3.6 Optimal design of split-plot experiments.\u003c\/p\u003e \u003cp\u003e10.3.7 A design construction algorithm for optimal split-plot designs.\u003c\/p\u003e \u003cp\u003e10.3.8 Difficulties when analyzing data from split-plot experiments.\u003c\/p\u003e \u003cp\u003e10.4 Background reading.\u003c\/p\u003e \u003cp\u003e10.5 Summary.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 A two-way split-plot design.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e11.1 Key concepts.\u003c\/p\u003e \u003cp\u003e11.2 Case: the battery cell experiment.\u003c\/p\u003e \u003cp\u003e11.2.1 Problem and design.\u003c\/p\u003e \u003cp\u003e11.2.2 Data analysis.\u003c\/p\u003e \u003cp\u003e11.3 Peek into the black box.\u003c\/p\u003e \u003cp\u003e11.3.1 The two-way split-plot model.\u003c\/p\u003e \u003cp\u003e11.3.2 Generalized least squares estimation.\u003c\/p\u003e \u003cp\u003e11.3.3 Optimal design of two-way split-plot experiments.\u003c\/p\u003e \u003cp\u003e11.3.4 A design construction algorithm for D-optimal two-way split-plot designs.\u003c\/p\u003e \u003cp\u003e11.3.5 Extensions and related designs.\u003c\/p\u003e \u003cp\u003e11.4 Background reading.\u003c\/p\u003e \u003cp\u003e11.5 Summary.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eBibliography.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eIndex.\u003c\/b\u003e\u003c\/p\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":48864638796119,"sku":"9780470744611","price":63.6,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780470744611.jpg?v=1722272842"},{"product_id":"datadriven-seo-with-python-9781484291740","title":"DataDriven SEO with Python","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cdiv\u003e\n\u003cp\u003eSolve SEO problems using data science. This hands-on book is packed with Python code and data science techniques to help you generate data-driven recommendations and automate the SEO workload. \u003c\/p\u003eThis book is a practical, modern introduction to data science in the SEO context using Python. With social media, mobile, changing search engine algorithms, and ever-increasing expectations of users for super web experiences, too much data is generated for an SEO professional to make sense of in spreadsheets. For any modern-day SEO professional to succeed, it is relevant to find an alternate solution, and data science equips SEOs to grasp the issue at hand and solve it. From machine learning to Natural Language Processing (NLP) techniques, \u003ci\u003eData-Driven SEO\u003c\/i\u003e \u003ci\u003ewith Python\u003c\/i\u003e provides tried and tested techniques with full explanations for solving both everyday and complex SEO problems.\u003cp\u003e\u003c\/p\u003e\n\u003cp\u003eThis book is ideal for SEO professionals who want to take their industry ski\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cb\u003e\u003ci\u003eData Driven SEO with Python\u003c\/i\u003e\u003c\/b\u003e\u003cb\u003e\u003ci\u003e\u003cbr\u003e\u003c\/i\u003e\u003c\/b\u003e\u003cb\u003eChapter 1: Meeting the Challenges of SEO with Data\u003c\/b\u003e1.1 Agents of change in SEO1.2 The Pillars of SEO Strategy1.3 Installing Python1.4 Using Python for SEO\u003cb\u003eChapter 2: Keyword Research\u003c\/b\u003e\u003cbr\u003e2.1 Data Sources2.2 Google Search Console2.4 Google Trends2.5 Google Suggest2.6 Competitor Analytics2.7 SERPs\u003cb\u003eChapter 3: Technical\u003c\/b\u003e\u003cbr\u003e3.1 Improving CTRs3.2 Allocate keywords to pages based on the copy3.3 Allocating parent nodes to the orphaned URLs3.4 Improve interlinking based on copy3.5 Automate Technical Audits\u003cb\u003eChapter 4: Content \u0026amp; UX\u003c\/b\u003e\u003cbr\u003e4.1 Content that best satisfies the user query4.2 Splitting and merging URLs4.3 Content Strategy: Planning landing page content \u003cb\u003eChapter 5: Authority\u003c\/b\u003e\u003cbr\u003e5.1 A little SEO history5.1 The source of authority5.2 Finding good links\u003cb\u003eChapter 6: Competitors\u003c\/b\u003e\u003cbr\u003e6.1 Defining the problem6.2 Data Strategy6.3 Data Sources6.4 Selecting Your Competitors6.5 Get Features6.6 Explore, Clean and Transform6.7 Modelling The SERPS6.8 Evaluating your Model6.9 Activation\u003cb\u003eChapter 7: Experiments\u003c\/b\u003e\u003cbr\u003e7.1 How experiments fit into the SEO process7.2 Generating Hypotheses7.3 Experiment Design7.4 Running your experiment7.5 Experiment Evaluation\u003cb\u003eChapter 8: Dashboards\u003c\/b\u003e\u003cbr\u003e8.1 Use a Data Layer8.2 Extract, Transform and Load (ETL)8.3 Transform8.4 Querying the Data Warehouse (DW)8.5 Visualization8.6 Making Future Forecasts\u003cb\u003eChapter 9: Site Migrations and Relaunches\u003c\/b\u003e\u003cbr\u003e9.1 Data sources9.2 Establishing the Impact9.3 Segmenting the URLs9.4 Legacy Site URLs9.5 Priority9.6 Roadmap\u003cb\u003eChapter 10: Google Updates\u003c\/b\u003e\u003cbr\u003e10.1 Data sources10.2 Winners and Losers10.3 Quantifying the Impact10.4 Search Intent10.5 Unique URLs10.6 Recommendations\u003cb\u003eChapter 11: The Future of SEO\u003c\/b\u003e\u003cbr\u003e11.1 Automation11.2 Your journey to SEO science11.3 Suggest resources\u003cb\u003eAppendix: Code\u003cbr\u003e\u003c\/b\u003e\u003cb\u003eGlossary\u003cbr\u003e\u003c\/b\u003e\u003cb\u003eIndex\u003c\/b\u003e\u003cbr\u003e\u003c\/p\u003e\n\u003c\/div\u003e","brand":"APress","offers":[{"title":"Default Title","offer_id":48867298804055,"sku":"9781484291740","price":29.69,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781484291740.jpg?v=1722282668"},{"product_id":"paradigms-of-combinatorial-optimization-problems-and-new-approaches-volume-2-9781848211483","title":"Paradigms of Combinatorial Optimization: Problems","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eCombinatorial optimization is a multidisciplinary scientific area, lying in the interface of three major scientific domains: mathematics, theoretical computer science and management.\u003cbr\u003e The three volumes of the Combinatorial Optimization series aims to cover a wide range of topics in this area. These topics also deal with fundamental notions and approaches as with several classical applications of combinatorial optimization.\u003cbr\u003e \u003cbr\u003e   \u003cp\u003e\u003cbr\u003e “Paradigms of Combinatorial Optimization” is divided in two parts:\u003cbr\u003e • Paradigmatic Problems, that handles several famous combinatorial optimization problems as max cut, min coloring, optimal satisfiability tsp, etc., the study of which has largely contributed to both the development, the legitimization and the establishment of the Combinatorial Optimization as one of the most active actual scientific domains;\u003cbr\u003e • Classical and New Approaches, that presents the several methodological approaches that fertilize and are fertilized by Combinatorial optimization such as: Polynomial Approximation, Online Computation, Robustness, etc., and, more recently, Algorithmic Game Theory.\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e\"Finally, the essay is useful for researchers and scientists in diverse fields (mathematics, programmers, engineers, etc.) as well as post-graduate students (and even undergraduates).\" (Contemporary Physics, 19 August 2011)  \u003cp\u003e \u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e\u003cb\u003ePreface xvii\u003c\/b\u003e\u003cbr\u003e Vangelis Th. PASCHOS\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePART I. PARADIGMATIC PROBLEMS 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eChapter 1. Optimal Satisfiability 3\u003cbr\u003e Cristina BAZGAN\u003c\/p\u003e \u003cp\u003eChapter 2. Scheduling Problems 33\u003cbr\u003e Philippe CHRÉTIENNE and Christophe PICOULEAU\u003c\/p\u003e \u003cp\u003eChapter 3. Location Problems 61\u003cbr\u003e Aristotelis GIANNAKOS\u003c\/p\u003e \u003cp\u003eChapter 4. MiniMax Algorithms and Games 89\u003cbr\u003e Michel KOSKAS\u003c\/p\u003e \u003cp\u003eChapter 5. Two-dimensional Bin Packing Problems 107\u003cbr\u003e Andrea LODI, Silvano MARTELLO, Michele MONACI and Daniele VIGO\u003c\/p\u003e \u003cp\u003eChapter 6. The Maximum Cut Problem 131\u003cbr\u003e Walid BEN-AMEUR, Ali Ridha MAHJOUB and José NETO\u003c\/p\u003e \u003cp\u003eChapter 7. The Traveling Salesman Problem and its Variations 173\u003cbr\u003e Jérôme MONNOT and Sophie TOULOUSE\u003c\/p\u003e \u003cp\u003eChapter 8. 0–1 Knapsack Problems 215\u003cbr\u003e Gérard PLATEAU and Anass NAGIH\u003c\/p\u003e \u003cp\u003eChapter 9. Integer Quadratic Knapsack Problems 243\u003cbr\u003e Dominique QUADRI, Eric SOUTIF and Pierre TOLLA\u003c\/p\u003e \u003cp\u003eChapter 10. Graph Coloring Problems 265\u003cbr\u003e Dominique DE WERRA and Daniel KOBLER\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePART II. NEW APPROACHES 311\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eChapter 11. Polynomial Approximation 313\u003cbr\u003e Marc DEMANGE and Vangelis Th. PASCHOS\u003c\/p\u003e \u003cp\u003eChapter 12. Approximation Preserving Reductions 351\u003cbr\u003e Giorgio AUSIELLO and Vangelis Th. PASCHOS\u003c\/p\u003e \u003cp\u003eChapter 13. Inapproximability of Combinatorial Optimization Problems 381\u003cbr\u003e Luca TREVISAN\u003c\/p\u003e \u003cp\u003eChapter 14. Local Search: Complexity and Approximation 435\u003cbr\u003e Eric ANGEL, Petros CHRISTOPOULOS and Vassilis ZISSIMOPOULOS\u003c\/p\u003e \u003cp\u003eChapter 15. On-line Algorithms 473\u003cbr\u003e Giorgio AUSIELLO and Luca BECCHETTI\u003c\/p\u003e \u003cp\u003eChapter 16. Polynomial Approximation for Multicriteria Combinatorial Optimization Problems 511\u003cbr\u003e Eric ANGEL, Evripidis BAMPIS and Laurent GOURVÈS\u003c\/p\u003e \u003cp\u003eChapter 17. An Introduction to Inverse Combinatorial Problems 547\u003cbr\u003e Marc DEMANGE and Jérôme MONNOT\u003c\/p\u003e \u003cp\u003eChapter 18. Probabilistic Combinatorial Optimization 587\u003cbr\u003e Cécile MURAT and Vangelis Th. PASCHOS\u003c\/p\u003e \u003cp\u003eChapter 19. Robust Shortest Path Problems 615\u003cbr\u003e Virginie GABREL and Cécile MURAT\u003c\/p\u003e \u003cp\u003eChapter 20. Algorithmic Games 641\u003cbr\u003e Aristotelis GIANNAKOS and Vangelis PASCHOS\u003c\/p\u003e \u003cp\u003e\u003cb\u003eList of Authors 675\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eIndex 681\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eSummary of Other Volumes in the Series 689\u003c\/b\u003e\u003c\/p\u003e","brand":"ISTE Ltd and John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":48868737352023,"sku":"9781848211483","price":294.45,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781848211483.jpg?v=1722289472"},{"product_id":"new-trends-in-fractional-programming-9781536153712","title":"New Trends in Fractional Programming","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eThis monograph presents smooth, unified, and generalized fractional programming problems, particularly advanced duality models for discrete min-max fractional programming. In the current, interdisciplinary, computer-oriented research environment, these programs are among the most rapidly expanding research areas in terms of their multi-faceted applications including problems ranging from robotics to money market portfolio management. The other more significant aspect of this monograph is in its consideration of minimax fractional integral type problems using higher order sonvexity and sounivexity notions. This is significant for the development of different types of duality models in terms of weak, strong, and strictly converse duality theorems, which can be handled by transforming them into generalized fractional programming problems. Fractional integral type programming is one of the fastest expanding areas of optimization, which feature several types of real-world problems. It can be applied to different branches of engineering (including multi-time multi-objective mechanical engineering problems) as well as to economics, to minimize a ratio of functions between given periods of time. Furthermore, it can be utilized as a resource in order to measure the efficiency or productivity of a system. In these types of problems, the objective function is given as a ratio of functions. For example, we consider a problem that deals with minimizing a maximum of several time-dependent ratios involving integral expressions.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003eFor more information, please visit our website at:https:\/\/novapublishers.com\/shop\/new-trends-in-fractional-programming\/","brand":"Nova Science Publishers Inc","offers":[{"title":"Default Title","offer_id":48886123921751,"sku":"9781536153712","price":163.19,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781536153712.jpg?v=1722538903"},{"product_id":"optimization-and-robotic-applications-9781536165258","title":"Optimization and Robotic Applications","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eOptimisation is the process of obtaining the most appropriate solution by providing certain constraints for the given purpose or purposes. Mathematically, optimisation can be briefly defined as minimising or maximising a function. In short, optimisation is to look for the best. The best found is called \"optimum. Optimisation is used to accelerate decision-making processes and to solve real-life problems in an effective, accurate and real-time manner. In addition to the economic benefits, optimisation is also used as an effective method to include the preferences and constraints of customers, employers and employees in the decision process and to improve the quality of the resources in the system. The purpose of optimisation is to achieve the best result, the best goal. Improvements can be made to the current situation or situations to achieve the best result. One of the major shortcomings in optimisation and robotic is the transformation of theoretical knowledge into practice. The purpose of the book is to introduce students, teachers, researchers, and practitioners to new advances in this area. The book content includes theoretical and practical studies prepared with the academic contributions of scientists working in different fields. It was decided to publish each chapter in the book after being examined by the scientific board. As an editor, my duty is to ensure breadth, while the chapter authors treat the delegated chapters with depth. The book is designed for practitioners or researchers of all levels of expertise from novice to expert. Each of the book's individual topics could be considered as a compact, self-contained mini-book right under its title. The approach is to provide a framework and a set of techniques for evaluating and improving optimisation and robotic. It presents a specific set of solutions, mostly obtained from real world projects and experimental studies, for routine applications. It further highlights promising emerging techniques for research and exploration opportunities. The development team of this book wants to thank their colleagues who made contributions to this book by providing continuous encouragements and thorough reviews of the chapters of the book.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003ePreface; A Source Seeking Algorithm with Application to a Quadcopter Model; Activation Functions for Deep Learning in Smart Manufacturing; A New Approach to Kaiser and Gaussian Window Based Cosine Modulated Filter Bank Design; A Preliminary Study of Region Of Interest Based Functional Connectivity Analysis for Classification of MDD and Healthy Subjects Using Graph Metrics; Investigation of the Effect of Graphene Oxide and Aluminum Oxide Particles on the Mechanical Properties of Glass \/ Kevlar \/Carbon Fiber Reinforced Epoxy Composites; Conversion from Conventional Power Distribution Networks to Smart Power Distribution Networks; A Parametric Study for Artificial Bee Colony Algorithm Used in Vehicle Routing Problem with Simultaneous Delivery and Pickup; Lightweighting Airborne Vehicles Structural Analysis of Carbon Fiber-Reinforced Aluminum and High Altitude Long Endurance New Drone: Octocopter Robot Unmanned Aerial Vehicles (UAVs) Design by Computational Fluid Dynamics and Finite Element Method; Index.","brand":"Nova Science Publishers Inc","offers":[{"title":"Default Title","offer_id":48886149546327,"sku":"9781536165258","price":113.59,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781536165258.jpg?v=1722538993"},{"product_id":"advances-in-economics-optimization-collected-scientific-papers-dedicated-to-the-memory-of-l-v-kantorovich-9781631170737","title":"Advances in Economics \u0026 Optimization: Collected","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eEconomics is a science which studies human behaviours as a relationship between ends and scarce means which have alternative uses. Since economic resources are scarce, optimisation forms an integral part in the study of economics. In addition, in the presence of imperfect market structure, externalities, imperfect information or public goods, the market fails to provide an efficient allocation mechanism. Optimisation of economic activities provides an effective remedial measure for market failures. This contributed volume collects advances in the studies of economics and optimisation. Contributions cover areas on analysis of optimal allocation of economic resources, economic optimisation techniques, the interface economics and optimisation, optimisation under market mechanism and history of development of optimisation techniques. The studies assembled in this volume are dedicated to the memory of a pioneering researcher and Nobel Laureate in the field of economic optimisation -- Leonid Vitalyevich Kantorovich. In his 100th birthday tribute in 2012, the International Conference Mathematics, Economic, Management: Kantorovich-100 in St-Petersburg was held in his memory. Selected papers from the conference are included in this Volume. In addition, contributed papers from authors who had worked closely with Kantorovich are also contained.","brand":"Nova Science Publishers Inc","offers":[{"title":"Default Title","offer_id":48887100408151,"sku":"9781631170737","price":159.74,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781631170737.jpg?v=1722542999"},{"product_id":"optimization-techniques-9781906574215","title":"Optimization Techniques","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e","brand":"New Age International (UK) Ltd","offers":[{"title":"Default Title","offer_id":48888407228759,"sku":"9781906574215","price":47.5,"currency_code":"GBP","in_stock":true}]},{"product_id":"piecewise-affine-control-continuous-time-sampled-data-and-networked-systems-9781611975895","title":"Piecewise Affine Control: Continuous-Time,","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eEngineering systems operate through actuators, most of which will exhibit phenomena such as saturation or zones of no operation, commonly known as dead zones. These are examples of piecewise-affine characteristics, and they can have a considerable impact on the stability and performance of engineering systems. This book targets controller design for piecewise affine systems, fulfilling both stability and performance requirements.\u003cbr\u003e\u003cbr\u003eThe authors present a unified computational methodology for the analysis and synthesis of piecewise affine controllers, taking an approach that is capable of handling sliding modes, sampled-data, and networked systems. They introduce algorithms that will be applicable to nonlinear systems approximated by piecewise affine systems, and they feature several examples from areas such as switching electronic circuits, autonomous vehicles, neural networks, and aerospace applications.\u003cbr\u003e\u003cbr\u003e\u003cem\u003ePiecewise Affine Control: Continuous-Time, Sampled-Data, and Networked Systems\u003c\/em\u003e is intended for graduate students, advanced senior undergraduate students, and researchers in academia and industry. It is also appropriate for engineers working on applications where switched linear and affine models are important.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003ePiecewise affine systems are widely used as modeling and design tools across a number of applications, ranging from robotics to systems biology. These systems require a delicate touch as they can exhibit complex and sometimes surprising features. This impressive book navigates the world of such systems with clarity, technical depth, and elegance.”- Professor Magnus Egerstedt, Georgia Institute of Technology","brand":"Society for Industrial \u0026 Applied Mathematics,U.S.","offers":[{"title":"Default Title","offer_id":49084186952023,"sku":"9781611975895","price":78.2,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781611975895.jpg?v=1725551329"},{"product_id":"introduction-to-unconstrained-optimization-with-r-9789811508936","title":"Introduction to Unconstrained Optimization with R","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eThis book discusses unconstrained optimization with R—a free, open-source computing environment, which works on several platforms, including Windows, Linux, and macOS. The book highlights methods such as the steepest descent method, Newton method, conjugate direction method, conjugate gradient methods, quasi-Newton methods, rank one correction formula, DFP method, BFGS method and their algorithms, convergence analysis, and proofs. Each method is accompanied by worked examples and R scripts. To help readers apply these methods in real-world situations, the book features a set of exercises at the end of each chapter. Primarily intended for graduate students of applied mathematics, operations research and statistics, it is also useful for students of mathematics, engineering, management, economics, and agriculture.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e1. Introduction.- 2. Mathematical Foundations.- 3. Basics of \u003cb\u003eR.\u003c\/b\u003e- 4. First Order and Second Order Necessary Conditions.- 5. One Dimensional Optimization Methods.- 6. Steepest Descent Method.- 7. Newton’s Method.- 8. Conjugate Direction Methods.- 9. Quasi-Newton Methods.","brand":"Springer Verlag, Singapore","offers":[{"title":"Default Title","offer_id":49084933931351,"sku":"9789811508936","price":999.99,"currency_code":"GBP","in_stock":false}]},{"product_id":"intelligent-optimization-9789819732852","title":"Intelligent Optimization","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eThis textbook comprehensively explores the foundational principles, algorithms, and applications of intelligent optimization, making it an ideal resource for both undergraduate and postgraduate artificial intelligence courses. It remains equally valuable for active researchers and individuals engaged in self-study. Serving as a significant reference, it delves into advanced topics within the evolutionary computation field, including multi-objective optimization, dynamic optimization, constrained optimization, robust optimization, expensive optimization, and other pivotal scientific studies related to optimization.   Designed to be approachable and inclusive, this textbook equips readers with the essential mathematical background necessary for understanding intelligent optimization. It employs an accessible writing style, complemented by extensive pseudo-code and diagrams that vividly illustrate the mechanisms, principles, and algorithms of optimization. With a focus on practicality, th","brand":"Springer","offers":[{"title":"Default Title","offer_id":49084950413655,"sku":"9789819732852","price":999.99,"currency_code":"GBP","in_stock":false}]},{"product_id":"programming-mathematics-using-matlab-9780128177990","title":"Programming Mathematics Using MATLAB","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003ePart 1. MATLAB 1. Introduction to MATLAB 2. Vectors and Matrices (Arrays) 3. Plotting in MATLAB 4. Three-Dimensional Plots 5. Functions  6. Control Flow 7. Miscellaneous Commands and Code Improvement   Part 2. Mathematics and MATLAB 8. Transformations and Fern Fractals 9. Complex Numbers and Fractals 10. Series and Taylor Polynomials 11. Numerical Integration 12. The Gram–Schmidt Process   Appendices A. Publishing and Live Scripts B. Final Projects C. Linear Algebra Projects D. Multivariable Calculus Projects","brand":"Elsevier Science Publishing Co Inc","offers":[{"title":"Default Title","offer_id":49399837753687,"sku":"9780128177990","price":60.0,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780128177990.jpg?v=1730468869"},{"product_id":"algorithms-for-optimization-9780262039420","title":"Algorithms for Optimization","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e","brand":"MIT Press Ltd","offers":[{"title":"Default Title","offer_id":49400686018903,"sku":"9780262039420","price":81.0,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780262039420.jpg?v=1730471293"},{"product_id":"service-science-9780470525883","title":"Service Science","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eThis book addresses the many important problems in service operations management, which can be analyzed using two core methodologies: optimization and queueing theory (including numerical simulation of queues).\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e\"The book is well written and very easy to follow. The reviewer highly recommends the book to be\u003cbr\u003e considered as a textbook for courses on service operations at the senior-undergraduate and graduate levels.\" (A Journal for the Worldwide Service Science Community, 2011) \u003cbr\u003e  \u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003ePreface.  \u003cp\u003eAcknowledgements.\u003c\/p\u003e \u003cp\u003e1. Why study services?\u003c\/p\u003e \u003cp\u003e1.1 What are services.\u003c\/p\u003e \u003cp\u003e1.2 Services as a percent of the economy.\u003c\/p\u003e \u003cp\u003e1.3 Public versus private service delivery.\u003c\/p\u003e \u003cp\u003e1.4 Why model services?\u003c\/p\u003e \u003cp\u003e1.5 Key service decisions.\u003c\/p\u003e \u003cp\u003e1.6 Philosophy about models.\u003c\/p\u003e \u003cp\u003e1.7 Outline of the book.\u003c\/p\u003e \u003cp\u003e1.8 Problems.\u003c\/p\u003e \u003cp\u003e1.9 References.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eMETHODOLOGICAL FOUNDATIONS.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2 Optimization.\u003c\/p\u003e \u003cp\u003e2.1 Introduction.\u003c\/p\u003e \u003cp\u003e2.2 Five key elements of optimization.\u003c\/p\u003e \u003cp\u003e2.3 Taxonomy of optimization models.\u003c\/p\u003e \u003cp\u003e2.4 You probably have seen one already.\u003c\/p\u003e \u003cp\u003e2.5 Linear programming.\u003c\/p\u003e \u003cp\u003e2.6 Special network form.\u003c\/p\u003e \u003cp\u003e2.7 Integer problems.\u003c\/p\u003e \u003cp\u003e2.8 Multiple objective problems.\u003c\/p\u003e \u003cp\u003e2.9 Mark’s ten rules of formulating problems.\u003c\/p\u003e \u003cp\u003e2.10 Problems.\u003c\/p\u003e \u003cp\u003e2.11 References.\u003c\/p\u003e \u003cp\u003e3 Queueing theory.\u003c\/p\u003e \u003cp\u003e3.1 Introduction.\u003c\/p\u003e \u003cp\u003e3.2 What is a queueing theory?\u003c\/p\u003e \u003cp\u003e3.3 Key performance metrics for queues and Little’s formula.\u003c\/p\u003e \u003cp\u003e3.4 A framework for Markovian queues.\u003c\/p\u003e \u003cp\u003e3.5 Key results for non-Markovian queues.\u003c\/p\u003e \u003cp\u003e3.6 Solving queueing models numerically.\u003c\/p\u003e \u003cp\u003e3.7 When conditions change over time.\u003c\/p\u003e \u003cp\u003e3.8 Conclusions.\u003c\/p\u003e \u003cp\u003e3.9 Problems.\u003c\/p\u003e \u003cp\u003e3.10 References.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAPPLICATION AREAS.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4 Location and districting problems in services.\u003c\/p\u003e \u003cp\u003e4.1 Example applications.\u003c\/p\u003e \u003cp\u003e4.2 Taxonomy of location problems.\u003c\/p\u003e \u003cp\u003e4.3 Covering problems.\u003c\/p\u003e \u003cp\u003e4.4 Median problems - minimizing the demand-weighted average distance.\u003c\/p\u003e \u003cp\u003e4.5 Multi-objective models.\u003c\/p\u003e \u003cp\u003e4.6 Districting problems.\u003c\/p\u003e \u003cp\u003e4.7 Franchise location problems.\u003c\/p\u003e \u003cp\u003e4.8 Summary and software.\u003c\/p\u003e \u003cp\u003e4.9 Problems.\u003c\/p\u003e \u003cp\u003e4.10 References.\u003c\/p\u003e \u003cp\u003e5 Inventory decisions in services.\u003c\/p\u003e \u003cp\u003e5.1 Why is inventory in a service modeling book?\u003c\/p\u003e \u003cp\u003e5.2 EOQ - a basic inventory model.\u003c\/p\u003e \u003cp\u003e5.3 Extensions of the EOQ model.\u003c\/p\u003e \u003cp\u003e5.4 Time varying demand.\u003c\/p\u003e \u003cp\u003e5.5 Uncertain demand and lead times.\u003c\/p\u003e \u003cp\u003e5.6 Newsvendor problem and applications.\u003c\/p\u003e \u003cp\u003e5.7 Summary.\u003c\/p\u003e \u003cp\u003e5.8 Problems.\u003c\/p\u003e \u003cp\u003e5.9 References.\u003c\/p\u003e \u003cp\u003e6 Resource allocation problems and decisions in services.\u003c\/p\u003e \u003cp\u003e6.1 Example resource allocation problems.\u003c\/p\u003e \u003cp\u003e6.2 How to formulate an assignment or resource allocation problem.\u003c\/p\u003e \u003cp\u003e6.3 Infeasible solutions.\u003c\/p\u003e \u003cp\u003e6.4 Assigning students to freshman seminars.\u003c\/p\u003e \u003cp\u003e6.5 Assigning students to intersession courses.\u003c\/p\u003e \u003cp\u003e6.6 Improving the assignment of zip codes to Congressional districts.\u003c\/p\u003e \u003cp\u003e6.7 Summary.\u003c\/p\u003e \u003cp\u003e6.8 Problems.\u003c\/p\u003e \u003cp\u003e6.9 References.\u003c\/p\u003e \u003cp\u003e7 Short-term workforce scheduling.\u003c\/p\u003e \u003cp\u003e7.1 Overview of scheduling.\u003c\/p\u003e \u003cp\u003e7.2 Simple model.\u003c\/p\u003e \u003cp\u003e7.3 Extensions of the simple model.\u003c\/p\u003e \u003cp\u003e7.4 More difficult extensions.\u003c\/p\u003e \u003cp\u003e7.5 Linking scheduling to service.\u003c\/p\u003e \u003cp\u003e7.6 Time-dependent queueing analyzer.\u003c\/p\u003e \u003cp\u003e7.7 Assigning specific employees to shifts.\u003c\/p\u003e \u003cp\u003e7.8 Summary.\u003c\/p\u003e \u003cp\u003e7.9 Problems.\u003c\/p\u003e \u003cp\u003e7.10 References.\u003c\/p\u003e \u003cp\u003e8 Long-term workforce planning.\u003c\/p\u003e \u003cp\u003e8.1 Why is long-term workforce planning an issue?\u003c\/p\u003e \u003cp\u003e8.2 Basic model.\u003c\/p\u003e \u003cp\u003e8.3 Grouping of skills.\u003c\/p\u003e \u003cp\u003e8.4 Planning over time.\u003c\/p\u003e \u003cp\u003e8.5 Linking to project scheduling.\u003c\/p\u003e \u003cp\u003e8.6 Linking to personnel training and planning in general.\u003c\/p\u003e \u003cp\u003e8.7 Simple model of training.\u003c\/p\u003e \u003cp\u003e8.8 Summary.\u003c\/p\u003e \u003cp\u003e8.9 Problems.\u003c\/p\u003e \u003cp\u003e8.10 References.\u003c\/p\u003e \u003cp\u003e9 Priority services, call center design and customer scheduling.\u003c\/p\u003e \u003cp\u003e9.1 Examples.\u003c\/p\u003e \u003cp\u003e9.2 Priority queueing for emergency and other services.\u003c\/p\u003e \u003cp\u003eservice in each class with non-preemptive priorities.\u003c\/p\u003e \u003cp\u003e9.2.3 Priority service with Poisson arrivals, multiple servers and identically distributed exponential service times..\u003c\/p\u003e \u003cp\u003e9.2.4 Preemptive queueing.\u003c\/p\u003e \u003cp\u003e9.3 Call center design.\u003c\/p\u003e \u003cp\u003e9.4 Scheduling in services.\u003c\/p\u003e \u003cp\u003e9.5 Summary.\u003c\/p\u003e \u003cp\u003e9.6 Problems.\u003c\/p\u003e \u003cp\u003e9.7 References.\u003c\/p\u003e \u003cp\u003e10 Vehicle routing and services.\u003c\/p\u003e \u003cp\u003e10.1 Example routing problems.\u003c\/p\u003e \u003cp\u003e10.2 Classification of routing problems.\u003c\/p\u003e \u003cp\u003e10.3 Arc routing.\u003c\/p\u003e \u003cp\u003e10.4 The traveling salesman problem.\u003c\/p\u003e \u003cp\u003e10.5 Vehicle routing problems.\u003c\/p\u003e \u003cp\u003e10.6 Summary.\u003c\/p\u003e \u003cp\u003e10.7 Problems.\u003c\/p\u003e \u003cp\u003e10.8 References.\u003c\/p\u003e \u003cp\u003e11 Where to from here?\u003c\/p\u003e \u003cp\u003e11.1 Introduction.\u003c\/p\u003e \u003cp\u003e11.2 Other methodologies.\u003c\/p\u003e \u003cp\u003e11.3 Other applications in services.\u003c\/p\u003e \u003cp\u003e11.4 Summary.\u003c\/p\u003e \u003cp\u003e11.5 References.\u003c\/p\u003e \u003cp\u003eAPPENDICES.\u003c\/p\u003e \u003cp\u003eA. Sums of series - basic formulae.\u003c\/p\u003e \u003cp\u003eB. Overview of probability.\u003c\/p\u003e \u003cp\u003eB.1. Introduction and basic definitions.\u003c\/p\u003e \u003cp\u003eB.2 Axioms of probability ..\u003c\/p\u003e \u003cp\u003eB.3 Joint, marginal and conditional probabilities and Bayes’ theorem.\u003c\/p\u003e \u003cp\u003eB.4 Counting, ordered pairs, permutations and combinations.\u003c\/p\u003e \u003cp\u003eB.5 Random variables.\u003c\/p\u003e \u003cp\u003eB.6 Discrete random variables.\u003c\/p\u003e \u003cp\u003eB.7 Continuous random variables.\u003c\/p\u003e \u003cp\u003eB.8 Moment and probability generating functions.\u003c\/p\u003e \u003cp\u003eB.9 Generating random variables.\u003c\/p\u003e \u003cp\u003eB.10 Random variables in Excel.\u003c\/p\u003e \u003cp\u003eC. References.\u003c\/p\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":49402354762071,"sku":"9780470525883","price":124.15,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780470525883.jpg?v=1730480158"},{"product_id":"optimization-by-vector-space-methods-9780471181170","title":"Optimization by Vector Space Methods","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eEngineers must make decisions regarding the distribution of expensive resources in a manner that will be economically beneficial. This problem can be realistically formulated and logically analyzed with optimization theory. This book shows engineers how to use optimization theory to solve complex problems.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003eLinear Spaces.\u003cbr\u003e \u003cbr\u003e Hilbert Space.\u003cbr\u003e \u003cbr\u003e Least-Squares Estimation.\u003cbr\u003e \u003cbr\u003e Dual Spaces.\u003cbr\u003e \u003cbr\u003e Linear Operators and Adjoints.\u003cbr\u003e \u003cbr\u003e Optimization of Functionals.\u003cbr\u003e \u003cbr\u003e Global Theory of Constrained Optimization.\u003cbr\u003e \u003cbr\u003e Local Theory of Constrained Optimization.\u003cbr\u003e \u003cbr\u003e Iterative Methods of Optimization.\u003cbr\u003e \u003cbr\u003e Indexes.","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":49402516275543,"sku":"9780471181170","price":119.65,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780471181170.jpg?v=1730480627"},{"product_id":"realtime-optimization-by-extremumseeking-control-9780471468592","title":"RealTime Optimization by ExtremumSeeking Control","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eAn up-close look at the theory behind and application of extremum seeking\u003cbr\u003e Originally developed as a method of adaptive control for hard-to-model systems, extremum seeking solves some of the same problems as today''s neural network techniques, but in a more rigorous and practical way. Following the resurgence in popularity of extremum-seeking control in aerospace and automotive engineering, Real-Time Optimization by Extremum-Seeking Control presents the theoretical foundations and selected applications of this method of real-time optimization.\u003cbr\u003e Written by authorities in the field and pioneers in adaptive nonlinear control systems, this book presents both significant theoretic value and important practical potential. Filled with in-depth insight and expert advice, Real-Time Optimization by Extremum-Seeking Control:\u003cbr\u003e * Develops optimization theory from the points of dynamic feedback and adaptation\u003cbr\u003e * Builds a solid bridge between the classical optimization theory and \u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e\"The subject matter is hard; this short book is therefore presented as an overview.\" (\u003ci\u003eComputing Reviws.com\u003c\/i\u003e, March 26, 2004)  \u003cp\u003e\"…a well-written and authoritative book…an essential resource for learning about extremum-seeking control and for motivating further developments in this subject area.\" (\u003ci\u003eIEEE Control Systems Magazine\u003c\/i\u003e, April 2004)\u003c\/p\u003e \u003cp\u003e“...recommended..” (\u003ci\u003eChoice\u003c\/i\u003e, Vol. 41, No. 7, March 2004)\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003ePreface ix\u003c\/p\u003e \u003cp\u003eI Theory 1\u003c\/p\u003e \u003cp\u003eII Applications 91\u003c\/p\u003e \u003cp\u003eAppendices 199\u003c\/p\u003e \u003cp\u003eBibliography 223\u003c\/p\u003e \u003cp\u003eIndex 235\u003c\/p\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":49402604323159,"sku":"9780471468592","price":95.36,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780471468592.jpg?v=1730480929"},{"product_id":"practical-methods-of-optimization-9780471494638","title":"Practical Methods of Optimization","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eThis textbook provides a thorough treatment of standard methods such as linear and quadratic programming, Newton-like methods and the conjugate gradient method. The theoretical aspects of the subject include a treatment of optimality conditions and the significance of Lagrange multipliers.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003eUNCONSTRAINED OPTIMIZATION.\u003cbr\u003e \u003cbr\u003e Structure of Methods.\u003cbr\u003e \u003cbr\u003e Newton-like Methods.\u003cbr\u003e \u003cbr\u003e Conjugate Direction Methods.\u003cbr\u003e \u003cbr\u003e Restricted Step Methods.\u003cbr\u003e \u003cbr\u003e Sums of Squares and Nonlinear Equations.\u003cbr\u003e \u003cbr\u003e CONSTRAINED OPTIMIZATION.\u003cbr\u003e \u003cbr\u003e Linear Programming.\u003cbr\u003e \u003cbr\u003e The Theory of Constrained Optimization.\u003cbr\u003e \u003cbr\u003e Quadratic Programming.\u003cbr\u003e \u003cbr\u003e General Linearly Constrained Optimization.\u003cbr\u003e \u003cbr\u003e Nonlinear Programming.\u003cbr\u003e \u003cbr\u003e Other Optimization Problems.\u003cbr\u003e \u003cbr\u003e Non-Smooth Optimization.\u003cbr\u003e \u003cbr\u003e References.\u003cbr\u003e \u003cbr\u003e Subject Index.","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":49402614907223,"sku":"9780471494638","price":69.26,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780471494638.jpg?v=1730480973"},{"product_id":"combinatorial-optimization-33-wiley-series-in-discrete-mathematics-and-optimization-9780471558941","title":"Combinatorial Optimization 33 Wiley Series in","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eA complete, highly accessible introduction to one of today's most exciting areas of applied mathematics  One of the youngest, most vital areas of applied mathematics, combinatorial optimization integrates techniques from combinatorics, linear programming, and the theory of algorithms.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003eProblems and Algorithms.\u003cbr\u003e \u003cbr\u003e Optimal Trees and Paths.\u003cbr\u003e \u003cbr\u003e Maximum Flow Problems.\u003cbr\u003e \u003cbr\u003e Minimum-Cost Flow Problems.\u003cbr\u003e \u003cbr\u003e Optimal Matchings.\u003cbr\u003e \u003cbr\u003e Integrality of Polyhedra.\u003cbr\u003e \u003cbr\u003e The Traveling Salesman Problem.\u003cbr\u003e \u003cbr\u003e Matroids.\u003cbr\u003e \u003cbr\u003e NP and NP-Completeness.\u003cbr\u003e \u003cbr\u003e Appendix.\u003cbr\u003e \u003cbr\u003e Bibliography.\u003cbr\u003e \u003cbr\u003e Index.","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":49402631684439,"sku":"9780471558941","price":148.45,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780471558941.jpg?v=1730481050"},{"product_id":"network-models-in-optimization-and-their-applications-in-practice-9780471571384","title":"Network Models in Optimization and Their","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eUnique in that it focuses on formulation and case studies rather than solutions procedures covering applications for pure, generalized and integer networks, equivalent formulations plus successful techniques of network models.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003eNetform Origins and Uses: Why Modeling and Netforms AreImportant.\u003cbr\u003e \u003cbr\u003e Fundamental Models for Pure Networks.\u003cbr\u003e \u003cbr\u003e Additional Pure Network Formulation Techniques.\u003cbr\u003e \u003cbr\u003e Dynamic Network Models.\u003cbr\u003e \u003cbr\u003e Generalized Networks.\u003cbr\u003e \u003cbr\u003e Netforms with Discrete Requirements.\u003cbr\u003e \u003cbr\u003e Appendices.\u003cbr\u003e \u003cbr\u003e Index.","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":49402633060695,"sku":"9780471571384","price":188.06,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780471571384.jpg?v=1730481056"},{"product_id":"x-and-the-city-9780691154640","title":"X and the City","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eExplores a range of entertaining questions about urban life such as: How do you estimate the number of dental or doctor's offices, gas stations, restaurants, or movie theaters in a city of a given size? How can mathematics be used to maximize traffic flow through tunnels? And, more.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e\"[Adam's] writing is fun and accessible... College or even advanced high school mathematics instructors will find plenty of great examples here to supplement the standard calculus problem sets.\"--Library Journal \"For mathematics professionals, especially those engaged in teaching, this book does contain some novel examples that illustrate topics such as probability and analysis.\"--Choice \"Read this book and come away with a fresh view of how cities work. Enjoy it for the connections between mathematics and the real world. Share it with your friends, family, and maybe even a municipal planning commissioner or two!\"--Sandra L. Arlinghaus, Mathematical Reviews Clippings \"It goes without saying that the exposition is very friendly and lucid: this makes the vast majority of material accessible to a general audience interested in mathematical modeling and real life applications. This excellent book may well complement standard texts on engineering mathematics, mathematical modeling, applied mathematics, differential equations; it is a delightful and entertaining reading itself. Thank you, Vickie Kearn, the editor of A Mathematical Nature Walk, for suggesting the idea of this book to Professor Adam--your idea has been delightfully implemented!\"--Svitlana P. Rogovchenko, Zentralblatt MATH \"[Y]ou'll find this book quite extensive in how many different areas you can apply mathematics in the city and just how revealing even a simple model can be... A Mathematical Nature Walk opened my eyes to nature and now Adam has done the same for cities.\"--David S. Mazel, MAA Reviews \"The author has an entertaining style, interweaving clever stories with the process of mathematical modeling. This book is not designed as a textbook, although it could certainly be used as an interesting source of real-world problems and examples for advanced high school mathematics courses.\"--Theresa Jorgensen, Mathematics Teacher\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003ePreface xiii  Acknowledgments xvii   Chapter 1 Introduction: Cancer, Princess Dido, and the city 1   Chapter 2 Getting to the city 7   Chapter 3 Living in the city 15   Chapter 4   Eating in the city 35   Chapter 5 Gardening in the city 41   Chapter 6 Summer in the city 47   Chapter 7 Not driving in the city! 63   Chapter 8 Driving in the city 73   Chapter 9 Probability in the city 89   Chapter 10 Traffic in the city 97   Chapter 11 Car following in the city--I 107   Chapter 12 Car following in the city--II 113   Chapter 13 Congestion in the city 121   Chapter 14 Roads in the city 129   Chapter 15 Sex and the city 135   Chapter 16   Growth and the city 149   Chapter 17 The axiomatic city 159   Chapter 18 Scaling in the city 167   Chapter 19 Air pollution in the city 179   Chapter 20 Light in the city 191   Chapter 21 Nighttime in the city--I 209   Chapter 22 Nighttime in the city--II 221   Chapter 23 Lighthouses in the city? 233   Chapter 24 Disaster in the city? 247   Chapter 25 Getting away from the city 255   Appendix 1 Theorems for Princess Dido 261   Appendix 2 Dido and the sinc function 263   Appendix 3 Taxicab geometry 269   Appendix 4   The Poisson distribution 273   Appendix 5   The method of Lagrange multipliers 277    Appendix 6 A spiral braking path 279   Appendix 7 The average distance between two random  points in a circle 281   Appendix 8 Informal \"derivation\" of the logistic  differential equation 283   Appendix 9 A miniscule introduction to fractals 287   Appendix 10 Random walks and the diffusion equation 291   Appendix 11 Rainbow\/halo details 297   Appendix 12 The Earth as vacuum cleaner? 303   Annotated references and notes 309  Index 317","brand":"Princeton University Press","offers":[{"title":"Default Title","offer_id":49403790852439,"sku":"9780691154640","price":22.5,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780691154640.jpg?v=1730484553"},{"product_id":"powerup-9780691161518","title":"PowerUp","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e\"Lane explores secondary, or hidden, mathematical gems that a player might discover upon mature reflection. . . . Just as most car drivers prefer not to inquire how the internal combustion engine works, most video-type users prefer not to ask how computer magic works. For the few who do ask questions, Lane assures us and as his book testifies, 'there's a lot of mathematics under the surface'.\"\u003cb\u003e---Andrew James Simoson, \u003ci\u003eMathSciNet\u003c\/i\u003e\u003c\/b\u003e\u003cbr\u003e\"Lane explains some pretty technical concepts in an accessible way. . . . A fun survey of interesting maths related through the lens of video games.\"\u003cb\u003e---Paul Taylor, \u003ci\u003eAperiodical\u003c\/i\u003e\u003c\/b\u003e\u003cbr\u003e\"The examples [in \u003ci\u003ePower-Up\u003c\/i\u003e] were carefully chosen from very popular games, so even the most casual player will have heard of the vast majority of the games discussed. In general, Lane's writing is easy to digest, and the use of color and high-quality paper gives the book a nice look and feel.\" * Choice *\u003cbr\u003e\"\u003ci\u003ePower­Up\u003c\/i\u003e is a very readable book based on examples taken from popular video games. . . . It is a pity that too many people are deprived of the pleasure of finding things out via the intellectual game of mathematics. Hopefully, the effort of the likes of Matthew Lane will someday solve the severe marketing problem of mathematics.\" * Computing Reviews *\u003cbr\u003e\"Overall the book is excellent. Lane has written a high readable text with colorful illustrations. You won’t regret reading it and maybe \u003ci\u003ePower-Up\u003c\/i\u003e will add a new level of insight to your computer gaming.\" * MAA Reviews *\u003cbr\u003e\"Matthew Lane explores the mathematical underpinning many popular video games in this well-written and very enjoyable book that is pitched at a very broad audience\"\u003cb\u003e---Dominic Thorrington, \u003ci\u003eMathematics Today\u003c\/i\u003e\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003eAcknowledgments xi  Introduction 1  1. Let's Get Physical 7  1.1 Platforming Perils 7  1.2 Platforming in Three Dimensions 10  1.3 LittleBigPlanet: Exploring Physics through Gameplay 12  1.4 From 2D to 3D: Bending Laws in Portal 14  1.5 Exploring Reality with A Slower Speed of Light 18  1.6 Exploring Alternative Realities 21  1.7 Beyond Physics: Minecraft or Mine Field? 26  1.8 Closing Remarks 27  1.9 Addendum: Describing Distortion 29  2. Repeat Offenders 34  2.1 Let's Play the Feud! 34  2.2 Game Shows and Birthdays 36  2.3 Beyond the First Duplicate 39  2.4 The Draw Something Debacle 41  2.5 Delayed Repetition: Increasing N 46  2.6 Delayed Repetition:Weight Lifting 48  2.7 The Completionist's Dilemma 53  2.8 Closing Remarks 55  2.9 Addendum: In Search of a Minimal k 55  3. Get Out the Voting System 58  3.1 Everybody Votes, but Not for Everything 58  3.2 Plurality Voting: An Example 60  3.3 Ranked-Choice Voting Systems and Arrow's Impossibility Theorem 61  3.4 An Escape from Impossibility? 66  3.5 Is There a \"Best\" System? 68  3.6 What Game Developers Know that Politicians Don't 71  3.7 The Best of the Rest 76  3.8 Closing Remarks 82  3.9 Addendum: TheWilson Score Confidence Interval 83  4. Knowing the Score 86  4.1 Ranking Players 86  4.2 Orisinal Original 87  4.3 What's in a Score? 91  4.4 Threes! Company 98  4.5 A Mathematical Model of Threes! 100  4.6 Invalid Scores 105  4.7 Lowest of the Low 109  4.8 Highest of the High 116  4.9 Closing Remarks 121  5. The Thrill of the Chase 122  5.1 I'ma GonnaWin! 122  5.2 Shell Games 123  5.3 Green-Shelled Monsters 125  5.4 Generalizations and Limitations 129  5.5 Seeing Red 131  5.6 Apollonius Circle Pursuit 134  5.7 Overview of aWinning Strategy 136  5.8 Pinpointing the Intersections 141  5.9 Blast Radius 145  5.10 The Pursuer and Pursued in Ms. Pac-Man 148  5.11 Concluding Remarks 153  5.12 Addendum: The Pursuit Curve for Red Shells and a Refined Inequality 153  6. Gaming Complexity 158  6.1 From Russia with Fun 158  6.2 P, NP, and Kevin Bacon 160  6.3 Desktop Diversions 165  6.4 Platforming Problems 169  6.5 Fetch Quests: An Overview 170  6.6 Fetch Quests and Traveling Salesmen 175  6.7 Closing Remarks 183  7. The Friendship Realm 184  7.1 Taking It to the Next Level 184  7.2 Friendship as Gameplay: The Sims and Beyond 186  7.3 A Game-Inspired Friendship Model 190  7.4 Approximations to the Model 193  7.5 The Cost of Maintaining a Friendship 195  7.6 From Virtual Friends to Realistic Romance 198  7.7 Modeling Different Personalities 200  7.8 Improving the Model (Again!) 203  7.9 Concluding Remarks 209  8. Order in Chaos 210  8.1 The Essence of Chaos 210  8.2 Love in the Time of Chaos 211  8.3 Shell Games Revisited 216  8.4 How's theWeather? 223  8.5 Concluding Remarks 225  9. The Value of Games 227  9.1 More Important Than Math 227  9.2 Why Games? 230  9.3 What Next? 242  Notes 244  Bibliography 269  Index 273","brand":"Princeton University Press","offers":[{"title":"Default Title","offer_id":49403805598039,"sku":"9780691161518","price":25.2,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780691161518.jpg?v=1730484599"},{"product_id":"optimization-in-medicine-and-biology-03-engineering-management-innovation-9780849305634","title":"Optimization in Medicine and Biology 03","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eThanks to recent advancements, optimization is now recognized as a crucial component in research and decision-making across a number of fields. Through optimization, scientists have made tremendous advances in cancer treatment planning, disease control, and drug development, as well as in sequencing DNA, and identifying protein structures.   \u003cp\u003e\u003cb\u003eOptimization in Medicine and Biology\u003c\/b\u003e provides researchers with a comprehensive, single-source reference that will enable them to apply the very latest optimization techniques to their work. With contributions from pioneering international experts this volume integrates strong foundational theory, good modeling techniques, and efficient and robust algorithms with relevant applications   \u003c\/p\u003e\u003cp\u003eDivided into two sections, the first begins with mathematical programming techniques for medical decision making processes and demonstrates their application to optimizing pediatric vaccine formularies, kidney paired donation, and the cost-effectiveness \u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003e\u003cb\u003eMedicine.\u003c\/b\u003e Classification and Disease Prediction via Mathematical Programming. Using Influence Diagrams in Cost Effectiveness Analysis for Medical Decisions. Non-Bayesian Classification to Obtain High Quality Clinical Decisions. Optimizing Pediatric Vaccine Formularies. . Optimization Over Graphs for Kidney Paired Donation. Introduction to Radiation Therapy Planning Optimization. Beam Orientation Optimization Methods in Intensity Modulated Radiation Therapy Treatment Planning. Multileaf collimator shape matrix decomposition. Optimal Planning for Radiation Therapy. \u003cb\u003eBiology. \u003c\/b\u003eAn Introduction to Systems Biology for Mathematical Programmers. Algorithms for Genomics Analysis. Computational Methods for Probe Design and Selection. An Implementation of Logical Analysis of Data for Oligo Probe Selection. A New Dihedral Angle Measure for Protein Secondary Prediction. Optimization of Tumor Virotherapy with Recombinant Measles Viruses. Combating Microbial Resistance to Antimicrobial Agents through Dosing Regimen Optimization. Appendix.\u003c\/p\u003e","brand":"Taylor \u0026 Francis Ltd","offers":[{"title":"Default Title","offer_id":49406214144343,"sku":"9780849305634","price":237.34,"currency_code":"GBP","in_stock":true}]},{"product_id":"engineering-optimization-9781118936337","title":"Engineering Optimization","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e\u003cb\u003eAn Application-Oriented Introduction to Essential Optimization Concepts and Best Practices\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eOptimization is an inherent human tendency that gained new life after the advent of calculus; now, as the world grows increasingly reliant on complex systems, optimization has become both more important and more challenging than ever before. \u003ci\u003eEngineering Optimization\u003c\/i\u003e provides a practically-focused introduction to modern engineering optimization best practices, covering fundamental analytical and numerical techniques throughout each stage of the optimization process.\u003c\/p\u003e \u003cp\u003eAlthough essential algorithms are explained in detail, the focus lies more in the human function: how to create an appropriate objective function, choose decision variables, identify and incorporate constraints, define convergence, and other critical issues that define the success or failure of an optimization project.\u003c\/p\u003e \u003cp\u003eExamples, exercises, and homework throughout reinforce the author's do, not stud\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003eContents\u003c\/p\u003e \u003cp\u003ePreface xix\u003c\/p\u003e \u003cp\u003eAcknowledgments xxvii\u003c\/p\u003e \u003cp\u003eNomenclature xxix\u003c\/p\u003e \u003cp\u003eAbout the Companion Website xxxvii\u003c\/p\u003e \u003cp\u003e\u003cb\u003eSection 1 Introductory Concepts 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Optimization: Introduction and Concepts 3\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 Optimization and Terminology 3\u003c\/p\u003e \u003cp\u003e1.2 Optimization Concepts and Definitions 4\u003c\/p\u003e \u003cp\u003e1.3 Examples 6\u003c\/p\u003e \u003cp\u003e1.4 Terminology Continued 10\u003c\/p\u003e \u003cp\u003e1.4.1 Constraint 10\u003c\/p\u003e \u003cp\u003e1.4.2 Feasible Solutions 10\u003c\/p\u003e \u003cp\u003e1.4.3 Minimize or Maximize 11\u003c\/p\u003e \u003cp\u003e1.4.4 Canonical Form of the Optimization Statement 11\u003c\/p\u003e \u003cp\u003e1.5 Optimization Procedure 12\u003c\/p\u003e \u003cp\u003e1.6 Issues That Shape Optimization Procedures 16\u003c\/p\u003e \u003cp\u003e1.7 Opposing Trends 17\u003c\/p\u003e \u003cp\u003e1.8 Uncertainty 20\u003c\/p\u003e \u003cp\u003e1.9 Over- and Under-specification in Linear Equations 21\u003c\/p\u003e \u003cp\u003e1.10 Over- and Under-specification in Optimization 22\u003c\/p\u003e \u003cp\u003e1.11 Test Functions 23\u003c\/p\u003e \u003cp\u003e1.12 Significant Dates in Optimization 23\u003c\/p\u003e \u003cp\u003e1.13 Iterative Procedures 26\u003c\/p\u003e \u003cp\u003e1.14 Takeaway 27\u003c\/p\u003e \u003cp\u003e1.15 Exercises 27\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Optimization Application Diversity and Complexity 33\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 Optimization 33\u003c\/p\u003e \u003cp\u003e2.2 Nonlinearity 33\u003c\/p\u003e \u003cp\u003e2.3 Min, Max, Min–Max, Max–Min, … 34\u003c\/p\u003e \u003cp\u003e2.4 Integers and Other Discretization 35\u003c\/p\u003e \u003cp\u003e2.5 Conditionals and Discontinuities: Cliffs Ridges\/Valleys 36\u003c\/p\u003e \u003cp\u003e2.6 Procedures, Not Equations 37\u003c\/p\u003e \u003cp\u003e2.7 Static and Dynamic Models 38\u003c\/p\u003e \u003cp\u003e2.8 Path Integrals 38\u003c\/p\u003e \u003cp\u003e2.9 Economic Optimization and Other Nonadditive Cost Functions 38\u003c\/p\u003e \u003cp\u003e2.10 Reliability 39\u003c\/p\u003e \u003cp\u003e2.11 Regression 40\u003c\/p\u003e \u003cp\u003e2.12 Deterministic and Stochastic 42\u003c\/p\u003e \u003cp\u003e2.13 Experimental w.r.t. Modeled OF 43\u003c\/p\u003e \u003cp\u003e2.14 Single and Multiple Optima 44\u003c\/p\u003e \u003cp\u003e2.15 Saddle Points 45\u003c\/p\u003e \u003cp\u003e2.16 Inflections 46\u003c\/p\u003e \u003cp\u003e2.17 Continuum and Discontinuous DVs 47\u003c\/p\u003e \u003cp\u003e2.18 Continuum and Discontinuous Models 47\u003c\/p\u003e \u003cp\u003e2.19 Constraints and Penalty Functions 48\u003c\/p\u003e \u003cp\u003e2.20 Ranks and Categorization: Discontinuous OFs 50\u003c\/p\u003e \u003cp\u003e2.21 Underspecified OFs 51\u003c\/p\u003e \u003cp\u003e2.22 Takeaway 51\u003c\/p\u003e \u003cp\u003e2.23 Exercises 51\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Validation: Knowing That the Answer Is Right 53\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction 53\u003c\/p\u003e \u003cp\u003e3.2 Validation 53\u003c\/p\u003e \u003cp\u003e3.3 Advice on Becoming Proficient 55\u003c\/p\u003e \u003cp\u003e3.4 Takeaway 56\u003c\/p\u003e \u003cp\u003e3.5 Exercises 57\u003c\/p\u003e \u003cp\u003e\u003cb\u003eSection 2 Univariate Search Techniques 59\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Univariate (Single DV) Search Techniques 61\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Univariate (Single DV) 61\u003c\/p\u003e \u003cp\u003e4.2 Analytical Method of Optimization 62\u003c\/p\u003e \u003cp\u003e4.2.1 Issues with the Analytical Approach 63\u003c\/p\u003e \u003cp\u003e4.3 Numerical Iterative Procedures 64\u003c\/p\u003e \u003cp\u003e4.3.1 Newton’s Methods 64\u003c\/p\u003e \u003cp\u003e4.3.2 Successive Quadratic (A Surrogate Model or Approximating Model Method) 68\u003c\/p\u003e \u003cp\u003e4.4 Direct Search Approaches 70\u003c\/p\u003e \u003cp\u003e4.4.1 Bisection Method 70\u003c\/p\u003e \u003cp\u003e4.4.2 Golden Section Method 72\u003c\/p\u003e \u003cp\u003e4.4.3 Perspective at This Point 74\u003c\/p\u003e \u003cp\u003e4.4.4 Heuristic Direct Search 74\u003c\/p\u003e \u003cp\u003e4.4.5 Leapfrogging 76\u003c\/p\u003e \u003cp\u003e4.4.6 LF for Stochastic Functions 79\u003c\/p\u003e \u003cp\u003e4.5 Perspectives on Univariate Search Methods 82\u003c\/p\u003e \u003cp\u003e4.6 Evaluating Optimizers 85\u003c\/p\u003e \u003cp\u003e4.7 Summary of Techniques 85\u003c\/p\u003e \u003cp\u003e4.7.1 Analytical Method 86\u003c\/p\u003e \u003cp\u003e4.7.2 Newton’s (and Variants Like Secant) 86\u003c\/p\u003e \u003cp\u003e4.7.3 Successive Quadratic 86\u003c\/p\u003e \u003cp\u003e4.7.4 Golden Section Method 86\u003c\/p\u003e \u003cp\u003e4.7.5 Heuristic Direct 87\u003c\/p\u003e \u003cp\u003e4.7.6 Leapfrogging 87\u003c\/p\u003e \u003cp\u003e4.8 Takeaway 87\u003c\/p\u003e \u003cp\u003e4.9 Exercises 88\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Path Analysis 93\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction 93\u003c\/p\u003e \u003cp\u003e5.2 Path Examples 93\u003c\/p\u003e \u003cp\u003e5.3 Perspective About Variables 96\u003c\/p\u003e \u003cp\u003e5.4 Path Distance Integral 97\u003c\/p\u003e \u003cp\u003e5.5 Accumulation along a Path 99\u003c\/p\u003e \u003cp\u003e5.6 Slope along a Path 101\u003c\/p\u003e \u003cp\u003e5.7 Parametric Path Notation 103\u003c\/p\u003e \u003cp\u003e5.8 Takeaway 104\u003c\/p\u003e \u003cp\u003e5.9 Exercises 104\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Stopping and Convergence Criteria: 1-D Applications 107\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Stopping versus Convergence Criteria 107\u003c\/p\u003e \u003cp\u003e6.2 Determining Convergence 107\u003c\/p\u003e \u003cp\u003e6.2.1 Threshold on the OF 108\u003c\/p\u003e \u003cp\u003e6.2.2 Threshold on the Change in the OF 108\u003c\/p\u003e \u003cp\u003e6.2.3 Threshold on the Change in the DV 108\u003c\/p\u003e \u003cp\u003e6.2.4 Threshold on the Relative Change in the DV 109\u003c\/p\u003e \u003cp\u003e6.2.5 Threshold on the Relative Change in the OF 109\u003c\/p\u003e \u003cp\u003e6.2.6 Threshold on the Impact of the DV on the OF 109\u003c\/p\u003e \u003cp\u003e6.2.7 Convergence Based on Uncertainty Caused by the Givens 109\u003c\/p\u003e \u003cp\u003e6.2.8 Multiplayer Range 110\u003c\/p\u003e \u003cp\u003e6.2.9 Steady-State Convergence 110\u003c\/p\u003e \u003cp\u003e6.3 Combinations of Convergence Criteria 111\u003c\/p\u003e \u003cp\u003e6.4 Choosing Convergence Threshold Values 112\u003c\/p\u003e \u003cp\u003e6.5 Precision 112\u003c\/p\u003e \u003cp\u003e6.6 Other Convergence Criteria 113\u003c\/p\u003e \u003cp\u003e6.7 Stopping Criteria to End a Futile Search 113\u003c\/p\u003e \u003cp\u003e6.7.1 N Iteration Threshold 114\u003c\/p\u003e \u003cp\u003e6.7.2 Execution Error 114\u003c\/p\u003e \u003cp\u003e6.7.3 Constraint Violation 114\u003c\/p\u003e \u003cp\u003e6.8 Choices! 114\u003c\/p\u003e \u003cp\u003e6.9 Takeaway 114\u003c\/p\u003e \u003cp\u003e6.10 Exercises 115\u003c\/p\u003e \u003cp\u003e\u003cb\u003eSection 3 Multivariate Search Techniques 117\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Multidimension Application Introduction and the Gradient 119\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction 119\u003c\/p\u003e \u003cp\u003e7.2 Illustration of Surface and Terms 122\u003c\/p\u003e \u003cp\u003e7.3 Some Surface Analysis 123\u003c\/p\u003e \u003cp\u003e7.4 Parametric Notation 128\u003c\/p\u003e \u003cp\u003e7.5 Extension to Higher Dimension 130\u003c\/p\u003e \u003cp\u003e7.6 Takeaway 131\u003c\/p\u003e \u003cp\u003e7.7 Exercises 131\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Elementary Gradient-Based Optimizers: CSLS and ISD 135\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction 135\u003c\/p\u003e \u003cp\u003e8.2 Cauchy’s Sequential Line Search 135\u003c\/p\u003e \u003cp\u003e8.2.1 CSLS with Successive Quadratic 137\u003c\/p\u003e \u003cp\u003e8.2.2 CSLS with Newton\/Secant 138\u003c\/p\u003e \u003cp\u003e8.2.3 CSLS with Golden Section 138\u003c\/p\u003e \u003cp\u003e8.2.4 CSLS with Leapfrogging 138\u003c\/p\u003e \u003cp\u003e8.2.5 CSLS with Heuristic Direct Search 139\u003c\/p\u003e \u003cp\u003e8.2.6 CSLS Commentary 139\u003c\/p\u003e \u003cp\u003e8.2.7 CSLS Pseudocode 140\u003c\/p\u003e \u003cp\u003e8.2.8 VBA Code for a 2-DV Application 141\u003c\/p\u003e \u003cp\u003e8.3 Incremental Steepest Descent 144\u003c\/p\u003e \u003cp\u003e8.3.1 Pseudocode for the ISD Method 144\u003c\/p\u003e \u003cp\u003e8.3.2 Enhanced ISD 145\u003c\/p\u003e \u003cp\u003e8.3.3 ISD Code 148\u003c\/p\u003e \u003cp\u003e8.4 Takeaway 149\u003c\/p\u003e \u003cp\u003e8.5 Exercises 149\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Second-Order Model-Based Optimizers: SQ and NR 155\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction 155\u003c\/p\u003e \u003cp\u003e9.2 Successive Quadratic 155\u003c\/p\u003e \u003cp\u003e9.2.1 Multivariable SQ 156\u003c\/p\u003e \u003cp\u003e9.2.2 SQ Pseudocode 159\u003c\/p\u003e \u003cp\u003e9.3 Newton–Raphson 159\u003c\/p\u003e \u003cp\u003e9.3.1 NR Pseudocode 162\u003c\/p\u003e \u003cp\u003e9.3.2 Attenuate NR 163\u003c\/p\u003e \u003cp\u003e9.3.3 Quasi-Newton 166\u003c\/p\u003e \u003cp\u003e9.4 Perspective on CSLS, ISD, SQ, and NR 168\u003c\/p\u003e \u003cp\u003e9.5 Choosing Step Size for Numerical Estimate of Derivatives 169\u003c\/p\u003e \u003cp\u003e9.6 Takeaway 170\u003c\/p\u003e \u003cp\u003e9.7 Exercises 170\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Gradient-Based Optimizer Solutions: LM, RLM, CG, BFGS, RG, and GRG 173\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e10.1 Introduction 173\u003c\/p\u003e \u003cp\u003e10.2 Levenberg–Marquardt (LM) 173\u003c\/p\u003e \u003cp\u003e10.2.1 LM VBA Code for a 2-DV Case 175\u003c\/p\u003e \u003cp\u003e10.2.2 Modified LM (RLM) 176\u003c\/p\u003e \u003cp\u003e10.2.3 RLM Pseudocode 177\u003c\/p\u003e \u003cp\u003e10.2.4 RLM VBA Code for a 2-DV Case 178\u003c\/p\u003e \u003cp\u003e10.3 Scaled Variables 180\u003c\/p\u003e \u003cp\u003e10.4 Conjugate Gradient (CG) 182\u003c\/p\u003e \u003cp\u003e10.5 Broyden–Fletcher–Goldfarb–Shanno (BFGS) 183\u003c\/p\u003e \u003cp\u003e10.6 Generalized Reduced Gradient (GRG) 184\u003c\/p\u003e \u003cp\u003e10.7 Takeaway 186\u003c\/p\u003e \u003cp\u003e10.8 Exercises 186\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Direct Search Techniques 187\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e11.1 Introduction 187\u003c\/p\u003e \u003cp\u003e11.2 Cyclic Heuristic Direct (CHD) Search 188\u003c\/p\u003e \u003cp\u003e11.2.1 CHD Pseudocode 188\u003c\/p\u003e \u003cp\u003e11.2.2 CHD VBA Code 189\u003c\/p\u003e \u003cp\u003e11.3 Hooke–Jeeves (HJ) 192\u003c\/p\u003e \u003cp\u003e11.3.1 HJ Code in VBA 195\u003c\/p\u003e \u003cp\u003e11.4 Compare and Contrast CHD and HJ Features: A Summary 197\u003c\/p\u003e \u003cp\u003e11.5 Nelder–Mead (NM) Simplex: Spendley, Hext, and Himsworth 199\u003c\/p\u003e \u003cp\u003e11.6 Multiplayer Direct Search Algorithms 200\u003c\/p\u003e \u003cp\u003e11.7 Leapfrogging 201\u003c\/p\u003e \u003cp\u003e11.7.1 Convergence Criteria 208\u003c\/p\u003e \u003cp\u003e11.7.2 Stochastic Surfaces 209\u003c\/p\u003e \u003cp\u003e11.7.3 Summary 209\u003c\/p\u003e \u003cp\u003e11.8 Particle Swarm Optimization 209\u003c\/p\u003e \u003cp\u003e11.8.1 Individual Particle Behavior 210\u003c\/p\u003e \u003cp\u003e11.8.2 Particle Swarm 213\u003c\/p\u003e \u003cp\u003e11.8.3 PSO Equation Analysis 215\u003c\/p\u003e \u003cp\u003e11.9 Complex Method (CM) 216\u003c\/p\u003e \u003cp\u003e11.10 A Brief Comparison 217\u003c\/p\u003e \u003cp\u003e11.11 Takeaway 218\u003c\/p\u003e \u003cp\u003e11.12 Exercises 219\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Linear Programming 223\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e12.1 Introduction 223\u003c\/p\u003e \u003cp\u003e12.2 Visual Representation and Concepts 225\u003c\/p\u003e \u003cp\u003e12.3 Basic LP Procedure 228\u003c\/p\u003e \u003cp\u003e12.4 Canonical LP Statement 228\u003c\/p\u003e \u003cp\u003e12.5 LP Algorithm 229\u003c\/p\u003e \u003cp\u003e12.6 Simplex Tableau 230\u003c\/p\u003e \u003cp\u003e12.7 Takeaway 231\u003c\/p\u003e \u003cp\u003e12.8 Exercises 231\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Dynamic Programming 233\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e13.1 Introduction 233\u003c\/p\u003e \u003cp\u003e13.2 Conditions 236\u003c\/p\u003e \u003cp\u003e13.3 DP Concept 237\u003c\/p\u003e \u003cp\u003e13.4 Some Calculation Tips 240\u003c\/p\u003e \u003cp\u003e13.5 Takeaway 241\u003c\/p\u003e \u003cp\u003e13.6 Exercises 241\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 Genetic Algorithms and Evolutionary Computation 243\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e14.1 Introduction 243\u003c\/p\u003e \u003cp\u003e14.2 GA Procedures 243\u003c\/p\u003e \u003cp\u003e14.3 Fitness of Selection 245\u003c\/p\u003e \u003cp\u003e14.4 Takeaway 250\u003c\/p\u003e \u003cp\u003e14.5 Exercises 250\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15 Intuitive Optimization 253\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e15.1 Introduction 253\u003c\/p\u003e \u003cp\u003e15.2 Levels 254\u003c\/p\u003e \u003cp\u003e15.3 Takeaway 254\u003c\/p\u003e \u003cp\u003e15.4 Exercises 254\u003c\/p\u003e \u003cp\u003e\u003cb\u003e16 Surface Analysis II 257\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e16.1 Introduction 257\u003c\/p\u003e \u003cp\u003e16.2 Maximize Is Equivalent to Minimize the Negative 257\u003c\/p\u003e \u003cp\u003e16.3 Scaling by a Positive Number Does Not Change DV∗ 258\u003c\/p\u003e \u003cp\u003e16.4 Scaled and Translated OFs Do Not Change DV∗ 258\u003c\/p\u003e \u003cp\u003e16.5 Monotonic Function Transformation Does Not Change DV∗ 258\u003c\/p\u003e \u003cp\u003e16.6 Impact on Search Path or NOFE 261\u003c\/p\u003e \u003cp\u003e16.7 Inequality Constraints 263\u003c\/p\u003e \u003cp\u003e16.8 Transforming DVs 263\u003c\/p\u003e \u003cp\u003e16.9 Takeaway 263\u003c\/p\u003e \u003cp\u003e16.10 Exercises 263\u003c\/p\u003e \u003cp\u003e\u003cb\u003e17 Convergence Criteria 2: N-D Applications 265\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e17.1 Introduction 265\u003c\/p\u003e \u003cp\u003e17.2 Defining an Iteration 265\u003c\/p\u003e \u003cp\u003e17.3 Criteria for Single TS Deterministic Procedures 266\u003c\/p\u003e \u003cp\u003e17.4 Criteria for Multiplayer Deterministic Procedures 267\u003c\/p\u003e \u003cp\u003e17.5 Stochastic Applications 268\u003c\/p\u003e \u003cp\u003e17.7 Takeaway 269\u003c\/p\u003e \u003cp\u003e17.8 Exercises 269\u003c\/p\u003e \u003cp\u003e\u003cb\u003e18 Enhancements to Optimizers 271\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e18.1 Introduction 271\u003c\/p\u003e \u003cp\u003e18.2 Criteria for Replicate Trials 271\u003c\/p\u003e \u003cp\u003e18.3 Quasi-Newton 274\u003c\/p\u003e \u003cp\u003e18.4 Coarse–Fine Sequence 275\u003c\/p\u003e \u003cp\u003e18.5 Number of Players 275\u003c\/p\u003e \u003cp\u003e18.6 Search Range Adjustment 276\u003c\/p\u003e \u003cp\u003e18.7 Adjustment of Optimizer Coefficient Values or Options in Process 276\u003c\/p\u003e \u003cp\u003e18.8 Initialization Range 277\u003c\/p\u003e \u003cp\u003e18.9 OF and DV Transformations 277\u003c\/p\u003e \u003cp\u003e18.10 Takeaway 278\u003c\/p\u003e \u003cp\u003e18.11 Exercises 278\u003c\/p\u003e \u003cp\u003e\u003cb\u003eSection 4 Developing Your Application Statements 279\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e19 Scaled Variables and Dimensional Consistency 281\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e19.1 Introduction 281\u003c\/p\u003e \u003cp\u003e19.2 A Scaled Variable Approach 283\u003c\/p\u003e \u003cp\u003e19.3 Sampling of Issues with Primitive Variables 283\u003c\/p\u003e \u003cp\u003e19.4 Linear Scaling Options 285\u003c\/p\u003e \u003cp\u003e19.5 Nonlinear Scaling 286\u003c\/p\u003e \u003cp\u003e19.6 Takeaway 287\u003c\/p\u003e \u003cp\u003e19.7 Exercises 287\u003c\/p\u003e \u003cp\u003e\u003cb\u003e20 Economic Optimization 289\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e20.1 Introduction 289\u003c\/p\u003e \u003cp\u003e20.2 Annual Cash Flow 290\u003c\/p\u003e \u003cp\u003e20.3 Including Risk as an Annual Expense 291\u003c\/p\u003e \u003cp\u003e20.4 Capital 293\u003c\/p\u003e \u003cp\u003e20.5 Combining Capital and Nominal Annual Cash Flow 293\u003c\/p\u003e \u003cp\u003e20.6 Combining Time Value and Schedule of Capital and Annual Cash Flow 296\u003c\/p\u003e \u003cp\u003e20.7 Present Value 297\u003c\/p\u003e \u003cp\u003e20.8 Including Uncertainty 298\u003c\/p\u003e \u003cp\u003e20.8.1 Uncertainty Models 301\u003c\/p\u003e \u003cp\u003e20.8.2 Methods to Include Uncertainty in an Optimization 303\u003c\/p\u003e \u003cp\u003e20.9 Takeaway 304\u003c\/p\u003e \u003cp\u003e20.10 Exercises 304\u003c\/p\u003e \u003cp\u003e\u003cb\u003e21 Multiple OF and Constraint Applications 305\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e21.1 Introduction 305\u003c\/p\u003e \u003cp\u003e21.2 Solution 1: Additive Combinations of the Functions 306\u003c\/p\u003e \u003cp\u003e21.2.1 Solution 1a: Classic Weighting Factors 307\u003c\/p\u003e \u003cp\u003e21.2.2 Solution 1b: Equal Concern Weighting 307\u003c\/p\u003e \u003cp\u003e21.2.3 Solution 1c: Nonlinear Weighting 309\u003c\/p\u003e \u003cp\u003e21.3 Solution 2: Nonadditive OF Combinations 311\u003c\/p\u003e \u003cp\u003e21.4 Solution 3: Pareto Optimal 311\u003c\/p\u003e \u003cp\u003e21.5 Takeaway 316\u003c\/p\u003e \u003cp\u003e21.6 Exercises 316\u003c\/p\u003e \u003cp\u003e\u003cb\u003e22 Constraints 319\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e22.1 Introduction 319\u003c\/p\u003e \u003cp\u003e22.2 Equality Constraints 320\u003c\/p\u003e \u003cp\u003e22.2.1 Explicit Equality Constraints 320\u003c\/p\u003e \u003cp\u003e22.2.2 Implicit Equality Constraints 321\u003c\/p\u003e \u003cp\u003e22.3 Inequality Constraints 321\u003c\/p\u003e \u003cp\u003e22.3.1 Penalty Function: Discontinuous 323\u003c\/p\u003e \u003cp\u003e22.3.2 Penalty Function: Soft Constraint 323\u003c\/p\u003e \u003cp\u003e22.3.3 Inequality Constraints: Slack and Surplus Variables 325\u003c\/p\u003e \u003cp\u003e22.4 Constraints: Pass\/Fail Categories 329\u003c\/p\u003e \u003cp\u003e22.5 Hard Constraints Can Block Progress 330\u003c\/p\u003e \u003cp\u003e22.6 Advice 331\u003c\/p\u003e \u003cp\u003e22.7 Constraint-Equivalent Features 332\u003c\/p\u003e \u003cp\u003e22.8 Takeaway 332\u003c\/p\u003e \u003cp\u003e22.9 Exercises 332\u003c\/p\u003e \u003cp\u003e\u003cb\u003e23 Multiple Optima 335\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e23.1 Introduction 335\u003c\/p\u003e \u003cp\u003e23.2 Solution: Multiple Starts 337\u003c\/p\u003e \u003cp\u003e23.2.1 A Priori Method 340\u003c\/p\u003e \u003cp\u003e23.2.2 A Posteriori Method 342\u003c\/p\u003e \u003cp\u003e23.2.3 Snyman and Fatti Criterion A Posteriori Method 345\u003c\/p\u003e \u003cp\u003e23.3 Other Options 348\u003c\/p\u003e \u003cp\u003e23.4 Takeaway 349\u003c\/p\u003e \u003cp\u003e23.5 Exercises 350\u003c\/p\u003e \u003cp\u003e\u003cb\u003e24 Stochastic Objective Functions 353\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e24.1 Introduction 353\u003c\/p\u003e \u003cp\u003e24.2 Method Summary for Optimizing Stochastic Functions 356\u003c\/p\u003e \u003cp\u003e24.2.1 Step 1: Replicate the Apparent Best Player 356\u003c\/p\u003e \u003cp\u003e24.2.2 Step 2: Steady-State Detection 357\u003c\/p\u003e \u003cp\u003e24.3 What Value to Report? 358\u003c\/p\u003e \u003cp\u003e24.4 Application Examples 359\u003c\/p\u003e \u003cp\u003e24.4.1 GMC Control of Hot and Cold Mixing 359\u003c\/p\u003e \u003cp\u003e24.4.2 MBC of Hot and Cold Mixing 359\u003c\/p\u003e \u003cp\u003e24.4.3 Batch Reaction Management 359\u003c\/p\u003e \u003cp\u003e24.4.4 Reservoir and Stochastic Boot Print 361\u003c\/p\u003e \u003cp\u003e24.4.5 Optimization Results 362\u003c\/p\u003e \u003cp\u003e24.5 Takeaway 365\u003c\/p\u003e \u003cp\u003e24.6 Exercises 365\u003c\/p\u003e \u003cp\u003e\u003cb\u003e25 Effects of Uncertainty 367\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e25.1 Introduction 367\u003c\/p\u003e \u003cp\u003e25.2 Sources of Error and Uncertainty 368\u003c\/p\u003e \u003cp\u003e25.3 Significant Digits 370\u003c\/p\u003e \u003cp\u003e25.4 Estimating Uncertainty on Values 371\u003c\/p\u003e \u003cp\u003e25.5 Propagating Uncertainty on DV Values 372\u003c\/p\u003e \u003cp\u003e25.5.1 Analytical Method 373\u003c\/p\u003e \u003cp\u003e25.5.2 Numerical Method 375\u003c\/p\u003e \u003cp\u003e25.6 Implicit Relations 378\u003c\/p\u003e \u003cp\u003e25.7 Estimating Uncertainty in DV∗ and OF∗ 378\u003c\/p\u003e \u003cp\u003e25.8 Takeaway 379\u003c\/p\u003e \u003cp\u003e25.9 Exercises 379\u003c\/p\u003e \u003cp\u003e\u003cb\u003e26 Optimization of Probable Outcomes and Distribution Characteristics 381\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e26.1 Introduction 381\u003c\/p\u003e \u003cp\u003e26.2 The Concept of Modeling Uncertainty 385\u003c\/p\u003e \u003cp\u003e26.3 Stochastic Approach 387\u003c\/p\u003e \u003cp\u003e26.4 Takeaway 389\u003c\/p\u003e \u003cp\u003e26.5 Exercises 389\u003c\/p\u003e \u003cp\u003e\u003cb\u003e27 Discrete and Integer Variables 391\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e27.1 Introduction 391\u003c\/p\u003e \u003cp\u003e27.2 Optimization Solutions 394\u003c\/p\u003e \u003cp\u003e27.2.1 Exhaustive Search 394\u003c\/p\u003e \u003cp\u003e27.2.2 Branch and Bound 394\u003c\/p\u003e \u003cp\u003e27.2.3 Cyclic Heuristic 394\u003c\/p\u003e \u003cp\u003e27.2.4 Leapfrogging or Other Multiplayer Search 395\u003c\/p\u003e \u003cp\u003e27.3 Convergence 395\u003c\/p\u003e \u003cp\u003e27.4 Takeaway 395\u003c\/p\u003e \u003cp\u003e27.5 Exercises 395\u003c\/p\u003e \u003cp\u003e\u003cb\u003e28 Class Variables 397\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e28.1 Introduction 397\u003c\/p\u003e \u003cp\u003e28.2 The Random Keys Method: Sequence 398\u003c\/p\u003e \u003cp\u003e28.3 The Random Keys Method: Dichotomous Variables 400\u003c\/p\u003e \u003cp\u003e28.4 Comments 401\u003c\/p\u003e \u003cp\u003e28.5 Takeaway 401\u003c\/p\u003e \u003cp\u003e28.6 Exercises 401\u003c\/p\u003e \u003cp\u003e\u003cb\u003e29 Regression 403\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e29.1 Introduction 403\u003c\/p\u003e \u003cp\u003e29.2 Perspective 404\u003c\/p\u003e \u003cp\u003e29.3 Least Squares Regression: Traditional View on Linear Model Parameters 404\u003c\/p\u003e \u003cp\u003e29.4 Models Nonlinear in DV 405\u003c\/p\u003e \u003cp\u003e29.4.1 Models with a Delay 407\u003c\/p\u003e \u003cp\u003e29.5 Maximum Likelihood 408\u003c\/p\u003e \u003cp\u003e29.5.1 Akaho’s Method 411\u003c\/p\u003e \u003cp\u003e29.6 Convergence Criterion 416\u003c\/p\u003e \u003cp\u003e29.7 Model Order or Complexity 421\u003c\/p\u003e \u003cp\u003e29.8 Bootstrapping to Reveal Model Uncertainty 425\u003c\/p\u003e \u003cp\u003e29.8.1 Interpretation of Bootstrapping Analysis 428\u003c\/p\u003e \u003cp\u003e29.8.2 Appropriating Bootstrapping 430\u003c\/p\u003e \u003cp\u003e29.9 Perspective 431\u003c\/p\u003e \u003cp\u003e29.10 Takeaway 431\u003c\/p\u003e \u003cp\u003e29.11 Exercises 432\u003c\/p\u003e \u003cp\u003e\u003cb\u003eSection 5 Perspective on Many Topics 441\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e30 Perspective 443\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e30.1 Introduction 443\u003c\/p\u003e \u003cp\u003e30.2 Classifications 443\u003c\/p\u003e \u003cp\u003e30.3 Elements Associated with Optimization 445\u003c\/p\u003e \u003cp\u003e30.4 Root Finding Is Not Optimization 446\u003c\/p\u003e \u003cp\u003e30.5 Desired Engineering Attributes 446\u003c\/p\u003e \u003cp\u003e30.6 Overview of Optimizers and Attributes 447\u003c\/p\u003e \u003cp\u003e30.6.1 Gradient Based: Cauchy Sequential Line Search, Incremental Steepest Descent, GRG, Etc. 447\u003c\/p\u003e \u003cp\u003e30.6.2 Local Surface Characterization Based: Newton–Raphson, Levenberg–Marquardt, Successive Quadratic, RLM, Quasi-Newton, Etc. 448\u003c\/p\u003e \u003cp\u003e30.6.3 Direct Search with Single Trial Solution: Cyclic Heuristic, Hooke–Jeeves, and Nelder–Mead 448\u003c\/p\u003e \u003cp\u003e30.6.4 Multiplayer Direct Search Optimizers: Leapfrogging, Particle Swarm, and Genetic Algorithms 448\u003c\/p\u003e \u003cp\u003e30.7 Choices 448\u003c\/p\u003e \u003cp\u003e30.8 Variable Classifications 449\u003c\/p\u003e \u003cp\u003e30.8.1 Nominal 449\u003c\/p\u003e \u003cp\u003e30.8.2 Ordinal 450\u003c\/p\u003e \u003cp\u003e30.8.3 Cardinal 450\u003c\/p\u003e \u003cp\u003e30.9 Constraints 451\u003c\/p\u003e \u003cp\u003e30.10 Takeaway 453\u003c\/p\u003e \u003cp\u003e30.11 Exercises 453\u003c\/p\u003e \u003cp\u003e\u003cb\u003e31 Response Surface Aberrations 459\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e31.1 Introduction 459\u003c\/p\u003e \u003cp\u003e31.2 Cliffs (Vertical Walls) 459\u003c\/p\u003e \u003cp\u003e31.3 Sharp Valleys (or Ridges) 459\u003c\/p\u003e \u003cp\u003e31.4 Striations 463\u003c\/p\u003e \u003cp\u003e31.5 Level Spots (Functions 1, 27, 73, 84) 463\u003c\/p\u003e \u003cp\u003e31.6 Hard-to-Find Optimum 466\u003c\/p\u003e \u003cp\u003e31.7 Infeasible Calculations 468\u003c\/p\u003e \u003cp\u003e31.8 Uniform Minimum 468\u003c\/p\u003e \u003cp\u003e31.9 Noise: Stochastic Response 469\u003c\/p\u003e \u003cp\u003e31.10 Multiple Optima 471\u003c\/p\u003e \u003cp\u003e31.11 Takeaway 473\u003c\/p\u003e \u003cp\u003e31.12 Exercises 473\u003c\/p\u003e \u003cp\u003e\u003cb\u003e32 Identifying the Models, OF, DV, Convergence Criteria, and Constraints 475\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e32.1 Introduction 475\u003c\/p\u003e \u003cp\u003e32.2 Evaluate the Results 476\u003c\/p\u003e \u003cp\u003e32.3 Takeaway 482\u003c\/p\u003e \u003cp\u003e32.4 Exercises 482\u003c\/p\u003e \u003cp\u003e\u003cb\u003e33 Evaluating Optimizers 489\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e33.1 Introduction 489\u003c\/p\u003e \u003cp\u003e33.2 Challenges to Optimizers 490\u003c\/p\u003e \u003cp\u003e33.3 Stakeholders 490\u003c\/p\u003e \u003cp\u003e33.4 Metrics of Optimizer Performance 490\u003c\/p\u003e \u003cp\u003e33.5 Designing an Experimental Test 492\u003c\/p\u003e \u003cp\u003e33.6 Takeaway 495\u003c\/p\u003e \u003cp\u003e33.7 Exercises 496\u003c\/p\u003e \u003cp\u003e\u003cb\u003e34 Troubleshooting Optimizers 499\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e34.1 Introduction 499\u003c\/p\u003e \u003cp\u003e34.2 DV Values Do Not Change 499\u003c\/p\u003e \u003cp\u003e34.3 Multiple DV∗ Values for the Same OF∗ Value 499\u003c\/p\u003e \u003cp\u003e34.4 EXE Error 500\u003c\/p\u003e \u003cp\u003e34.5 Extreme Values 500\u003c\/p\u003e \u003cp\u003e34.6 DV∗ Is Dependent on Convergence Threshold 500\u003c\/p\u003e \u003cp\u003e34.7 OF∗ Is Irreproducible 501\u003c\/p\u003e \u003cp\u003e34.8 Concern over Results 501\u003c\/p\u003e \u003cp\u003e34.9 CDF Features 501\u003c\/p\u003e \u003cp\u003e34.10 Parameter Correlation 502\u003c\/p\u003e \u003cp\u003e34.11 Multiple Equivalent Solutions 504\u003c\/p\u003e \u003cp\u003e34.12 Takeaway 504\u003c\/p\u003e \u003cp\u003e34.13 Exercises 504\u003c\/p\u003e \u003cp\u003e\u003cb\u003eSection 6 Analysis of Leapfrogging Optimization 505\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e35 Analysis of Leapfrogging 507\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e35.1 Introduction 507\u003c\/p\u003e \u003cp\u003e35.2 Balance in an Optimizer 508\u003c\/p\u003e \u003cp\u003e35.3 Number of Initializations to be Confident That the Best Will Draw All Others to the Global Optimum 510\u003c\/p\u003e \u003cp\u003e35.3.1 Methodology 511\u003c\/p\u003e \u003cp\u003e35.3.2 Experimental 512\u003c\/p\u003e \u003cp\u003e35.3.3 Results 513\u003c\/p\u003e \u003cp\u003e35.4 Leap-To Window Amplification Analysis 515\u003c\/p\u003e \u003cp\u003e35.5 Analysis of α and M to Prevent Convergence on the Side of a Hill 519\u003c\/p\u003e \u003cp\u003e35.6 Analysis of α and M to Minimize NOFE 521\u003c\/p\u003e \u003cp\u003e35.7 Probability Distribution of Leap-Overs 522\u003c\/p\u003e \u003cp\u003e35.7.1 Data 526\u003c\/p\u003e \u003cp\u003e35.8 Takeaway 527\u003c\/p\u003e \u003cp\u003e35.9 Exercises 528\u003c\/p\u003e \u003cp\u003e\u003cb\u003eSection 7 Case Studies 529\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e36 Case Study 1: Economic Optimization of a Pipe System 531\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e36.1 Process and Analysis 531\u003c\/p\u003e \u003cp\u003e36.1.1 Deterministic Continuum Model 531\u003c\/p\u003e \u003cp\u003e36.1.2 Deterministic Discontinuous Model 534\u003c\/p\u003e \u003cp\u003e36.1.3 Stochastic Discontinuous Model 535\u003c\/p\u003e \u003cp\u003e36.2 Exercises 536\u003c\/p\u003e \u003cp\u003e\u003cb\u003e37 Case Study 2: Queuing Study 539\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e37.1 The Process and Analysis 539\u003c\/p\u003e \u003cp\u003e37.2 Exercises 541\u003c\/p\u003e \u003cp\u003e\u003cb\u003e38 Case Study 3: Retirement Study 543\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e38.1 The Process and Analysis 543\u003c\/p\u003e \u003cp\u003e38.2 Exercises 550\u003c\/p\u003e \u003cp\u003e\u003cb\u003e39 Case Study 4: A Goddard Rocket Study 551\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e39.1 The Process and Analysis 551\u003c\/p\u003e \u003cp\u003e39.2 Pre-Assignment Note 554\u003c\/p\u003e \u003cp\u003e39.3 Exercises 555\u003c\/p\u003e \u003cp\u003e\u003cb\u003e40 Case Study 5: Reservoir 557\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e40.1 The Process and Analysis 557\u003c\/p\u003e \u003cp\u003e40.2 Exercises 559\u003c\/p\u003e \u003cp\u003e\u003cb\u003e41 Case Study 6: Area Coverage 561\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e41.1 Description and Analysis 561\u003c\/p\u003e \u003cp\u003e41.2 Exercises 562\u003c\/p\u003e \u003cp\u003e\u003cb\u003e42 Case Study 7: Approximating Series Solution to an ODE 565\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e42.1 Concepts and Analysis 565\u003c\/p\u003e \u003cp\u003e42.2 Exercises 568\u003c\/p\u003e \u003cp\u003e\u003cb\u003e43 Case Study 8: Horizontal Tank Vapor–Liquid Separator 571\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e43.1 Description and Analysis 571\u003c\/p\u003e \u003cp\u003e43.2 Exercises 576\u003c\/p\u003e \u003cp\u003e\u003cb\u003e44 Case Study 9: In Vitro Fertilization 579\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e44.1 Description and Analysis 579\u003c\/p\u003e \u003cp\u003e44.2 Exercises 583\u003c\/p\u003e \u003cp\u003e\u003cb\u003e45 Case Study 10: Data Reconciliation 585\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e45.1 Description and Analysis 585\u003c\/p\u003e \u003cp\u003e45.2 Exercises 588\u003c\/p\u003e \u003cp\u003e\u003cb\u003eSection 8 Appendices 591\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eAppendix A Mathematical Concepts and Procedures 593\u003c\/p\u003e \u003cp\u003eAppendix B Root Finding 605\u003c\/p\u003e \u003cp\u003eAppendix C Gaussian Elimination 611\u003c\/p\u003e \u003cp\u003eAppendix D Steady-State Identification in Noisy Signals 621\u003c\/p\u003e \u003cp\u003eAppendix E Optimization Challenge Problems (2-D and Single OF) 635\u003c\/p\u003e \u003cp\u003eAppendix F Brief on VBA Programming: Excel in Office 2013 709\u003c\/p\u003e \u003cp\u003eSection 9 References and Index 717\u003c\/p\u003e \u003cp\u003eReferences and Additional Resources 719\u003c\/p\u003e \u003cp\u003eIndex 723\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":49406950900055,"sku":"9781118936337","price":100.65,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781118936337.jpg?v=1730497666"},{"product_id":"practical-financial-optimization-9781405132008","title":"Practical Financial Optimization","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eThis book gives a comprehensive account of financial optimization models used to support decision-making for financial engineers. It starts with the classical static mean-variance analysis and portfolio immunization, moves on to scenario-based models, and builds towards multi-period dynamic portfolio optimization.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e“This volume is both a comprehensive guide to optimization techniques useful in financial decision making and a well-illustrated essay on the relationship between theory and practice. While the real problem may always be more complex than any model of it we build, that does not necessarily imply that the largest, most complex model will serve us best. Zenios supplies the reader with a spectrum of optimization models, from simple to complex, and sage advice on how to use them.”\u003cbr\u003e \u003cb\u003eFrom the Foreword by Harry M. Markowitz, Nobel Laureate in Economics\u003c\/b\u003e\u003cbr\u003e \u003cp\u003e\u003cbr\u003e \u003c\/p\u003e \u003cp\u003e“Most books on portfolio optimization focus on continuous time stochastic control models. By contrast, Zenios’s decision to focus on mathematical programming models in financial engineering is an auspicious one. The book is well organized and clearly written, and uses a minimum of technical prerequisites (both mathematical and financial). It should therefore be accessible and of interest to a broad audience: industry practitioners interested in the potential application of optimization to the problems they face, students curious about how optimization is applied in finance, and professional researchers who would like a comprehensive overview of the uses of mathematical programming in financial engineering.”\u003cbr\u003e \u003cb\u003eDavid Saunders, University of Waterloo\u003c\/b\u003e\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003eForeword: Harry M. Markowitz. \u003cp\u003ePreface.\u003c\/p\u003e \u003cp\u003eAcknowledgments.\u003c\/p\u003e \u003cp\u003eNotation.\u003c\/p\u003e \u003cp\u003eList of Models.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eI. Introduction\u003c\/b\u003e.\u003c\/p\u003e \u003cp\u003e1. An Optimization View of Financial Engineering.\u003c\/p\u003e \u003cp\u003e2. Basics of Risk Management.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eII. Portfolio Optimization Models\u003c\/b\u003e.\u003c\/p\u003e \u003cp\u003e3. Mean-Variance Analysis.\u003c\/p\u003e \u003cp\u003e4. Portfolio Models for Fixed Income.\u003c\/p\u003e \u003cp\u003e5. Scenario Optimization.\u003c\/p\u003e \u003cp\u003e6. Dynamic Portfolio Optimization with Stochastic Programming.\u003c\/p\u003e \u003cp\u003e7. Index Funds.\u003c\/p\u003e \u003cp\u003e8. Designing Financial Products.\u003c\/p\u003e \u003cp\u003e9. Scenario Generation.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eIII. Applications.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e10. International Asset Allocation.\u003c\/p\u003e \u003cp\u003e11. Corporate Bond Portfolios.\u003c\/p\u003e \u003cp\u003e12. Insurance Policies with Guarantees.\u003c\/p\u003e \u003cp\u003e13. Personal Financial Planning.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eIV. Library of Financial Optimization Models\u003c\/b\u003e.\u003c\/p\u003e \u003cp\u003e14. FINLIB: A Library of Financial Optimization Models.\u003c\/p\u003e \u003cp\u003eBibliography.\u003c\/p\u003e \u003cp\u003eIndex\u003c\/p\u003e","brand":"John Wiley and Sons Ltd","offers":[{"title":"Default Title","offer_id":49407869354327,"sku":"9781405132008","price":52.5,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781405132008.jpg?v=1730500797"},{"product_id":"practical-financial-optimization-9781405132015","title":"Practical Financial Optimization","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eThis book gives a comprehensive account of financial optimization models used to support decision-making for financial engineers. It starts with the classical static mean-variance analysis and portfolio immunization, moves on to scenario-based models, and builds towards multi-period dynamic portfolio optimization.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e\"This volume is both a comprehensive guide to optimization techniques useful in financial decision making and a well-illustrated essay on the relationship between theory and practice. While the real problem may always be more complex than any model of it we build, that does not necessarily imply that the largest, most complex model will serve us best. Zenios supplies the reader with a spectrum of optimization models, from simple to complex, and sage advice on how to use them.\"From the Foreword by Harry M. Markowitz, Nobel Laureate in Economics  \"Most books on portfolio optimization focus on continuous time stochastic control models. By contrast, Zenios's decision to focus on mathematical programming models in financial engineering is an auspicious one. The book is well organized and clearly written, and uses a minimum of technical prerequisites (both mathematical and financial). It should therefore be accessible and of interest to a broad audience: industry practitioners interested in the potential application of optimization to the problems they face, students curious about how optimization is applied in finance, and professional researchers who would like a comprehensive overview of the uses of mathematical programming in financial engineering.\"David Saunders, University of Waterloo\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003eForeword.  \u003cp\u003ePreface.\u003c\/p\u003e \u003cp\u003eAcknowledgements.\u003c\/p\u003e \u003cp\u003eList of Models.\u003c\/p\u003e \u003cp\u003eNotation.\u003c\/p\u003e \u003cp\u003eI. Introduction.\u003c\/p\u003e \u003cp\u003e1. An Optimization View of Financial Engineering.\u003c\/p\u003e \u003cp\u003e2. Basics of Risk Management.\u003c\/p\u003e \u003cp\u003eII. Portfolio Optimization Models.\u003c\/p\u003e \u003cp\u003e3. Mean-Variance Analysis.\u003c\/p\u003e \u003cp\u003e4. Portfolio Models for Fixed Income.\u003c\/p\u003e \u003cp\u003e5. Scenario Optimization.\u003c\/p\u003e \u003cp\u003e6. Dynamic Portfolio Optimization with Stochastic Programming.\u003c\/p\u003e \u003cp\u003e7. Index Funds.\u003c\/p\u003e \u003cp\u003e8. Designing Financial Products.\u003c\/p\u003e \u003cp\u003e9. Scenario Generation.\u003c\/p\u003e \u003cp\u003eIII. Applications.\u003c\/p\u003e \u003cp\u003e10. Application I: International Asset Allocation.\u003c\/p\u003e \u003cp\u003e11. Application II: Corporate Bond Portfolios.\u003c\/p\u003e \u003cp\u003e12. Application III: Insurance Policies with Guarantees.\u003c\/p\u003e \u003cp\u003e13. Application IV: Personal Financial Planning.\u003c\/p\u003e \u003cp\u003eIV. Library of Financial Optimization Models.\u003c\/p\u003e \u003cp\u003e14. FINLIB: A Library of Financial Optimization Models\u003c\/p\u003e \u003cp\u003eA. Basics of Optimization.\u003c\/p\u003e \u003cp\u003eB. Basics of Probability Theory.\u003c\/p\u003e \u003cp\u003eC. Stochastic Processes.\u003c\/p\u003e \u003cp\u003eBibliography.\u003c\/p\u003e \u003cp\u003eIndex.\u003c\/p\u003e","brand":"John Wiley and Sons Ltd","offers":[{"title":"Default Title","offer_id":49407869387095,"sku":"9781405132015","price":34.67,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781405132015.jpg?v=1730500797"},{"product_id":"topology-optimization-design-of-heterogeneous-materials-and-structures-9781786305589","title":"Topology Optimization Design of Heterogeneous","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eThis book pursues optimal design from the perspective of mechanical properties and resistance to failure caused by cracks and fatigue. The book abandons the scale separation hypothesis and takes up phase-field modeling, which is at the cutting edge of research and is of high industrial and practical relevance.  Part 1 starts by testing the limits of the homogenization-based approach when the size of the representative volume element is non-negligible compared to the structure. The book then introduces a non-local homogenization scheme to take into account the strain gradient effects. Using a phase field method, Part 2 offers three significant contributions concerning optimal placement of the inclusion phases. Respectively, these contributions take into account fractures in quasi-brittle materials, interface cracks and periodic composites.  The topology optimization proposed has significantly increased the fracture resistance of the composites studied.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eIntroduction ix\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart 1. Multiscale Topology Optimization in the Context of Non-separated Scales \u003c\/b\u003e\u003cb\u003e1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 1. Size Effect Analysis in Topology Optimization for Periodic Structures Using the Classical Homogenization \u003c\/b\u003e\u003cb\u003e3\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1. The classical homogenization method 4\u003c\/p\u003e \u003cp\u003e1.1.1. Localization problem 4\u003c\/p\u003e \u003cp\u003e1.1.2. Definition and computation of the effective material properties 7\u003c\/p\u003e \u003cp\u003e1.1.3. Numerical implementation for the local problem with PER 9\u003c\/p\u003e \u003cp\u003e1.2. Topology optimization model and procedure 10\u003c\/p\u003e \u003cp\u003e1.2.1. Optimization model and sensitivity number 10\u003c\/p\u003e \u003cp\u003e1.2.2. Finite element meshes and relocalization scheme 12\u003c\/p\u003e \u003cp\u003e1.2.3. Optimization procedure 14\u003c\/p\u003e \u003cp\u003e1.3. Numerical examples 16\u003c\/p\u003e \u003cp\u003e1.3.1. Doubly clamped elastic domain 17\u003c\/p\u003e \u003cp\u003e1.3.2. L-shaped structure 19\u003c\/p\u003e \u003cp\u003e1.3.3. MBB beam 24\u003c\/p\u003e \u003cp\u003e1.4. Concluding remarks 25\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 2. Multiscale Topology Optimization of Periodic Structures Taking into Account Strain Gradient \u003c\/b\u003e\u003cb\u003e29\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1. Non-local filter-based homogenization for non-separated scales 30\u003c\/p\u003e \u003cp\u003e2.1.1. Definition of local and mesoscopic fields through the filter 30\u003c\/p\u003e \u003cp\u003e2.1.2. Microscopic unit cell calculations 33\u003c\/p\u003e \u003cp\u003e2.1.3. Mesoscopic structure calculations 39\u003c\/p\u003e \u003cp\u003e2.2. Topology optimization procedure 41\u003c\/p\u003e \u003cp\u003e2.2.1. Model definition and sensitivity numbers 41\u003c\/p\u003e \u003cp\u003e2.2.2. Overall optimization procedure 42\u003c\/p\u003e \u003cp\u003e2.3. Validation of the non-local homogenization approach 43\u003c\/p\u003e \u003cp\u003e2.4. Numerical examples 45\u003c\/p\u003e \u003cp\u003e2.4.1. Cantilever beam with a concentrated load 46\u003c\/p\u003e \u003cp\u003e2.4.2. Four-point bending lattice structure 52\u003c\/p\u003e \u003cp\u003e2.5. Concluding remarks 55\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 3. Topology Optimization of Meso-structures with Fixed Periodic Microstructures \u003c\/b\u003e\u003cb\u003e57\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1. Optimization model and procedure 58\u003c\/p\u003e \u003cp\u003e3.2. Numerical examples 61\u003c\/p\u003e \u003cp\u003e3.2.1. A double-clamped beam 61\u003c\/p\u003e \u003cp\u003e3.2.2. A cantilever beam 64\u003c\/p\u003e \u003cp\u003e3.3. Concluding remarks 66\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart 2. Topology Optimization for Maximizing the Fracture Resistance \u003c\/b\u003e\u003cb\u003e67\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 4. Topology Optimization for Optimal Fracture Resistance of Quasi-brittle Composites \u003c\/b\u003e\u003cb\u003e69\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1. Phase field modeling of crack propagation 71\u003c\/p\u003e \u003cp\u003e4.1.1. Phase field approximation of cracks 71\u003c\/p\u003e \u003cp\u003e4.1.2. Thermodynamics of the phase field crack evolution 72\u003c\/p\u003e \u003cp\u003e4.1.3. Weak forms of displacement and phase field problems 75\u003c\/p\u003e \u003cp\u003e4.1.4. Finite element discretization 76\u003c\/p\u003e \u003cp\u003e4.2. Topology optimization model for fracture resistance 78\u003c\/p\u003e \u003cp\u003e4.2.1. Model definitions 78\u003c\/p\u003e \u003cp\u003e4.2.2. Sensitivity analysis 80\u003c\/p\u003e \u003cp\u003e4.2.3. Extended BESO method 85\u003c\/p\u003e \u003cp\u003e4.3. Numerical examples 87\u003c\/p\u003e \u003cp\u003e4.3.1. Design of a 2D reinforced plate with one pre-existing crack notch 88\u003c\/p\u003e \u003cp\u003e4.3.2. Design of a 2D reinforced plate with two pre-existing crack notches 93\u003c\/p\u003e \u003cp\u003e4.3.3. Design of a 2D reinforced plate with multiple pre-existing cracks 96\u003c\/p\u003e \u003cp\u003e4.3.4. Design of a 3D reinforced plate with a single pre-existing crack notch surface 98\u003c\/p\u003e \u003cp\u003e4.4. Concluding remarks 101\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 5. Topology Optimization for Optimal Fracture Resistance Taking into Account Interfacial Damage \u003c\/b\u003e\u003cb\u003e103\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1. Phase field modeling of bulk crack and cohesive interfaces 104\u003c\/p\u003e \u003cp\u003e5.1.1. Regularized representation of a discontinuous field 104\u003c\/p\u003e \u003cp\u003e5.1.2. Energy functional 106\u003c\/p\u003e \u003cp\u003e5.1.3. Displacement and phase field problems 108\u003c\/p\u003e \u003cp\u003e5.1.4. Finite element discretization and numerical implementation 111\u003c\/p\u003e \u003cp\u003e5.2. Topology optimization method 114\u003c\/p\u003e \u003cp\u003e5.2.1. Model definitions 114\u003c\/p\u003e \u003cp\u003e5.2.2. Sensitivity analysis 116\u003c\/p\u003e \u003cp\u003e5.3. Numerical examples 119\u003c\/p\u003e \u003cp\u003e5.3.1. Design of a plate with one initial crack under traction 120\u003c\/p\u003e \u003cp\u003e5.3.2. Design of a plate without initial cracks for traction loads 123\u003c\/p\u003e \u003cp\u003e5.3.3. Design of a square plate without initial cracks in tensile loading 125\u003c\/p\u003e \u003cp\u003e5.3.4. Design of a plate with a single initial crack under three-point bending 128\u003c\/p\u003e \u003cp\u003e5.3.5. Design of a plate containing multiple inclusions 130\u003c\/p\u003e \u003cp\u003e5.4. Concluding remarks 133\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 6. Topology Optimization for Maximizing the Fracture Resistance of Periodic Composites \u003c\/b\u003e\u003cb\u003e135\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1. Topology optimization model 136\u003c\/p\u003e \u003cp\u003e6.2. Numerical examples 138\u003c\/p\u003e \u003cp\u003e6.2.1. Design of a periodic composite under three-point bending 138\u003c\/p\u003e \u003cp\u003e6.2.2. Design of a periodic composite under non-symmetric three-point bending 146\u003c\/p\u003e \u003cp\u003e6.3. Concluding remarks 151\u003c\/p\u003e \u003cp\u003eConclusion 153\u003c\/p\u003e \u003cp\u003eReferences 157\u003c\/p\u003e \u003cp\u003eIndex 173\u003c\/p\u003e","brand":"ISTE Ltd and John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":49412284252503,"sku":"9781786305589","price":125.06,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781786305589.jpg?v=1730516260"},{"product_id":"applications-of-combinatorial-optimization-volume-3-9781848211490","title":"Applications of Combinatorial Optimization,","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eCombinatorial optimization is a multidisciplinary scientific area, lying in the interface of three major scientific domains: mathematics, theoretical computer science and management.  \u003cp\u003eThe three volumes of the Combinatorial Optimization series aims to cover a wide range of topics in this area. These topics also deal with fundamental notions and approaches as with several classical applications of combinatorial optimization.\u003c\/p\u003e \u003cp\u003e“Applications of Combinatorial Optimization” is presenting a certain number among the most common and well-known applications of Combinatorial Optimization.\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e\u003cb\u003ePreface xiii\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 1. Airline Crew Pairing Optimization 1\u003c\/b\u003e\u003cbr\u003e \u003ci\u003eLaurent ALFANDARI and Anass NAGIH\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1.1. Introduction 1\u003c\/p\u003e \u003cp\u003e1.2. Definition of the problem 2\u003c\/p\u003e \u003cp\u003e1.3. Solution approaches 7\u003c\/p\u003e \u003cp\u003e1.4. Solving the subproblem for column generation 11\u003c\/p\u003e \u003cp\u003e1.5. Conclusion 21\u003c\/p\u003e \u003cp\u003e1.6. Bibliography 22\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 2. The Task Allocation Problem 23\u003c\/b\u003e\u003cbr\u003e \u003ci\u003eMoaiz BEN DHAOU and Didier FAYARD\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e2.1. Presentation 24\u003c\/p\u003e \u003cp\u003e2.2. Definitions and modeling 24\u003c\/p\u003e \u003cp\u003e2.3. Review of the main works 29\u003c\/p\u003e \u003cp\u003e2.4. A little-studied model 38\u003c\/p\u003e \u003cp\u003e2.5. Conclusion 43\u003c\/p\u003e \u003cp\u003e2.6. Bibliography 43\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 3. A Comparison of Some Valid Inequality Generation Methods for General 0–1 Problems 49\u003c\/b\u003e\u003cbr\u003e \u003ci\u003ePierre BONAMI and Michel MINOUX\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e3.1. Introduction 49\u003c\/p\u003e \u003cp\u003e3.2. Presentation of the various techniques tested 53\u003c\/p\u003e \u003cp\u003e3.3. Computational results 67\u003c\/p\u003e \u003cp\u003e3.4. Bibliography 70\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 4. Production Planning 73\u003c\/b\u003e\u003cbr\u003e \u003ci\u003eNadia BRAUNER, Gerd FINKE and Maurice QUEYRANNE\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e4.1. Introduction 73\u003c\/p\u003e \u003cp\u003e4.2. Hierarchical planning 74\u003c\/p\u003e \u003cp\u003e4.3. Strategic planning and productive system design 75\u003c\/p\u003e \u003cp\u003e4.4. Tactical planning and inventory management 77\u003c\/p\u003e \u003cp\u003e4.5. Operations planning and scheduling 90\u003c\/p\u003e \u003cp\u003e4.6. Conclusion and perspectives 104\u003c\/p\u003e \u003cp\u003e4.7. Bibliography 105\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 5. Operations Research and Goods Transportation 111\u003c\/b\u003e\u003cbr\u003e \u003ci\u003eTeodor Gabriel CRAINIC and Frédéric SEMET\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e5.1. Introduction 111\u003c\/p\u003e \u003cp\u003e5.2. Goods transport systems 113\u003c\/p\u003e \u003cp\u003e5.3. Systems design 115\u003c\/p\u003e \u003cp\u003e5.4. Long-distance transport 122\u003c\/p\u003e \u003cp\u003e5.5. Vehicle routing problems 137\u003c\/p\u003e \u003cp\u003e5.6. Exact models and methods for the VRP 139\u003c\/p\u003e \u003cp\u003e5.7. Heuristic methods for the VRP 147\u003c\/p\u003e \u003cp\u003e5.8. Conclusion 160\u003c\/p\u003e \u003cp\u003e5.9. Appendix: metaheuristics 161\u003c\/p\u003e \u003cp\u003e5.10. Bibliography 164\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 6. Optimization Models for Transportation Systems Planning 177\u003c\/b\u003e\u003cbr\u003e \u003ci\u003eTeodor Gabriel CRAINIC and Michael FLORIAN\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6.1. Introduction 177\u003c\/p\u003e \u003cp\u003e6.2. Spatial interaction models 178\u003c\/p\u003e \u003cp\u003e6.3. Traffic assignment models and methods 181\u003c\/p\u003e \u003cp\u003e6.4. Transit route choice models 193\u003c\/p\u003e \u003cp\u003e6.5. Strategic planning of multimodal systems 197\u003c\/p\u003e \u003cp\u003e6.6. Conclusion 204\u003c\/p\u003e \u003cp\u003e6.7. Bibliography 204\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 7. A Model for the Design of a Minimum-cost Telecommunications Network 209\u003c\/b\u003e\u003cbr\u003e \u003ci\u003eMarc DEMANGE, Cécile MURAT, Vangelis Th. PASCHOS and Sophie TOULOUSE\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e7.1. Introduction 209\u003c\/p\u003e \u003cp\u003e7.2. Minimum cost network construction 210\u003c\/p\u003e \u003cp\u003e7.3. Mathematical model, general context 213\u003c\/p\u003e \u003cp\u003e7.4. Proposed algorithm 216\u003c\/p\u003e \u003cp\u003e7.5. Critical points 220\u003c\/p\u003e \u003cp\u003e7.6. Conclusion 223\u003c\/p\u003e \u003cp\u003e7.7. Bibliography 223\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 8. Parallel Combinatorial Optimization 225\u003c\/b\u003e\u003cbr\u003e \u003ci\u003eVan-Dat CUNG, Bertrand LE CUN and Catherine ROUCAIROL\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e8.1. Impact of parallelism in combinatorial optimization 225\u003c\/p\u003e \u003cp\u003e8.2. Parallel metaheuristics 226\u003c\/p\u003e \u003cp\u003e8.3. Parallelizing tree exploration in exact methods 235\u003c\/p\u003e \u003cp\u003e8.4. Conclusion 247\u003c\/p\u003e \u003cp\u003e8.5. Bibliography 248\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 9. Network Design Problems: Fundamental Methods 253\u003c\/b\u003e\u003cbr\u003e \u003ci\u003eAlain Quilliot\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e9.1. Introduction 253\u003c\/p\u003e \u003cp\u003e9.2. The main mathematical and algorithmic tools for network design 258\u003c\/p\u003e \u003cp\u003e9.3. Models and problems 275\u003c\/p\u003e \u003cp\u003e9.4. The STEINER-EXTENDED problem 280\u003c\/p\u003e \u003cp\u003e9.5. Conclusion 281\u003c\/p\u003e \u003cp\u003e9.6 Bibliography 281\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 10. Network Design Problems: Models and Applications 291\u003c\/b\u003e\u003cbr\u003e \u003ci\u003eAlain Quilliot\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e10.1. Introduction 291\u003c\/p\u003e \u003cp\u003e10.2. Models and location problems 293\u003c\/p\u003e \u003cp\u003e10.3. Routing models for telecommunications 298\u003c\/p\u003e \u003cp\u003e10.4. The design or dimensioning problem in telecommunications 301\u003c\/p\u003e \u003cp\u003e10.5. Coupled flows and multiflows for transport and production 306\u003c\/p\u003e \u003cp\u003e10.6. A mixed network pricing model 314\u003c\/p\u003e \u003cp\u003e10.7. Conclusion 319\u003c\/p\u003e \u003cp\u003e10.8. Bibliography 319\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 11. Multicriteria Task Allocation to Heterogenous Processors with Capacity and Mutual Exclusion Constraints 327\u003c\/b\u003e\u003cbr\u003e \u003ci\u003eBernard ROY and Roman SLOWINSKI\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e11.1. Introduction and formulation of the problem 328\u003c\/p\u003e \u003cp\u003e11.2. Modeling the set of feasible assignments 331\u003c\/p\u003e \u003cp\u003e11.3. The concept of a blocking configuration and analysis of the unblocking means 334\u003c\/p\u003e \u003cp\u003e11.4. The multicriteria assignment problem 346\u003c\/p\u003e \u003cp\u003e11.5. Exploring a set of feasible non-dominated assignments in the plane g2 × g3 348\u003c\/p\u003e \u003cp\u003e11.6. Numerical example 357\u003c\/p\u003e \u003cp\u003e11.7. Conclusion 363\u003c\/p\u003e \u003cp\u003e11.8. Bibliography 364\u003c\/p\u003e \u003cp\u003eList of Authors 365\u003c\/p\u003e \u003cp\u003eIndex 369\u003c\/p\u003e \u003cp\u003eSummary of Other Volumes in the Series 373\u003c\/p\u003e","brand":"ISTE Ltd and John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":49413706744151,"sku":"9781848211490","price":142.16,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781848211490.jpg?v=1730521113"},{"product_id":"concepts-of-combinatorial-optimization-volume-1-9781848211476","title":"Concepts of Combinatorial Optimization, Volume 1","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eCombinatorial optimization is a multidisciplinary scientific area, lying in the interface of three major scientific domains: mathematics, theoretical computer science and management.  \u003cp\u003eThe three volumes of the \u003cb\u003eCombinatorial Optimization series\u003c\/b\u003e aims to cover a wide range of topics in this area. These topics also deal with fundamental notions and approaches as with several classical applications of combinatorial optimization.\u003c\/p\u003e \u003cp\u003e\u003ci\u003eConcepts of Combinatorial Optimization\u003c\/i\u003e, is divided into three parts:\u003c\/p\u003e \u003cul\u003e\n\u003cli\u003eOn the complexity of combinatorial optimization problems, that presents basics about worst-case and randomized complexity;\u003c\/li\u003e\n\u003cli\u003eClassical solution methods, that presents the two most-known methods for solving hard combinatorial optimization problems, that are Branch-and-Bound and Dynamic Programming;\u003c\/li\u003e\n\u003cli\u003eElements from mathematical programming, that presents fundamentals from mathematical programming based methods that are in the heart of Operations Research since the origins of this field.\u003c\/li\u003e\n\u003c\/ul\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003ePreface xiii\u003cbr\u003e Vangelis Th. PASCHOS\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePART I. COMPLEXITY OF COMBINATORIAL OPTIMIZATION PROBLEMS 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 1. Basic Concepts in Algorithms and Complexity Theory 3\u003c\/b\u003e\u003cbr\u003e Vangelis Th. PASCHOS\u003c\/p\u003e \u003cp\u003e1.1. Algorithmic complexity 3\u003c\/p\u003e \u003cp\u003e1.2. Problem complexity 4\u003c\/p\u003e \u003cp\u003e1.3. The classes P, NP and NPO 7\u003c\/p\u003e \u003cp\u003e1.4. Karp and Turing reductions 9\u003c\/p\u003e \u003cp\u003e1.5. NP-completeness 10\u003c\/p\u003e \u003cp\u003e1.6. Two examples of NP-complete problems 13\u003c\/p\u003e \u003cp\u003e1.7. A few words on strong and weak NP-completeness 16\u003c\/p\u003e \u003cp\u003e1.8. A few other well-known complexity classes 17\u003c\/p\u003e \u003cp\u003e1.9. Bibliography 18\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 2. Randomized Complexity 21\u003c\/b\u003e\u003cbr\u003e Jérémy BARBAY\u003c\/p\u003e \u003cp\u003e2.1. Deterministic and probabilistic algorithms 22\u003c\/p\u003e \u003cp\u003e2.2. Lower bound technique 28\u003c\/p\u003e \u003cp\u003e2.3. Elementary intersection problem 35\u003c\/p\u003e \u003cp\u003e2.4. Conclusion 37\u003c\/p\u003e \u003cp\u003e2.5 Bibliography 37\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePART II. CLASSICAL SOLUTION METHODS 39\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 3. Branch-and-Bound Methods 41\u003c\/b\u003e\u003cbr\u003e Irène CHARON and Olivier HUDRY\u003c\/p\u003e \u003cp\u003e3.1. Introduction 41\u003c\/p\u003e \u003cp\u003e3.2. Branch-and-bound method principles 43\u003c\/p\u003e \u003cp\u003e3.3. A detailed example: the binary knapsack problem 54\u003c\/p\u003e \u003cp\u003e3.4. Conclusion 67\u003c\/p\u003e \u003cp\u003e3.5. Bibliography 68\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 4. Dynamic Programming 71\u003c\/b\u003e\u003cbr\u003e Bruno ESCOFFIER and Olivier SPANJAARD\u003c\/p\u003e \u003cp\u003e4.1. Introduction 71\u003c\/p\u003e \u003cp\u003e4.2. A first example: crossing the bridge 72\u003c\/p\u003e \u003cp\u003e4.3. Formalization 75\u003c\/p\u003e \u003cp\u003e4.4. Some other examples 79\u003c\/p\u003e \u003cp\u003e4.5. Solution 83\u003c\/p\u003e \u003cp\u003e4.6. Solution of the examples 88\u003c\/p\u003e \u003cp\u003e4.7. A few extensions 90\u003c\/p\u003e \u003cp\u003e4.8. Conclusion 98\u003c\/p\u003e \u003cp\u003e4.9. Bibliography 98\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePART III. ELEMENTS FROM MATHEMATICAL PROGRAMMING 101\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 5. Mixed Integer Linear Programming Models for Combinatorial Optimization Problems 103\u003c\/b\u003e\u003cbr\u003e Frédérico DELLA CROCE\u003c\/p\u003e \u003cp\u003e5.1. Introduction 103\u003c\/p\u003e \u003cp\u003e5.2. General modeling techniques 111\u003c\/p\u003e \u003cp\u003e5.3. More advanced MILP models 117\u003c\/p\u003e \u003cp\u003e5.4. Conclusions 132\u003c\/p\u003e \u003cp\u003e5.5. Bibliography 133\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 6. Simplex Algorithms for Linear Programming 135\u003c\/b\u003e\u003cbr\u003e Frédérico DELLA CROCE and Andrea GROSSO\u003c\/p\u003e \u003cp\u003e6.1. Introduction 135\u003c\/p\u003e \u003cp\u003e6.2. Primal and dual programs 135\u003c\/p\u003e \u003cp\u003e6.3. The primal simplex method 140\u003c\/p\u003e \u003cp\u003e6.4. Bland’s rule 145\u003c\/p\u003e \u003cp\u003e6.5. Simplex methods for the dual problem 147\u003c\/p\u003e \u003cp\u003e6.6. Using reduced costs and pseudo-costs for integer programming 152\u003c\/p\u003e \u003cp\u003e6.7. Bibliography 155\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 7. A Survey of some Linear Programming Methods 157\u003c\/b\u003e\u003cbr\u003e Pierre TOLLA\u003c\/p\u003e \u003cp\u003e7.1. Introduction 157\u003c\/p\u003e \u003cp\u003e7.2. Dantzig’s simplex method 158\u003c\/p\u003e \u003cp\u003e7.3. Duality 162\u003c\/p\u003e \u003cp\u003e7.4. Khachiyan’s algorithm 162\u003c\/p\u003e \u003cp\u003e7.5. Interior methods 165\u003c\/p\u003e \u003cp\u003e7.6. Conclusion 186\u003c\/p\u003e \u003cp\u003e7.7. Bibliography 187\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 8. Quadratic Optimization in 0–1 Variables 189\u003c\/b\u003e\u003cbr\u003e Alain BILLIONNET\u003c\/p\u003e \u003cp\u003e8.1. Introduction 189\u003c\/p\u003e \u003cp\u003e8.2. Pseudo-Boolean functions and set functions 190\u003c\/p\u003e \u003cp\u003e8.3. Formalization using pseudo-Boolean functions 191\u003c\/p\u003e \u003cp\u003e8.4. Quadratic pseudo-Boolean functions (qpBf) 192\u003c\/p\u003e \u003cp\u003e8.5. Integer optimum and continuous optimum of qpBfs 194\u003c\/p\u003e \u003cp\u003e8.6. Derandomization 195\u003c\/p\u003e \u003cp\u003e8.7. Posiforms and quadratic posiforms 196\u003c\/p\u003e \u003cp\u003e8.8. Optimizing a qpBf: special cases and polynomial cases 198\u003c\/p\u003e \u003cp\u003e8.9. Reductions, relaxations, linearizations, bound calculation and persistence 200\u003c\/p\u003e \u003cp\u003e8.10. Local optimum 206\u003c\/p\u003e \u003cp\u003e8.11. Exact algorithms and heuristic methods for optimizing qpBfs 208\u003c\/p\u003e \u003cp\u003e8.12. Approximation algorithms 211\u003c\/p\u003e \u003cp\u003e8.13. Optimizing a quadratic pseudo-Boolean function with linear constraints 213\u003c\/p\u003e \u003cp\u003e8.14. Linearization, convexification and Lagrangian relaxation for optimizing a qpBf with linear constraints 220\u003c\/p\u003e \u003cp\u003e8.15. -Approximation algorithms for optimizing a qpBf with linear constraints 223\u003c\/p\u003e \u003cp\u003e8.16. Bibliography 224\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 9. Column Generation in Integer Linear Programming 235\u003c\/b\u003e\u003cbr\u003e Irène LOISEAU, Alberto CESELLI, Nelson MACULAN and Matteo SALANI\u003c\/p\u003e \u003cp\u003e9.1. Introduction 235\u003c\/p\u003e \u003cp\u003e9.2. A column generation method for a bounded variable linear programming problem 236\u003c\/p\u003e \u003cp\u003e9.3. An inequality to eliminate the generation of a 0–1 column 238\u003c\/p\u003e \u003cp\u003e9.4. Formulations for an integer linear program 240\u003c\/p\u003e \u003cp\u003e9.5. Solving an integer linear program using column generation 243\u003c\/p\u003e \u003cp\u003e9.6. Applications 247\u003c\/p\u003e \u003cp\u003e9.7. Bibliography 255\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 10. Polyhedral Approaches 261\u003c\/b\u003e\u003cbr\u003e Ali Ridha MAHJOUB\u003c\/p\u003e \u003cp\u003e10.1. Introduction 261\u003c\/p\u003e \u003cp\u003e10.2. Polyhedra, faces and facets 265\u003c\/p\u003e \u003cp\u003e10.3. Combinatorial optimization and linear programming 276\u003c\/p\u003e \u003cp\u003e10.4. Proof techniques 282\u003c\/p\u003e \u003cp\u003e10.5. Integer polyhedra and min–max relations 293\u003c\/p\u003e \u003cp\u003e10.6. Cutting-plane method 301\u003c\/p\u003e \u003cp\u003e10.7. The maximum cut problem 308\u003c\/p\u003e \u003cp\u003e10.8. The survivable network design problem 313\u003c\/p\u003e \u003cp\u003e10.9. Conclusion 319\u003c\/p\u003e \u003cp\u003e10.10. Bibliography 320\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 11. Constraint Programming 325\u003c\/b\u003e\u003cbr\u003e Claude LE PAPE\u003c\/p\u003e \u003cp\u003e11.1. Introduction 325\u003c\/p\u003e \u003cp\u003e11.2. Problem definition 327\u003c\/p\u003e \u003cp\u003e11.3. Decision operators 328\u003c\/p\u003e \u003cp\u003e11.4. Propagation 330\u003c\/p\u003e \u003cp\u003e11.5. Heuristics 333\u003c\/p\u003e \u003cp\u003e11.6. Conclusion 336\u003c\/p\u003e \u003cp\u003e11.7. Bibliography 336\u003c\/p\u003e \u003cp\u003eList of Authors 339\u003c\/p\u003e \u003cp\u003eIndex 343\u003c\/p\u003e \u003cp\u003eSummary of Other Volumes in the Series 347\u003c\/p\u003e","brand":"ISTE Ltd and John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":49413706875223,"sku":"9781848211476","price":150.05,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781848211476.jpg?v=1730521112"},{"product_id":"progress-in-combinatorial-optimization-recent-progress-9781848212060","title":"Progress in Combinatorial Optimization: Recent","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eThis book presents recent developments and new trends in Combinatorial Optimization. Combinatorial Optimization is an active research area that has applications in many domains such as communications, network design, VLSI, scheduling, production, computational biology. In the past years, new results and major advances have been seen in many areas including computational complexity, approximation algorithms, cutting-plane based methods and submodularity function minimization. More efficient and powerful methods have been developed for approaching real-worlds problems, and new concepts and theoritical results have been introduced.","brand":"ISTE Ltd and John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":49413708251479,"sku":"9781848212060","price":223.2,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781848212060.jpg?v=1730521116"},{"product_id":"concepts-of-combinatorial-optimization-9781848216563","title":"Concepts of Combinatorial Optimization","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eCombinatorial optimization is a multidisciplinary scientific area, lying in the interface of three major scientific domains: mathematics, theoretical computer science and management.  The three volumes of the Combinatorial Optimization series aim to cover a wide range  of topics in this area. These topics also deal with fundamental notions and approaches as with several classical applications of combinatorial optimization.\u003cbr\u003e \u003cbr\u003e \u003ci\u003eConcepts of Combinatorial Optimization\u003c\/i\u003e, is divided into three parts:\u003cbr\u003e - On the complexity of combinatorial optimization problems, presenting basics about worst-case and randomized complexity;\u003cbr\u003e - Classical solution methods, presenting the two most-known methods for solving hard combinatorial optimization problems, that are Branch-and-Bound and Dynamic Programming;\u003cbr\u003e - Elements from mathematical programming, presenting fundamentals from mathematical programming based methods that are in the heart of Operations Research since the origins of this field.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003ePreface xiii  \u003cp\u003eVangelis Th. Paschos\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart I Complexity of Combinatioral Optimization Problems 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eChapter 1 Basic Concepts in Algorithms and Complexity Theory 3\u003cbr\u003e \u003ci\u003eVangelis Th. Paschos\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eChapter 2 Randomized Complexity 21\u003cbr\u003e \u003ci\u003eJérémy Barbay\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart II Classic Solution Methods 39\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eChapter 3 Branch-and-Bound Methods 41\u003cbr\u003e \u003ci\u003eIrène Charon and Olivier Hudry\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eChapter 4 Dynamic Programming 71\u003cbr\u003e \u003ci\u003eBruno Escoffier and Olivier Spanjaard\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart III Elements from Mathematical Programming 101\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eChapter 5 Mixed Integer Linear Programming Models for Combinatorial Optimization Problems 103\u003cbr\u003e \u003ci\u003eFrédérico Della Croce\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eChapter 6 Simplex Algorithms for Linear Programming 135\u003cbr\u003e \u003ci\u003eFrédérico Della Croce and Andrea Grosso\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eChapter 7 A Survey of Some Linear Programming Methods 157\u003cbr\u003e \u003ci\u003ePierre Tolla\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eChapter 8 Quadratic Optimization in 0-1 Variables 189\u003cbr\u003e \u003ci\u003eAlain Billionnet\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eChapter 9 Column Generation in Integar Linear Programming 235\u003cbr\u003e \u003ci\u003eIrène Loiseau, Alberto Ceselli, Nelson Maculan and Matteo Salani\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eChapter 10 Polyhedral Approaches 261\u003cbr\u003e \u003ci\u003eAli Ridha Mahjoub\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eChapter 11 Constaint Programming 325\u003cbr\u003e \u003ci\u003eClaude Le Pape\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eGeneral Bibliography 339\u003c\/p\u003e \u003cp\u003eList of Authors 363\u003c\/p\u003e \u003cp\u003eIndex 367\u003c\/p\u003e \u003cp\u003eSummary of Other Volumes in the Series 371\u003c\/p\u003e","brand":"ISTE Ltd and John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":49413716541783,"sku":"9781848216563","price":132.26,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781848216563.jpg?v=1730521148"}],"url":"https:\/\/bookcurl.com\/collections\/optimization.oembed?page=6","provider":"Book Curl","version":"1.0","type":"link"}