Optimization Books
now publishers Inc Convex Optimization for Machine Learning
Book SynopsisThis book covers an introduction to convex optimization, one of the powerful and tractable optimization problems that can be efficiently solved on a computer. The goal of the book is tohelp develop a sense of what convex optimization is, and how it can be used in a widening array of practical contexts with a particular emphasis on machine learning.The first part of the book covers core concepts of convex sets, convex functions, and related basic definitions that serve understanding convex optimization and its corresponding models. The second part deals with one very useful theory, called duality, which enables us to: (1) gain algorithmic insights; and (2) obtain an approximate solution to non-convex optimization problems which are often difficult to solve. The last part focuses on modern applications in machine learning and deep learning.A defining feature of this book is that it succinctly relates the “story” of how convex optimization plays a role, via historical examples and trending machine learning applications. Another key feature is that it includes programming implementation of a variety of machine learning algorithms inspired by optimization fundamentals, together with a brief tutorial of the used programming tools. The implementation is based on Python, CVXPY, and TensorFlow. This book does not follow a traditional textbook-style organization, but is streamlined via a series of lecture notes that are intimately related, centered around coherent themes and concepts. It serves as a textbook mainly for a senior-level undergraduate course, yet is also suitable for a first-year graduate course. Readers benefit from having a good background in linear algebra, some exposure to probability, and basic familiarity with Python.Trade ReviewThe topic is surely still of great interest, since courses on Convex Optimization, in conjunction or not with Machine Learning applications, are ubiquitous in Engineering curricula around the world. What appears as somewhat novel here is the juxtaposition of Part I and II on convex optimization and duality with Part III on machine learning applications. The emphasis on Python, TensorFlow etc. is also practically very important and surely appreciated by the students, especially if presented via challenging practical problems. More than completeness, I believe that what is important is that the book gives a meaningful “cut” through these topics, as this books appears to do. It seems important that the author tries to motivate and link together as much as possible part III with the previous parts, explaining why part I and II are important for part III, but also highlighting what the limits of convex models are and at which point they need be superseded by more general models. Giuseppe Carlo Calafiore, Professor at the Politecnico di Torino, Italy, and visiting Professor at UC Berkeley -- Giuseppe Carlo CalafioreI have looked at the manuscript and my impression is positive, the aims and scope are actual and comprehensive. The intended audience is senior undergraduates and early graduate, which differs the book significantly from several competing books , and this should be an advantage. I would say that a good senior undergraduate level textbook on convex optimization would, in my opinion, be very timely. Arkadi Nemirovski, Georgia Tech, USA -- Arkadi NemirovskiTable of Contents Preface 1 Convex Optimization Basics 1.1 Overview of the book 1.2 Definition of convex optimization 1.3 Tractability of convex optimization and gradient descent 1.4 Linear Program 1.5 Least Squares 1.6 Test error, regularization and CVXPY implementation 1.7 Computed tomography 1.8 Quadratic program 1.9 Second-order cone program 1.10 Semi-definite program 1.11 SDP relaxation 1.12 Problem Sets 2 Duality 2.1 Strong duality 2.2 Interior point method 2.3 Proof of strong duality theorem 2.4 Weak duality 2.5 Lagrange relaxation for Boolean problems 2.6 Lagrange relaxation for the MAXCUT problem 2.7 Problem Sets 3 Machine Learning Applications 3.1 Supervised learning and optimization 3.2 Logistic regression 3.3 Deep learning 3.4 Deep learning II 3.5 DL: TensorFlow implementation 3.6 Unsupervised Learning: Generative modeling 3.7 Generative Adversarial Networks (GANs) 3.8 GANs: TensorFlow implementation 3.9 Wasserstein GAN 3.10 Wasserstein GAN II 3.11 Wasserstein GAN: TensorFlow implementation 3.12 Fair machine learning 3.13 A fair classifier and its connection to GANs 3.14 A fair classifier: TensorFlow implementation Appendices
£109.25
now publishers Inc A Modern Approach to Teaching an Introduction to Optimization
Book SynopsisOptimization should be the science of making the best possible decisions. Making decisions is a virtually universal human activity encountered by professionals (in any field) or people in their everyday lives. You would think, then, that the study of making good decisions is a subject that should be taught broadly to students throughout engineering, the physical and social sciences, business, and policy. Yet today, “optimization” is widely taught as a mathematically sophisticated subject, often limited to graduate students in specialized fields.In operations research (or industrial engineering), “optimization” is equivalent to deterministic math programming, starting with linear programs (and the simplex algorithm), and then transitioning through integer linear programs and nonlinear programs. If you are in departments like electrical or mechanical engineering, optimization means teaching optimal control. And if you are in computer science, optimization today could be interpreted in the context of machine learning (such as fitting models to data) or as reinforcement learning.This book claims that the traditional style of teaching optimization is misguided and out of date. First, while the simplex algorithm is a powerful strategy for solving linear programs, the details of the simplex algorithm are completely inappropriate in an introductory course in optimization. Second, while linear programs are appropriate for solving many problems, they are only applicable to a tiny fraction of all decisions. Third, linear programs (along with integer and nonlinear programs) are static models for problems with (typically) vector-valued decisions. By contrast, most decisions are sequential since they are made periodically over time as new information is arriving. In addition, the vast majority of these decisions are scalar (possibly continuous or discrete).This book is designed for instructors (or potential instructors) looking to introduce the science of making good decisions to the broadest possible audience. It should also be of interest to anyone who has already had a traditional course in optimization of any type. The presentation is organized around a series of topics that suggest a fundamentally different approach to teaching “optimization” spanning both sequential decision problems (which offer the simplest problem settings) before transitioning to more complex vector-valued decisions. It also makes the case that most problems which are modeled as linear (or integer, or nonlinear programs) are actually methods for making decisions in a sequential setting. For this reason, these topics are introduced with much less emphasis on algorithms than is traditionally used, both in static and sequential settings.
£57.00
ISTE Ltd and John Wiley & Sons Inc Topology Optimization Design of Heterogeneous
Book SynopsisThis 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.Table of ContentsIntroduction ix Part 1. Multiscale Topology Optimization in the Context of Non-separated Scales 1 Chapter 1. Size Effect Analysis in Topology Optimization for Periodic Structures Using the Classical Homogenization 3 1.1. The classical homogenization method 4 1.1.1. Localization problem 4 1.1.2. Definition and computation of the effective material properties 7 1.1.3. Numerical implementation for the local problem with PER 9 1.2. Topology optimization model and procedure 10 1.2.1. Optimization model and sensitivity number 10 1.2.2. Finite element meshes and relocalization scheme 12 1.2.3. Optimization procedure 14 1.3. Numerical examples 16 1.3.1. Doubly clamped elastic domain 17 1.3.2. L-shaped structure 19 1.3.3. MBB beam 24 1.4. Concluding remarks 25 Chapter 2. Multiscale Topology Optimization of Periodic Structures Taking into Account Strain Gradient 29 2.1. Non-local filter-based homogenization for non-separated scales 30 2.1.1. Definition of local and mesoscopic fields through the filter 30 2.1.2. Microscopic unit cell calculations 33 2.1.3. Mesoscopic structure calculations 39 2.2. Topology optimization procedure 41 2.2.1. Model definition and sensitivity numbers 41 2.2.2. Overall optimization procedure 42 2.3. Validation of the non-local homogenization approach 43 2.4. Numerical examples 45 2.4.1. Cantilever beam with a concentrated load 46 2.4.2. Four-point bending lattice structure 52 2.5. Concluding remarks 55 Chapter 3. Topology Optimization of Meso-structures with Fixed Periodic Microstructures 57 3.1. Optimization model and procedure 58 3.2. Numerical examples 61 3.2.1. A double-clamped beam 61 3.2.2. A cantilever beam 64 3.3. Concluding remarks 66 Part 2. Topology Optimization for Maximizing the Fracture Resistance 67 Chapter 4. Topology Optimization for Optimal Fracture Resistance of Quasi-brittle Composites 69 4.1. Phase field modeling of crack propagation 71 4.1.1. Phase field approximation of cracks 71 4.1.2. Thermodynamics of the phase field crack evolution 72 4.1.3. Weak forms of displacement and phase field problems 75 4.1.4. Finite element discretization 76 4.2. Topology optimization model for fracture resistance 78 4.2.1. Model definitions 78 4.2.2. Sensitivity analysis 80 4.2.3. Extended BESO method 85 4.3. Numerical examples 87 4.3.1. Design of a 2D reinforced plate with one pre-existing crack notch 88 4.3.2. Design of a 2D reinforced plate with two pre-existing crack notches 93 4.3.3. Design of a 2D reinforced plate with multiple pre-existing cracks 96 4.3.4. Design of a 3D reinforced plate with a single pre-existing crack notch surface 98 4.4. Concluding remarks 101 Chapter 5. Topology Optimization for Optimal Fracture Resistance Taking into Account Interfacial Damage 103 5.1. Phase field modeling of bulk crack and cohesive interfaces 104 5.1.1. Regularized representation of a discontinuous field 104 5.1.2. Energy functional 106 5.1.3. Displacement and phase field problems 108 5.1.4. Finite element discretization and numerical implementation 111 5.2. Topology optimization method 114 5.2.1. Model definitions 114 5.2.2. Sensitivity analysis 116 5.3. Numerical examples 119 5.3.1. Design of a plate with one initial crack under traction 120 5.3.2. Design of a plate without initial cracks for traction loads 123 5.3.3. Design of a square plate without initial cracks in tensile loading 125 5.3.4. Design of a plate with a single initial crack under three-point bending 128 5.3.5. Design of a plate containing multiple inclusions 130 5.4. Concluding remarks 133 Chapter 6. Topology Optimization for Maximizing the Fracture Resistance of Periodic Composites 135 6.1. Topology optimization model 136 6.2. Numerical examples 138 6.2.1. Design of a periodic composite under three-point bending 138 6.2.2. Design of a periodic composite under non-symmetric three-point bending 146 6.3. Concluding remarks 151 Conclusion 153 References 157 Index 173
£125.06
ISTE Ltd and John Wiley & Sons Inc Applications of Combinatorial Optimization,
Book SynopsisCombinatorial 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 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. “Applications of Combinatorial Optimization” is presenting a certain number among the most common and well-known applications of Combinatorial Optimization.Table of ContentsPreface xiii Chapter 1. Airline Crew Pairing Optimization 1 Laurent ALFANDARI and Anass NAGIH 1.1. Introduction 1 1.2. Definition of the problem 2 1.3. Solution approaches 7 1.4. Solving the subproblem for column generation 11 1.5. Conclusion 21 1.6. Bibliography 22 Chapter 2. The Task Allocation Problem 23 Moaiz BEN DHAOU and Didier FAYARD 2.1. Presentation 24 2.2. Definitions and modeling 24 2.3. Review of the main works 29 2.4. A little-studied model 38 2.5. Conclusion 43 2.6. Bibliography 43 Chapter 3. A Comparison of Some Valid Inequality Generation Methods for General 0–1 Problems 49 Pierre BONAMI and Michel MINOUX 3.1. Introduction 49 3.2. Presentation of the various techniques tested 53 3.3. Computational results 67 3.4. Bibliography 70 Chapter 4. Production Planning 73 Nadia BRAUNER, Gerd FINKE and Maurice QUEYRANNE 4.1. Introduction 73 4.2. Hierarchical planning 74 4.3. Strategic planning and productive system design 75 4.4. Tactical planning and inventory management 77 4.5. Operations planning and scheduling 90 4.6. Conclusion and perspectives 104 4.7. Bibliography 105 Chapter 5. Operations Research and Goods Transportation 111 Teodor Gabriel CRAINIC and Frédéric SEMET 5.1. Introduction 111 5.2. Goods transport systems 113 5.3. Systems design 115 5.4. Long-distance transport 122 5.5. Vehicle routing problems 137 5.6. Exact models and methods for the VRP 139 5.7. Heuristic methods for the VRP 147 5.8. Conclusion 160 5.9. Appendix: metaheuristics 161 5.10. Bibliography 164 Chapter 6. Optimization Models for Transportation Systems Planning 177 Teodor Gabriel CRAINIC and Michael FLORIAN 6.1. Introduction 177 6.2. Spatial interaction models 178 6.3. Traffic assignment models and methods 181 6.4. Transit route choice models 193 6.5. Strategic planning of multimodal systems 197 6.6. Conclusion 204 6.7. Bibliography 204 Chapter 7. A Model for the Design of a Minimum-cost Telecommunications Network 209 Marc DEMANGE, Cécile MURAT, Vangelis Th. PASCHOS and Sophie TOULOUSE 7.1. Introduction 209 7.2. Minimum cost network construction 210 7.3. Mathematical model, general context 213 7.4. Proposed algorithm 216 7.5. Critical points 220 7.6. Conclusion 223 7.7. Bibliography 223 Chapter 8. Parallel Combinatorial Optimization 225 Van-Dat CUNG, Bertrand LE CUN and Catherine ROUCAIROL 8.1. Impact of parallelism in combinatorial optimization 225 8.2. Parallel metaheuristics 226 8.3. Parallelizing tree exploration in exact methods 235 8.4. Conclusion 247 8.5. Bibliography 248 Chapter 9. Network Design Problems: Fundamental Methods 253 Alain Quilliot 9.1. Introduction 253 9.2. The main mathematical and algorithmic tools for network design 258 9.3. Models and problems 275 9.4. The STEINER-EXTENDED problem 280 9.5. Conclusion 281 9.6 Bibliography 281 Chapter 10. Network Design Problems: Models and Applications 291 Alain Quilliot 10.1. Introduction 291 10.2. Models and location problems 293 10.3. Routing models for telecommunications 298 10.4. The design or dimensioning problem in telecommunications 301 10.5. Coupled flows and multiflows for transport and production 306 10.6. A mixed network pricing model 314 10.7. Conclusion 319 10.8. Bibliography 319 Chapter 11. Multicriteria Task Allocation to Heterogenous Processors with Capacity and Mutual Exclusion Constraints 327 Bernard ROY and Roman SLOWINSKI 11.1. Introduction and formulation of the problem 328 11.2. Modeling the set of feasible assignments 331 11.3. The concept of a blocking configuration and analysis of the unblocking means 334 11.4. The multicriteria assignment problem 346 11.5. Exploring a set of feasible non-dominated assignments in the plane g2 × g3 348 11.6. Numerical example 357 11.7. Conclusion 363 11.8. Bibliography 364 List of Authors 365 Index 369 Summary of Other Volumes in the Series 373
£142.16
ISTE Ltd and John Wiley & Sons Inc Concepts of Combinatorial Optimization
Book SynopsisCombinatorial 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. Concepts of Combinatorial Optimization, is divided into three parts: - On the complexity of combinatorial optimization problems, presenting basics about worst-case and randomized complexity; - Classical solution methods, presenting the two most-known methods for solving hard combinatorial optimization problems, that are Branch-and-Bound and Dynamic Programming; - 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.Table of ContentsPreface xiii Vangelis Th. Paschos Part I Complexity of Combinatioral Optimization Problems 1 Chapter 1 Basic Concepts in Algorithms and Complexity Theory 3 Vangelis Th. Paschos Chapter 2 Randomized Complexity 21 Jérémy Barbay Part II Classic Solution Methods 39 Chapter 3 Branch-and-Bound Methods 41 Irène Charon and Olivier Hudry Chapter 4 Dynamic Programming 71 Bruno Escoffier and Olivier Spanjaard Part III Elements from Mathematical Programming 101 Chapter 5 Mixed Integer Linear Programming Models for Combinatorial Optimization Problems 103 Frédérico Della Croce Chapter 6 Simplex Algorithms for Linear Programming 135 Frédérico Della Croce and Andrea Grosso Chapter 7 A Survey of Some Linear Programming Methods 157 Pierre Tolla Chapter 8 Quadratic Optimization in 0-1 Variables 189 Alain Billionnet Chapter 9 Column Generation in Integar Linear Programming 235 Irène Loiseau, Alberto Ceselli, Nelson Maculan and Matteo Salani Chapter 10 Polyhedral Approaches 261 Ali Ridha Mahjoub Chapter 11 Constaint Programming 325 Claude Le Pape General Bibliography 339 List of Authors 363 Index 367 Summary of Other Volumes in the Series 371
£132.26
ISTE Ltd and John Wiley & Sons Inc Applications of Combinatorial Optimization
Book SynopsisCombinatorial 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. Concepts of Combinatorial Optimization, is divided into three parts: - On the complexity of combinatorial optimization problems, presenting basics about worst-case and randomized complexity; - Classical solution methods, presenting the two most-known methods for solving hard combinatorial optimization problems, that are Branch-and-Bound and Dynamic Programming; - 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.Table of ContentsPreface xiii Chapter 1 Airline Crew Pairing Optimization 1 Laurent Alfandari and Anass Nagih Chapter 2 The Task Allocation Problem 23 Moaiz Ben Dhaou and Didier Fayard Chapter 3 A Comparison of Some Valid Inequality Generation Methods for General 0-1 Problems 49 Pierre Bonami and Michel Minoux Chapter 4 Production Planning 73 Nadia Brauner, Gerd Finke and Maurice Queyranne Chapter 5 Operations Research and Goods Transportation 111 Teodor Gabriel Crainic and Frédéric Semet Chapter 6 Optimization Models for Transportation Systems Planning 177 Teodor Gabriel Crainic and Michael Florian Chapter 7 A Model for the Design of a Minimum-cost Telecomminications Network 209 Marc Demange, Cécile Murat, Vangelis Th. Paschos and Sophie Toulouse Chapter 8 Parallel Combinatorial Optimization 225 Van-Dat Cung, Bertrand Le Cun and Catherine Roucairol Chapter 9 Network Design Problems: Fundamental Methods 253 Alain Quilliot Chapter 10 Network Design Problems: Models and Applications 291 Alain Quilliot Chapter 11 Multicriteria Task Allocation to Heterogenous Processors with Capacity and Mutual Exclusion Constraints 327 Bernard Roy and Roman Slowinski General Bibliography 365 List of Authors 401 Index 405 Summary of Other Volumes in the Series 409
£141.26
ISTE Ltd and John Wiley & Sons Inc Evolutionary Computation with Biogeography-based
Book SynopsisEvolutionary computation algorithms are employed to minimize functions with large number of variables. Biogeography-based optimization (BBO) is an optimization algorithm that is based on the science of biogeography, which researches the migration patterns of species. These migration paradigms provide the main logic behind BBO. Due to the cross-disciplinary nature of the optimization problems, there is a need to develop multiple approaches to tackle them and to study the theoretical reasoning behind their performance. This book explains the mathematical model of BBO algorithm and its variants created to cope with continuous domain problems (with and without constraints) and combinatorial problems.Table of ContentsChapter 1 The Science of Biogeography 1 1.1 Introduction 1 1.2 Island biogeography 3 1.3 Influence factors for biogeography 6 Chapter 2 Biogeography and Biological Optimization 11 2.1 A mathematical model of biogeography 11 2.2 Biogeography as an optimization process 16 2.3 Biological optimization 19 2.3.1 Genetic algorithms 19 2.3.2 Evolution strategies 20 2.3.3 Particle swarm optimization 21 2.3.4 Artificial bee colony algorithm 22 2.4 Conclusion 23 Chapter 3 A Basic BBO Algorithm 25 3.1 BBO definitions and algorithm 25 3.1.1 Migration 26 3.1.2 Mutation 27 3.1.3 BBO implementation 27 3.2 Differences between BBO and other optimization algorithms 35 3.2.1 BBO and genetic algorithms 35 3.2.2 BBO and other algorithms 36 3.3 Simulations 37 3.4 Conclusion 44 Chapter 4 BBO Extensions 45 4.1 Migration curves 45 4.2 Blended migration 49 4.3 Other approaches to BBO 51 4.4 Applications 56 4.5 Conclusion 59 Chapter 5 BBO as a Markov Process 61 5.1 Markov definitions and notations 61 5.2 Markov model of BBO 72 5.3 BBO convergence 79 5.4 Markov models of BBO extensions 90 5.5 Conclusions 99 Chapter 6 Dynamic System Models of BBO 103 6.1 Basic notation 103 6.2 Dynamic system models of BBO 105 6.3 Applications to benchmark problems 119 6.4 Conclusions 122 Chapter 7 Statistical Mechanics Approximations of BBO 123 7.1 Preliminary foundation 123 7.2 Statistical mechanics model of BBO 128 7.2.1 Migration 128 7.2.2 Mutation 134 7.3 Further discussion 141 7.3.1 Finite population effects 141 7.3.2 Separable fitness functions 142 7.4 Conclusions 143 Chapter 8 BBO for Combinatorial Optimization 145 8.1 Traveling salesman problem 147 8.2 BBO for the TSP 148 8.2.1 Population initialization 148 8.2.2 Migration in the TSP 150 8.2.3 Mutation in the TSP 157 8.2.4 Implementation framework 159 8.3 Graph coloring 163 8.4 Knapsack problem 165 8.5 Conclusion 167 Chapter 9 Constrained BBO 169 9.1 Constrained optimization 170 9.2 Constraint-handling methods 172 9.2.1 Static penalty methods 172 9.2.2 Superiority of feasible points 173 9.2.3 The eclectic evolutionary algorithm 174 9.2.4 Dynamic penalty methods 174 9.2.5 Adaptive penalty methods 176 9.2.6 The niched-penalty approach 177 9.2.7 Stochastic ranking 178 9.2.8 ε-level comparisons 178 9.3 BBO for constrained optimization 179 9.4 Conclusion 185 Chapter 10 BBO in Noisy Environments 187 10.1 Noisy fitness functions 188 10.2 Influence of noise on BBO 190 10.3 BBO with re-sampling 193 10.4 The Kalman BBO 196 10.5 Experimental results 199 10.6 Conclusion 201 Chapter 11 Multi-objective BBO 203 11.1 Multi-objective optimization problems 204 11.2 Multi-objective BBO 211 11.2.1 Vector evaluated BBO 211 11.2.2 Non-dominated sorting BBO 213 11.2.3 Niched Pareto BBO 216 11.2.4 Strength Pareto BBO 218 11.3 Real-world applications 223 11.3.1 Warehouse scheduling model 223 11.3.2 Optimization of warehouse scheduling 229 11.4 Conclusion 231 Chapter 12 Hybrid BBO Algorithms 233 12.1 Opposition-based BBO 234 12.1.1 Opposition definitions and concepts 234 12.1.2 Oppositional BBO 236 12.1.3 Experimental results 238 12.2 BBO with local search 240 12.2.1 Local search methods 240 12.2.2 Simulation results 245 12.3 BBO with other EAs 247 12.3.1 Iteration-level hybridization 247 12.3.2 Algorithm-level hybridization 250 12.3.3 Experimental results 254 12.4 Conclusion 256 Appendices 259 Appendix A Unconstrained Benchmark Functions 261 Appendix B Constrained Benchmark Functions 265 Appendix C Multi-objective Benchmark Functions 289 Bibliography 309 Index 325
£125.06
ISTE Ltd and John Wiley & Sons Inc Particle Swarm Optimization
Book SynopsisThis is the first book devoted entirely to Particle Swarm Optimization (PSO), which is a non-specific algorithm, similar to evolutionary algorithms, such as taboo search and ant colonies. Since its original development in 1995, PSO has mainly been applied to continuous-discrete heterogeneous strongly non-linear numerical optimization and it is thus used almost everywhere in the world. Its convergence rate also makes it a preferred tool in dynamic optimization.Table of ContentsForeword. Introduction. Part 1: Particle Swarm Optimization. Chapter 1. What is a difficult problem? Chapter 2. On a table corner. Chapter 3. First formulations. Chapter 4. Benchmark set. Chapter 5. Mistrusting chance. Chapter 6. First results. Chapter 7. Swarm: memory and influence graphs. Chapter 8. Distributions of proximity. Chapter 9. Optimal parameter settings. Chapter 10. Adaptations. Chapter 11. TRIBES or co-operation of tribes. Chapter 12. On the constraints. Chapter 13. Problems and applications. Chapter 14. Conclusion. Part 2: Outlines. Chapter 15. On parallelism. Chapter 16. Combinatorial problems. Chapter 17. Dynamics of a swarm. Chapter 18. Techniques and alternatives. Further Information. Bibliography. Index.
£128.66
Springer Nature Switzerland AG Estimation and Control of Dynamical Systems
Book SynopsisThis book provides a comprehensive presentation of classical and advanced topics in estimation and control of dynamical systems with an emphasis on stochastic control. Many aspects which are not easily found in a single text are provided, such as connections between control theory and mathematical finance, as well as differential games.The book is self-contained and prioritizes concepts rather than full rigor, targeting scientists who want to use control theory in their research in applied mathematics, engineering, economics, and management science. Examples and exercises are included throughout, which will be useful for PhD courses and graduate courses in general.Dr. Alain Bensoussan is Lars Magnus Ericsson Chair at UT Dallas and Director of the International Center for Decision and Risk Analysis which develops risk management research as it pertains to large-investment industrial projects that involve new technologies, applications and markets. He is also Chair Professor at City University Hong Kong.Trade Review“This book is a great resource for graduate students and those who want to learn and understand stochastic control theory. It is also a great read for experts who want to gain a broader overview of the subject and wish to see connections between different techniques. … this is an excellent book and a great complement to the current offering in stochastic control.” (Jan Palczewski, SIAM Review, Vol. 62 (1), 2020)Table of ContentsIntroduction.- State Representation of Linear Dynamical Systems.- Optimal Control of Linear Dynamical Systems.- Estimation Theory.- Further Techniques of Estimation.- Compliments on Probability Theory.- Filtering Theory in Continuous Time.- Stochastic Control of Linear Dynamic Systems with Full Information.- Stochastic Control of Linear Dynamical Systems with Partial Information.- Deterministic Optimal Control.- Stochastic Optimal Control.- Additional Results for BSDE.- Stochastic Control Problems in Finance.- Stochastic Control for Non-Markov Processes.- Principal Agent Control Problems.- Differential Games.- Stackelberg Differential Games.- Target Problems.
£75.99
Springer Nature Switzerland AG Optimization in Large Scale Problems: Industry
Book SynopsisThis volume provides resourceful thinking and insightful management solutions to the many challenges that decision makers face in their predictions, preparations, and implementations of the key elements that our societies and industries need to take as they move toward digitalization and smartness. The discussions within the book aim to uncover the sources of large-scale problems in socio-industrial dilemmas, and the theories that can support these challenges. How theories might also transition to real applications is another question that this book aims to uncover. In answer to the viewpoints expressed by several practitioners and academicians, this book aims to provide both a learning platform which spotlights open questions with related case studies. The relationship between Industry 4.0 and Society 5.0 provides the basis for the expert contributions in this book, highlighting the uses of analytical methods such as mathematical optimization, heuristic methods, decomposition methods, stochastic optimization, and more. The book will prove useful to researchers, students, and engineers in different domains who encounter large scale optimization problems and will encourage them to undertake research in this timely and practical field. The book splits into two parts. The first part covers a general perspective and challenges in a smart society and in industry. The second part covers several case studies and solutions from the operations research perspective for large scale challenges specific to various industry and society related phenomena.Table of ContentsPart 1.- Risk Based Optimization of Integrated Fabrication/Fulfillment Supply Chains (Nasim Nezamoddini, Faisal Aqlan, Amirhosein Gholami).- μθ-EGF: A New Multi-Thread Implementation Algorithm for the Packing Problem inspired by Electromagnetic Fields and Gravitational Effects (Felix Martinez-Rios and Jose Antonio Marmolejo-Saucedo).- The Vector Optimization Method for Solving Integer Linear Programming Problems. Application for the Unit Commitment Problem in Electrical Power Production (Lenar Nizamov).- An Outer Approximation Algorithm for Capacitated Disassembly Scheduling Problem with Parts Commonality and Random Demand (Kanglin Liu, MengWang, Zhi-Hai Zhang),- Multi-Tree Decomposition Methods for Large-Scale Mixed Integer Nonlinear Optimization (Ivo Nowak, Pavlo Muts, and Eligius M.T. Hendrix).- An Embarrassingly Parallel Method for Large-Scale Stochastic Programs (Burhaneddin Sandıkçı and Osman Y. Özaltın).- Part 2.- How to Effectively Train Large Scale Machines (Avan Samareh, Mahshid Salemi Parizi).- A Graph Search Algorithm for Solving Large Scale Median Problems on Real Road Networks (Saeed Ghanbartehrania, J. David Porterb, Mahnoush Samadi Dinania).- Solving Large Scale Optimization Problems in the Transportation Industry and Beyond through Column Generation (Yanqi Xu).- Dynamic Energy Management (Nicholas Moehle, Enzo Busseti, Stephen Boyd, and Matt Wytock).- An Approximation-Based Approach for Chance-Constrained Vehicle Routing and Air Traffic Control Problems (Lijian Chen).- Algorithmic Mechanism Design for Collaboration in Large-scale Transportation Networks (Minghui Lai and Xiaoqiang Cai).- Kantorovich-Rubinstein Distance Minimization: Application to Location Problems (Viktor Kuzmenko, Stan Uryasev).
£79.99
Springer Nature Switzerland AG Geometric Properties for Parabolic and Elliptic
Book SynopsisThis book contains the contributions resulting from the 6th Italian-Japanese workshop on Geometric Properties for Parabolic and Elliptic PDEs, which was held in Cortona (Italy) during the week of May 20–24, 2019. This book will be of great interest for the mathematical community and in particular for researchers studying parabolic and elliptic PDEs. It covers many different fields of current research as follows: convexity of solutions to PDEs, qualitative properties of solutions to parabolic equations, overdetermined problems, inverse problems, Brunn-Minkowski inequalities, Sobolev inequalities, and isoperimetric inequalities.Table of Contents- Poincaré and Hardy Inequalities on Homogeneous Trees. - Ground State Solutions for the Nonlinear Choquard Equation with Prescribed Mass. - Optimization of the Structural Performance of Non-homogeneous Partially Hinged Rectangular Plates. - Energy-Like Functional in a Quasilinear Parabolic Chemotaxis System. - Solvability of a Semilinear Heat Equation via a Quasi Scale Invariance. - Bounds for Sobolev Embedding Constants in Non-simply Connected Planar Domains. - Sharp Estimate of the Life Span of Solutions to the Heat Equation with a Nonlinear Boundary Condition. - Neutral Inclusions, Weakly Neutral Inclusions, and an Over-determined Problem for Confocal Ellipsoids. - Nonexistence of Radial Optimal Functions for the Sobolev Inequality on Cartan-Hadamard Manifolds. - Semiconvexity of Viscosity Solutions to Fully Nonlinear Evolution Equations via Discrete Games. - An Interpolating Inequality for Solutions of Uniformly Elliptic Equations. - Asymptotic Behavior of Solutions for a Fourth Order Parabolic Equation with Gradient Nonlinearity via the Galerkin Method. - A Note on Radial Solutions to the Critical Lane-Emden Equation with a Variable Coefficient. - Remark on One Dimensional Semilinear DampedWave Equation in a CriticalWeighted L2-space.
£67.49
Springer Nature Switzerland AG Practical Channel-Aware Resource Allocation: With
Book SynopsisThis book dives into radio resource allocation optimizations, a research area for wireless communications, in a pragmatic way and not only includes wireless channel conditions but also incorporates the channel in a simple and practical fashion via well-understood equations. Most importantly, the book presents a practical perspective by modeling channel conditions using terrain-aware propagation which narrows the gap between purely theoretical work and that of industry methods. The provided propagation modeling reflects industry grade scenarios for radio environment map and hence makes the channel based resource allocation presented in the book a field-grade view. Also, the book provides large scale simulations that account for realistic locations with terrain conditions that can produce realistic scenarios applicable in the field. Most portions of the book are accompanied with MATLAB code and occasionally MATLAB/Python/C code. The book is intended for graduate students, academics, researchers of resource allocation in mathematics, computer science, and electrical engineering departments as well as working professionals/engineers in wireless industry.Table of ContentsIntroduction.- Utility Functions and Resource Allocation.- Resource Allocation without Channel.- Distributed or Centralized.- Channel Conditions and Resource Allocation.- Propagation Modeling .- Simulation.- Conclusion.
£71.24
Springer Nature Switzerland AG An Optimization Primer
Book SynopsisThis richly illustrated book introduces the subject of optimization to a broad audience with a balanced treatment of theory, models and algorithms. Through numerous examples from statistical learning, operations research, engineering, finance and economics, the text explains how to formulate and justify models while accounting for real-world considerations such as data uncertainty. It goes beyond the classical topics of linear, nonlinear and convex programming and deals with nonconvex and nonsmooth problems as well as games, generalized equations and stochastic optimization.The book teaches theoretical aspects in the context of concrete problems, which makes it an accessible onramp to variational analysis, integral functions and approximation theory. More than 100 exercises and 200 fully developed examples illustrate the application of the concepts. Readers should have some foundation in differential calculus and linear algebra. Exposure to real analysis would be helpful but is not prerequisite. Trade Review“In the reviewer's opinion, this is an important book … . a lot of applications are given, so on one hand the readers can benefit from deep insights into the mathematical background of optimization theory … . This book, which as all books reflects the tastes of its authors, is a solid reference, not only for graduate students and postgraduate students, but also for all those researchers interested in recent developments of optimization theory and methods.” (Giorgio Giorgi, Mathematical Reviews, December, 2022)Table of ContentsPrelude.- Convex optimization.- Optimization under uncertainty.- Minimization problems.- Perturbation and duality.- Without convexity or smoothness.- Generalized Equations.- Risk modeling and sample averages.- Games and minsup problems.- Decomposition.
£55.99
Springer Nature Switzerland AG Uncertainty Quantification and Stochastic
Book SynopsisThis book presents techniques for determining uncertainties in numerical solutions with applications in the fields of business administration, civil engineering, and economics, using Excel as a computational tool. Also included are solutions to uncertainty problems involving stochastic methods. The list of topics specially covered in this volume includes linear and nonlinear programming, Lagrange multipliers (for sensitivity), multi objective optimization, and Game Theory, as well as linear algebraic equations, and probability and statistics. The book also provides a selection of numerical methods developed for Excel, in order to enhance readers’ understanding. As such, it offers a valuable guide for all graduate and undergraduate students in the fields of economics, business administration, civil engineering, and others that rely on Excel as a research tool.Table of ContentsChapter 1: Some tips to use EXCEL.- Chapter 2: Some useful Numerical Methods.- Chapter 3: Probabilities with EXCEL.- Chapter 4: Stochastic Processes.- Chapter 5: Representation of Random Variables.- Chapter 6: Uncertain Algebraic Equations.- Chapter 7: Random Differential Equations.- Chapter 8: UQ in Game Theory.- Chapter 9: Optimization under uncertainty.- Chapter 10: Reliability.- Bibliography.- Index.
£94.99
Springer Nature Switzerland AG Mesh Methods for Boundary-Value Problems and
Book SynopsisThis book gathers papers presented at the 13th International Conference on Mesh Methods for Boundary-Value Problems and Applications, which was held in Kazan, Russia, in October 2020. The papers address the following topics: the theory of mesh methods for boundary-value problems in mathematical physics; non-linear mathematical models in mechanics and physics; algorithms for solving variational inequalities; computing science; and educational systems. Given its scope, the book is chiefly intended for students in the fields of mathematical modeling science and engineering. However, it will also benefit scientists and graduate students interested in these fields.Table of ContentsTheory of the mesh methods for the boundary-value problems in Mathematical Physics.- Non-linear mathematical models in mechanics and physics.- Algorithms for solving variational inequalities.- Computing Science and educational systems.
£97.49
Springer Nature Switzerland AG Hamilton’s Principle in Continuum Mechanics
Book SynopsisThis revised, updated edition provides a comprehensive and rigorous description of the application of Hamilton’s principle to continuous media. To introduce terminology and initial concepts, it begins with what is called the first problem of the calculus of variations. For both historical and pedagogical reasons, it first discusses the application of the principle to systems of particles, including conservative and non-conservative systems and systems with constraints. The foundations of mechanics of continua are introduced in the context of inner product spaces. With this basis, the application of Hamilton’s principle to the classical theories of fluid and solid mechanics are covered. Then recent developments are described, including materials with microstructure, mixtures, and continua with singular surfaces.Table of ContentsMechanics of Systems of Particles .- Mathematical Preliminaries.- Mechanics of Continuous Media.- Motions and Comparison Motions of a Mixture.- Singular Surfaces.- Index.
£104.49
Springer Nature Switzerland AG Elements of the General Theory of Optimal
Book SynopsisIn this monograph, the authors develop a methodology that allows one to construct and substantiate optimal and suboptimal algorithms to solve problems in computational and applied mathematics. Throughout the book, the authors explore well-known and proposed algorithms with a view toward analyzing their quality and the range of their efficiency. The concept of the approach taken is based on several theories (of computations, of optimal algorithms, of interpolation, interlination, and interflatation of functions, to name several). Theoretical principles and practical aspects of testing the quality of algorithms and applied software, are a major component of the exposition. The computer technology in construction of T-efficient algorithms for computing ε-solutions to problems of computational and applied mathematics, is also explored. The readership for this monograph is aimed at scientists, postgraduate students, advanced students, and specialists dealing with issues of developing algorithmic and software support for the solution of problems of computational and applied mathematics.Table of Contents-Preface.- Introduction.- List of symbols and abbreviations.- 1. Elements of the computing theory.- 2. Theories of computational complexity.- 3. Interlination of functions.- 4. Interflatation of functions.- 5. Cubature formulae using interlanation functions.- 6. Testing the quality of algorithm programs.- 7. Computer technologies of solving problems of computational and applied mathematics with fixed values of quality characteristics.- Bilbiography.- Index.- About the Authors.
£87.99
Springer Nature Switzerland AG Numerical Infinities and Infinitesimals in
Book SynopsisThis book provides a friendly introduction to the paradigm and proposes a broad panorama of killing applications of the Infinity Computer in optimization: radically new numerical algorithms, great theoretical insights, efficient software implementations, and interesting practical case studies. This is the first book presenting to the readers interested in optimization the advantages of a recently introduced supercomputing paradigm that allows to numerically work with different infinities and infinitesimals on the Infinity Computer patented in several countries. One of the editors of the book is the creator of the Infinity Computer, and another editor was the first who has started to use it in optimization. Their results were awarded by numerous scientific prizes. This engaging book opens new horizons for researchers, engineers, professors, and students with interests in supercomputing paradigms, optimization, decision making, game theory, and foundations of mathematics and computer science.“Mathematicians have never been comfortable handling infinities… But an entirely new type of mathematics looks set to by-pass the problem… Today, Yaroslav Sergeyev, a mathematician at the University of Calabria in Italy solves this problem… ”MIT Technology Review“These ideas and future hardware prototypes may be productive in all fields of science where infinite and infinitesimal numbers (derivatives, integrals, series, fractals) are used.” A. Adamatzky, Editor-in-Chief of the International Journal of Unconventional Computing.“I am sure that the new approach … will have a very deep impact both on Mathematics and Computer Science.” D. Trigiante, Computational Management Science.“Within the grossone framework, it becomes feasible to deal computationally with infinite quantities, in a way that is both new (in the sense that previously intractable problems become amenable to computation) and natural”. R. Gangle, G. Caterina, F. Tohmé, Soft Computing.“The computational features offered by the Infinity Computer allow us to dynamically change the accuracy of representation and floating-point operations during the flow of a computation. When suitably implemented, this possibility turns out to be particularly advantageous when solving ill-conditioned problems. In fact, compared with a standard multi-precision arithmetic, here the accuracy is improved only when needed, thus not affecting that much the overall computational effort.” P. Amodio, L. Brugnano, F. Iavernaro & F. Mazzia, Soft ComputingTrade Review“This book could have deep impact upon not only local, global, multi-objective optimization and machine learning, but also possibly on applied mathematics more broadly and numerical computation. People interested in new ideas for computer science and its foundations and possibly even the philosophy of mathematics will find this volume interesting, as would those working in theoretical or applied optimization.” (Jonathan Gillard, Optimization Letters, Vol. 17 (2), 2023)Table of ContentsA New Computational Paradigm Using Grossone-Based Numerical Infinities and Infinitesimals.- Nonlinear Optimization: A Brief Overview.- The role of grossone in Nonlinear Programming and Exact Penalty Methods.
£112.49
Springer Nature Switzerland AG Convex Analysis and Beyond: Volume I: Basic
Book SynopsisThis book presents a unified theory of convex functions, sets, and set-valued mappings in topological vector spaces with its specifications to locally convex, Banach and finite-dimensional settings. These developments and expositions are based on the powerful geometric approach of variational analysis, which resides on set extremality with its characterizations and specifications in the presence of convexity. Using this approach, the text consolidates the device of fundamental facts of generalized differential calculus to obtain novel results for convex sets, functions, and set-valued mappings in finite and infinite dimensions. It also explores topics beyond convexity using the fundamental machinery of convex analysis to develop nonconvex generalized differentiation and its applications. The text utilizes an adaptable framework designed with researchers as well as multiple levels of students in mind. It includes many exercises and figures suited to graduate classes in mathematical sciences that are also accessible to advanced students in economics, engineering, and other applications. In addition, it includes chapters on convex analysis and optimization in finite-dimensional spaces that will be useful to upper undergraduate students, whereas the work as a whole provides an ample resource to mathematicians and applied scientists, particularly experts in convex and variational analysis, optimization, and their applications.Trade Review“Each chapter ends with an exercise section … . While primarily addressed to researchers, the book can be used for graduate courses in optimization, by undergraduate and graduate students for theses and projects as well as by researchers and practitioners from other fields where tools from convex analysis, variational analysis and optimization play a role. All in one, the reviewer warmly recommends this book to anyone interested.” (Sorin-Mihai Grad, zbMATH 1506.90001, 2023)“This outstanding book will certainly be useful to anyone interested to learn convex analysis, in particular to graduate students and researchers in the field. Most parts of it can also serve as the basis of advanced courses on a variety of topics. In view of the excellence of this first volume, one can expect the best of the announced second one, which will deal with applications of convex analysis.” (Juan Enrique Martínez-Legaz, Mathematical Reviews, February, 2023)“Every chapter of the book has one section of exercises and one section of commentaries. These sections provide the reader with a lot of information and give him/her great benefits in self-learning. … The book under review has many things to offer and, surely, it will play an important role in the development of convex analysis … . The book is very useful for theoretical research and practical use. Thanks to the art of writing of the authors … .” (Nguyen Dong Yen, Journal of Global Optimization, Vol. 85, 2023)Table of ContentsFundamentals.- Basic theory of convexity.- Convex generalized differentiation.- Enhanced calculus and fenchel duality.- Variational techniques and further subgradient study.- Miscellaneous topics on convexity.- Convexified Lipschitzian analysis.- List of Figures.- Glossary of Notation and Acronyms.- Subject Index.
£42.49
Springer Nature Switzerland AG Reliability-Based Optimization of Floating Wind
Book SynopsisThis book pursues the ambitious goal of combining floating wind turbine design optimization and reliability assessment, which has in fact not been done before. The topic is organized into a series of very ambitious objectives, which start with an initial state-of-the-art review, followed by the development of high-fidelity frameworks for a disruptive way to design next generation floating offshore wind turbine (FOWT) support structures. The development of a verified aero-hydro-servo-elastic coupled numerical model of dynamics for FOWTs and a holistic framework for automated simulation and optimization of FOWT systems, which is later used for the coupling of design optimization with reliability assessment of FOWT systems in a computationally and time-efficient manner, has been an aim of many groups internationally towards implementing a performance-based/goal-setting approach in the design of complex engineering systems. The outcomes of this work quantify the benefits of an optimal design with a lower mass while fulfilling design constraints. Illustrating that comprehensive design methods can be combined with reliability analysis and optimization algorithms towards an integrated reliability-based design optimization (RBDO) can benefit not only the offshore wind energy industry but also other applications such as, among others, civil infrastructure, aerospace, and automotive engineering.Table of ContentsIntroduction.- Review of Reliability-Based Risk Analysis Methods Used in the Offshore Wind Industry.- Floating Offshore Wind Turbine Systems.- Modeling, Automated Simulation, and Optimization.- Design Optimization of FloatingWind Turbine Support Structures.- Reliability-Based Design Optimization of a Spar-Type FloatingWind Turbine Support Structure.- Discussion.- Conclusions.
£151.99
Springer Nature Switzerland AG Modeling, Simulation and Optimization in the
Book SynopsisThis volume is addressed to people who are interested in modern mathematical solutions for real life applications. In particular, mathematical modeling, simulation and optimization is nowadays successfully used in various fields of application, like the energy- or health-sector. Here, mathematics is often the driving force for new innovations and most relevant for the success of many interdisciplinary projects. The presented chapters demonstrate the power of this emerging research field and show how society can benefit from applied mathematics.Table of ContentsPart I Prognostic MR Thermometry for Thermal Ablation of Liver Tumours.- 1 Sebastian Blauth et al., Mathematical Modeling and Simulation of Laser-Induced Thermotherapy for the Treatment of Liver Tumors.- 2 Matthias Andres and René Pinnau, The Cattaneo Model for Laser-Induced Thermotherapy: Identification of the Blood-Perfusion Rate.- 3 Kevin Tolle and Nicole Marheineke, On Online Parameter Identification in Laser-Induced Thermotherapy.- Part II Energy-efficient High Temperature Processes via Shape Optimisation.- 4 Robert Feßler at al., Feasibility Study on Simulating a 3D Furnace Including the Effects of Reactions and Vaporization.- 5 Thomas Marx et al., Shape Optimization for the SP1–Model for Convective Radiative Heat Transfer- 6 Nicolas Dietrich et al., Diffusive Radiation Models for Optimal Shape Design in Phosphate Production.- 7 Ruben Sanchez at al., Adjoint-based sensitivity analysis in high-temperature fluid flows with participating media.
£82.49
Springer International Publishing AG Introduction to Geometric Control
Book SynopsisThis 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.Controllability 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.Table of Contents1. 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.
£43.99
Springer International Publishing AG Introduction to Combinatorial Optimization
Book SynopsisIntroductory 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.Trade Review“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)“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)Table of Contents1. 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.
£38.24
Springer International Publishing AG Design of Heuristic Algorithms for Hard
Book SynopsisThis 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. 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.Table of ContentsPart 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.
£42.74
Springer International Publishing AG Design and Applications of Nature Inspired
Book SynopsisThis book gives a detailed information of various real-life applications from various fields using nature inspired optimization techniques. These techniques are proven to be efficient and robust in many difficult problems in literature. The authors provide detailed information about real-life problems and how various nature inspired optimizations are applied to solve these problems. The authors discuss techniques such as Biogeography Based Optimization, Glow Swarm Optimization, Elephant herd Optimization Algorithm, Cuckoo Search Algorithm, Ant Colony Optimization, and Grey Wolf Optimization etc. These algorithms are applied to a wide range of problems from the field of engineering, finance, medicinal etc. As an important part of the Women in Science and Engineering book series, the work highlights the contribution of women leaders in nature inspired optimization, inspiring women and men, girls and boys to enter and apply themselves to the field.Table of Contents1) AN OVERVIEW OF SWARM INTELLIGENCE BASED ALGORITHMS2) Particle Swarm Optimization and its Applications in the Manufacturing Industry3) Role of Machine Learning in Bioprocess Engineering: Current Perspectives and Future Directions 4) Advanced Selection Operation for Differential Evolution Algorithm 5) Profit Optimization of Two-Unit Briquetting System using grey wolf Optimization algorithm 6) Solving Portfolio optimization using Sine-cosine Algorithm embedded mutation operations7) Detecting Group Shilling Profiles in Recommender Systems: A Hybrid Clustering and Grey Wolf Optimizer Technique8) SINGLE IMAGE REFLECTION REMOVAL USING DEEP LEARNING 9) Social media analysis: A tool for popularity prediction using machine learning classifiers
£74.99
Springer International Publishing AG Operational Research: IO 2021—Analytics for a
Book SynopsisThis book provides the current status of research on the application of OR methods to solve emerging and relevant operations management problems. Each chapter is a selected contribution of the IO2021 - XXI Congress of APDIO, the Portuguese Association of Operational Research, held in Figueira da Foz from 7 to 8 November 2021. Under the theme of analytics for a better world, the book presents interesting results and applications of OR cutting-edge methods and techniques to various real-world problems. Of particular importance are works applying nonlinear, multi-objective optimization, hybrid heuristics, multicriteria decision analysis, data envelopment analysis, simulation, clustering techniques and decision support systems, in different areas such as supply chain management, production planning and scheduling, logistics, energy, telecommunications, finance and health. All chapters were carefully reviewed by the members of the scientific program committee.Table of ContentsA. R. Aguiar, T. Ramos, M. Isabel Gomes, Chapter 1 – A Biased Random-Key Genetic Algorithm for the Home Care Routing and Scheduling Problem: exploring the algorithm's configuration process.- B. F. Azevedo, F. Alvelos, Ana Maria A. C. Rocha, T. Brito, José Lima, Ana I. Pereira, Chapter 2 – An Integer Programming Approach for Sensor Location in a Forest Fire Monitoring System.- C. Bessa, R. Duque, A. Jesus, C. Silva, L. Eberle, S. Moniz, Chapter 3 – Capacity allocation incorporating market equity concerns: a Pharmaceutical Supply Chain case study.- I. S. Costa, R. Figueiredo, C. Requejo, Chapter 4 – The Shortest Path in Signed Graphs.- M. Dias, N. Lourenço, C. Silva, S. Moniz, Chapter 5 – The Break Point: A Machine Learning Approach to Web Breaks in Paper Mills.- M. M. Lima, F. Soares de Sousa, E. G. Öztürk, P. F. Rocha, A. M. Rodrigues, J. S. Ferreira, A. C. Nunes, I. C. Lopes, C. Teles Oliveira, Chapter 6 – A resectorization of fire brigades in the north of Portugal.- L. Magalhães, J. S. Guedes, J. Freire de Sousa, Chapter 7 – A Holistic Framework for Increasing Agility in a Production Process.- P. Nascimento, C. Silva, S. Mueller, S. Moniz, Chapter 8 – Nesting and scheduling for additive manufacturing: an approach considering order due dates.- E. Göksu Öztürk, Filipe Soares de Sousa, M. M. Lima, P. F. Rocha, A. M. Rodrigues, J. S. Ferreira, A. C. Nunes, I. C. Lopes, C. Teles Oliveira, Chapter 9 – Developing a System for Sectorization: An Overview.- M. Pascoal, M. T. Godinho, A. Moghanni, Chapter 10 – New models for finding K short and dissimilar paths.- M. T. Pereira, M. Oliveira, F. A. Ferreira, A. Barreiras, L. Carneiro, Chapter 11 – Time Windows Vehicle Routing Problem to on-time transportation of biological products on healthcare centres.- H. S. Rodrigues, Artur M. C. Brito da Cruz, Chapter 12 – The role of communication on the spread of dengue: an optimal control simulation.- A. Torrado, A. Paula Barbosa-Póvoa, Chapter 13 – Towards an Optimized and Socio-Economic Blood Supply Chain Network.- Clara B. Vaz, ngela P. Ferreira, Chapter 14 – A DEA Approach to Evaluate the Performance of the Electric Mobility Deployment in European Countries.- D. B. Viana, B. B. Oliveira, Chapter 15 – The art of the deal: Machine learning based trade promotion evaluation.
£127.99
Springer International Publishing AG Advances in Best-Worst Method: Proceedings of the
Book SynopsisThis book presents recent advances in the theory and application of the Best-Worst Method (BWM). It includes selected papers from the Third International Workshop on Best-Worst Method (BWM2022), held in Delft, the Netherlands, from 9 to 10 June 2022. The book provides valuable insights on why and how to use BWM in a diverse range of applications including health, energy, supply chain management, and engineering. Moreover, it highlights the use of BWM in different settings including individual decision-making vs group decision-making, and with complete information vs incomplete and uncertain information. Academics and practitioners whose work involves multi-criteria decision-making and decision analysis will particularly benefit from the papers gathered here.
£151.99
Springer International Publishing AG New Trends of Mathematical Inverse Problems and
Book SynopsisThis volume comprises the thoroughly reviewed and revised papers of the First International Conference on New Trends in Applied Mathematics, ICNTAM 2022, which took place in Béni Mellal, Morocco, 19-21 May 2022.The papers deal with the following topics: Inverse Problems, Partial Differential Equations, Mathematical Control, Numerical Analysis and Computer Science. The main interest is in recent trends on Inverse Problems analysis and real applications in Computer Science. The latter is viewed as a dynamic branch on the interface of mathematics and related fields, that has been growing rapidly over the past several decades. However, its mathematical analysis and interpretation still not well-detailed and needs much more clarifications. The main contribution of this book is to give some sufficient mathematical content with expressive results and accurate applications. As a growing field, it is gaining a lot of attention both in media as well as in the industry world, which will attract the interest of readers from different scientist discipline. Table of ContentsA. Oulmelk, M. Srati and L. Afraites. Comparing numerical methods for inverse source problem in time-fractional diffusion equation.- S. Lyaqini. An improvement to the nonparametric regression models using the nonsmooth loss functions.- A. Nachaoui. Iterative methods for inverse problems subject to the convection-diffusion equation.- Y. Essadaoui, I. Hafidi. The dynamic behavior of an incompressible lubrication system.- M. Nachaoui, A. Nachaoui and M A hilal. A new approach for solving an inverse Cauchy problem based on BFGS method.- S. Lyaqini, M. Nachaoui. Heart failure prediction using supervised machine learning algorithms.- M. Srati, A. Oulmelk and L. Afraites. Optimization method for estimating the source term in elliptic equation.- A. Nachaoui. Cauchy's problem for the modified biharmonic equation: ill-posedness and Iterative regularizing methods.- A. Nachaoui and S. M. Rasheed. A mesh free wavelet method to solve the Cauchy problem for the Helmholtz equation.- A. Nachaoui, M. Nachaoui and T. Tadumadze. Meshless methods to noninvasively calculate neurocortical potentials from potentials measured at the scalp surface.- A. Nachaoui and F. Aboud. Solving geometric inverse problems with a polynomial based meshless method.- A. El-Hakoum, Z. Zaabouli and L. Afraites. On the analysis of a coupled denoising PDE.- M. Nachaoui, F. Jauberteau. A novel identification scheme of an inverse source problem based on Hilbert reproducing kernels.
£111.99
Springer International Publishing AG Variable Neighborhood Search: 9th International
Book SynopsisThis volume constitutes the proceedings of the 9th International Conference on Variable Neighborhood Search, ICVNS 2023, held in Abu Dhabi, United Arab Emirates, in October 2022.The 11 full papers presented in this volume were carefully reviewed and selected from 29 submissions. The papers describe recent advances in methods and applications of variable neighborhood search.Table of ContentsA metaheuristic approach for solving Monitor Placement Problem.- A VNS-based heuristic for the minimum number of resources under a perfect schedule.- BVNS for Overlapping Community Detection.- A Simulation-Based Variable Neighborhood Search Approach for Optimizing Cross-Training Policies.- Multi-Objective Variable Neighborhood Search for improving software modularity.- An Effective VNS for Delivery Districting.- BVNS for the Minimum Sitting Arrangement problem in a cycle.- Assigning Multi-Skill Confgurations to Multiple Servers with a Reduced VNS.- Multi-Round Infuence Maximization: A Variable Neighborhood Search Approach.- A VNS based heuristic for a 2D Open Dimension Problem.- BVNS for the bi-objective multi row equal facility layout problem.
£42.74
Springer International Publishing AG Mathematical Optimization Theory and Operations
Book SynopsisThis book constitutes the refereed proceedings of the 22nd International Conference on Mathematical Optimization Theory and Operations Research, MOTOR 2023, held in Ekaterinburg, Russia, during July 2–8, 2023. The 28 full papers and 1 short paper included in this book were carefully reviewed and selected from 89 submissions. They were organized in topical sections as follows: Mathematical programming and applications; discrete and combinatorial optimization; stochastic optimization; scheduling; game theory; and optimal control and mathematical economics. The book also contains one invited talk in full paper length. Table of ContentsInvited papers.- General equilibrium models in production networks with substitution of inputs.- Mathematical programming and applications.- On decentralized nonsmooth optimization.- Byzantine-robust loopless stochastic variance-reduced gradient.- Semi-supervised k-means clustering via DC programming approach.- On the uniqueness of identification the thermal conductivity and heat capacit of substance.- On the uniqueness of identification the thermal conductivity and heat capacity of substance.- Discrete and combinatorial optimization.- Constant-factor approximation algorithms for some maximin multiclustering problems.- Aggregation tree construction using hierarchical structures.- Enumeration and unimodular equivalence of empty delta-modular simplices.- PTAS for p-means q-medoids r-given clustering problem.- Nested (2,3)-instances of the Cutting Stock Problem.- Stochastic optimization.- On the resource allocation problem to increase reliability of transport systems.- Distributionally robust optimization by probability criterion for estimating a bounded signal.- Scheduling.- Approximation algorithms for two-machine proportionate routing open shop on a tree.- MIP heuristics for a resource constrained project scheduling problem with workload stability constraints.- Hybrid evolutionary algorithm with optimized operators for total weighted tardiness problem.- Game theory.- Equilibrium arrivals to preemptive queueing system with fixed reward for completing request.- On optimal positional strategies in fractional optimal control problems.- On a single-type differential game of retention in a ring.- Harmonic numbers in Gambler’s Ruin Problem.- Exploitation and recovery periods in dynamic resource management problem.- Trade-off mechanism to sustain cooperation in pollution reduction.- Communication restriction-based characteristic function in differential games on networks.- Optimal control and mathematical economics.- Guaranteed expectation of the flock position with random distribution of items.- Method for solving a differential inclusion with a subdifferentiable support function of the right-hand side.- Approximate solution of small-time control synthesis problem based on linearization.- A Priori Estimates of the Objective Function in the Speed-in-Action Problem for a Linear Two-Dimensional Discrete-Time System.- An approach to solving input reconstruction problems in stochastic differential equations: dynamic algorithms and tuning their parameters.- Mathematical modeling of the household behavior on the labor market.- Visual positioning of a moving object using multi-objective control algorithm.
£61.74
Springer International Publishing AG The Traveling Salesman Problem: Optimization with
Book SynopsisThis book presents a new search paradigm for solving the Traveling Salesman Problem (TSP). The intrinsic difficulty of the TSP is associated with the combinatorial explosion of potential solutions in the solution space. The author introduces the idea of using the attractor concept in dynamical systems theory to reduce the search space for exhaustive search for the TSP. Numerous examples are used to describe how to use this new search algorithm to solve the TSP and its variants including: multi-objective TSP, dynamic TSP, and probabilistic TSP. This book is intended for readers in the field of optimization research and application.Table of ContentsIntroduction.- The Traveling Salesman Problem (TSP).- The Nature of Heuristic Local Search.- he Attractor-Based Search System.- Solving Multi-objective TSP.- Solving Dynamic TSP.- Solving Probabilistic TSP.- Conclusion.
£31.49
Springer International Publishing AG Learning and Intelligent Optimization: 17th
Book SynopsisThis book constitutes the refereed proceedings of the 17th International Conference on Learning and Intelligent Optimization, LION-17, held in Nice, France, during June 4–8, 2023.The 40 full papers presented have been carefully reviewed and selected from 83 submissions. They focus on all aspects of unleashing the potential of integrating machine learning and optimization approaches, including automatic heuristic selection, intelligent restart strategies, predict-then-optimize, Bayesian optimization, and learning to optimize.Table of ContentsAnomaly Classification to Enable Self-Healing in Cyber Physical Systems using Process Mining.- Hyper-box Classification Model using Mathematical Programming.- A leak localization algorithm in water distribution networks using probabilistic leak representation and optimal transport distance.- Fast and Robust Constrained Optimization via Evolutionary and Quadratic Programming.- Bayesian Optimization for Function Compositions with Applications to Dynamic Pricing.- A Bayesian optimization algorithm for constrained simulation optimization problems with heteroscedastic noise.- Hierarchical Machine Unlearning.- Explaining the Behavior of Reinforcement Learning Agents using Explaining the Behavior of Reinforcement Learning Agents using.- Deep Randomized Networks for Fast Learning.- Generative models via Optimal Transport and Gaussian Processes.- Real-world streaming process discovery from low-level event data.- Robust Neural Network Approach to System Identification in the High-Noise Regime.- GPU for Monte Carlo Search.- Learning the Bias Weights for Generalized Nested Rollout Policy Adaptation.- Heuristics selection with ML in CP Optimizer.- Model-based feature selection for neural networks: A mixed-integer programming approach.- An Error-Based Measure for Concept Drift Detection and Characterization.- Predict, Tune and Optimize for Data-Driven Shift Scheduling with Uncertain Demands.- On Learning When to Decompose Graphical Models.- Inverse Lighting with Differentiable Physically-Based Model.- Repositioning Fleet Vehicles: a Learning Pipeline.- Bayesian Decision Trees Inspired from Evolutionary Algorithms.- Towards Tackling MaxSAT by Combining Nested Monte Carlo with Local Search.- Relational Graph Attention-based Deep Reinforcement Learning: An Application to Flexible Job Shop Scheduling with Sequence-dependent Setup Times.- Experimental Digital Twin for Job Shops with Transportation Agents.- Learning to Prune Electric Vehicle Routing Problems.- A matheuristic approach for electric bus fleet scheduling.- Class GP: Gaussian Process Modeling for Heterogeneous Functions.- Surrogate Membership for Inferred Metrics in Fairness Evaluation.- The BeMi Stardust: a Structured Ensemble of Binarized Neural Network.- Discovering explicit scale-up criteria in crisis response with decision mining.- Job Shop Scheduling via Deep Reinforcement Learning: a Sequence to Sequence approach.- Generating a Graph Colouring Heuristic with Deep Q-Learning and Graph Neural Networks.- Multi-Task Predict-then-Optimize.- Integrating Hyperparameter Search into Model-Free AutoML with Context-Free Grammars.- Improving subtour elimination constraint generation in Branch-and-Cut algorithms for the TSP with Machine Learning.- Learn, Compare, Search: One Sawmill’s Search for the Best Cutting Patterns Across And/or Trees.- Dynamic Police Patrol Scheduling with Multi-Agent Reinforcement Learning.- Analysis of Heuristics for Vector Scheduling and Vector Bin Packing.- Unleashing the potential of restart by detecting the search stagnation.
£75.99
Springer International Publishing AG Calculus II: Practice Problems, Methods, and
Book SynopsisThis 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 coursesTable of ContentsChapter 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.
£42.74
Springer Optimization Discrete Mathematics and
Book SynopsisOn the morphism 1 ? 121, 2 ? 12221.- Polynomials and combinatorial identities.- Rainbow Greedy Matching Algorithms.- Predictive models of Non-Performing Loans: the case of Greece.- The Cost of Detection in Interaction Testing.- On the study of cycle chains representing non-reversible Markov chains associated with random walks with jumps in fixed environments.- Applying Distance Measures for Discrete Data.- Demand aggregation and mid-term energy planning problem on the business layer.- Factor Fitting, Rank Allocation, and Partitioning in Multilevel Low Rank Matrices.- A Code-based Watermarking Scheme for the Protection of Authenticity of Medical Images.- The minimum cost energy flow problem under demand uncertainty Effect on optimal solution, variability, worst and best case scenarios.- A mathematical study of the Braess's Paradox within a network comprising four nodes, five edges, and linear time functions.- On similiarities between two global optimization algorithms based on different (Bayesian and Lipschitzian) approaches.
£104.49
Springer Biofuel Production Performance and Emission
Book SynopsisPreface.- Introduction to Renewable Energy Resources and Sustainable Feedstocks for Biodiesel.- Studies on oil extraction, biodiesel production methods, and engine performance and emission characteristic.- Experimentation, Modelling, and Analysis of Biodiesel conversion and their engine performance and emission characteristics.- Artificial Intelligence Modelling of engine performance and emission characteristic.- Optimization of engine performance and emission characteristics.
£89.99
Springer Analysis Approximation Optimization Computation and Applications
Book SynopsisPart I Introduction.- Chapter 1 The Life Path and Scientific Career of Academician ProfessorGradimir V. Milovanovic.- Part II Contributed Chapters.- Chapter 2 Shift-Invariant Subspaces of Sobolev Spaces and Shift-Preserving Operators.- Chapter 3 Multicriteria Model with a Dominant Criterion in Satisfaction and Non-Satisfaction.- Chapter 4 Enhanced Averaged Quadrature Rules with Application to Error Estimation.- Chapter 5 A Curious Hypergeometric Identity and Perfectness of Meixner–Sorokin System of Weights.- Chapter 6 On Reverse Triangle Inequality and some its Applications.- Chapter 7 Classical Karamata Theory of Regular Variability and the Index Function Operator.- Chapter 8 A note on Laguerre Truncated Polynomials and QuadratureFormula.- Chapter 9 An Optimal Quadrature Formula with Derivative in the Hilbert Space.- Chapter 10 Dynamics of Operators on the Space of Radon Measures.- Chapter 11 Error Estimates of Gaussian-type Quadratures – A Survey.- Chapter 12 Partitions and Automorphisms of Finite Kurepa Trees.- Chapter 13 Karamata’s Theory, Asymptotic Relations and the Generalized Inverse.- Chapter 14 Optimal Solutions of System of ( ?? , ??)-Hilfer FDEs via BestProximity Point using MNC.- Chapter 15 Iterative Methods for the Inclusion of the Inverse Matrix.- Chapter 16 The Optimal Quadrature Formula for the Approximate Calculation of Fourier Coefficients in the Space f??2 (2,1) of Periodic Functions.- Chapter 17 On Almost Deferred Sequence Spaces Defined by Infinite Matrix.- Chapter 18 Certain Classes of Combinatorial Polynomials Involving (Inverse) Binomial Coefficients with their Applications.- Chapter 19 The Finite Difference Method for Solution of Parabolic Problem with Time-Dependent Coefficients.- Chapter 20 Quadrature Rules for Trigonometric Polynomials.- Chapter 21 A Boubaker Collocation Method to Solve the Single Degree of Freedom System.- Chapter 22 New Multiplicative Method of Multicriteria Analysis.
£124.92
Springer Geometry and NonConvex Optimization
Book SynopsisPreface.- Chapter 1 Hermite-Hadamard Like Inequalities Involving Generalized Bi-Convex Functions.- Chapter 2 Some properties of Barrelled and of Bornological locally convex spaces over an arbitrary complete valued field.- Chapter 3 Bounds for the Unweighted Jensen's Gap of Absolutely Continuous Functions.- Chapter 4 Test Instances for Multiobjective Mixed-Integer Nonlinear Optimization.- Chapter 5 A trace operator for weighted Sobolev spaces.- Chapter 6 Common fixed point results for Meir-Keeler type contraction mappings.- Chapter 7 Optimum Statistical Analysis on Sphere Surface.- Chapter 8 New Generalized Ostrowski Type Fractional Integral Inequalities.- Chapter 9 Genaralized Fractional Hilbert Type Integral Inequalities in Banach Spaces.- Chapter 10 Ternary derivation-homomorphism functional inequalities.- Chapter 11 C*-ternary biderivations and C*-ternary bihomomorphisms.- Chapter 12 A Survey of Erdos-Szekeres Products.- Chapter 13 Recent developments in general quasi variational inequality.- Chapter 14 General Variational Inequalities and Optimization.- Chapter 15 Characterizations and Set Theoretic Properties of Some Generalized Open and Fat Sets in Relator Spaces.- Chapter 16 Direct Continuity Properties of Relations in Relator Spaces.- Chapter 17 Variational Principles and Fixed Points in Symmetric Structures.- Chapter 18 Sequential Coercivity over Q-Ordered Q-Metric Spaces.- Chapter 19 A Hardy-Hilbert’s Integral Inequality with the Internal Variables Involving Two Derivative Functions.
£164.41
Springer Optimal Quadratic Programming and QCQP Algorithms with Case Studies
Book SynopsisPreface.- Part I Background.- Chapter 1 Linear Algebra.- Chapter 2 Optimization.- Part II Basic Algorithms.- Chapter 3 Gradient Methods.- Chapter 4 Conjugate Gradients as Direct Method.- Chapter 5 Gradient Projection.- Chapter 6 From Penalty to Exact Augmented Lagrangians.- Chapter 7 Active Sets with Finite Termination.- Part III Optimal Algorithms.- Chapter 8 Conjugate Gradients as Iterative Method.- Chapter 9 SMALE for Equality Constraints.- Chapter 10 MPRGP for Bound Constraints.- Chapter 11 MPGP and PBBF for Separable QCQP.- Chapter 12 Solvers for Separable and Equality QP/QCQP Problems.- Part IV Case Studies.- Chapter 13 Elliptic Variational Inequalities.- Chapter 14 Contact Problem with Friction.- Chapter 15 Model Predictive Control.- Chapter 16 Support Vector Machines.- Chapter 17 PERMON and ESPRESO Software.- References.
£116.99
Springer Turnpike Phenomenon for Markov Decision Processes
Book Synopsis1. Introduction.- 2. Uniqueness and stability of optimal policies.- 3. Existence of an overtaking optimal policy.- 4. A weak turnpike property.- 5. Convex Markov decision processes.- 6. Turnpike properties for MDPs with perturbations.- 7. Controllability properties.- 8. Optimal control problems with singleton-turnpikes.- 9. Conclusions.
£42.74
Walter de Gruyter Math Optimization for Artificial Intelligence
Book Synopsis
£129.67
De Gruyter Operational Perspective of Modeling System
Book Synopsis
£139.17
Springer Fachmedien Wiesbaden Numerische Verfahren der konvexen, nichtglatten
Book SynopsisKonvexe Optimierungsprobleme mit einer nichtglatten Zielfunktion treten in vielen Anwendungen auf, beispielsweise im Zusammenhang mit Penalty-Verfahren für differenzierbare Optimierungsprobleme, mit der Lagrange-Relaxation bei kombinatorischen Optimierungsproblemen oder bei der Strukturoptimierung von Stabwerken. Die wichtigsten numerischen Verfahren zur Lösung solcher Optimierungsprobleme sind Subgradienten- und Bundle-Verfahren. Das Buch gibt eine kompakte Einführung in die Grundlagen dieser Verfahren, die den Leser in die Lage versetzt, einfache Versionen der Verfahren selbst zu implementieren.Table of Contents1 Einführung.- 1.1 Konvexe Mengen und Funktionen.- 1.2 Konvexe Optimierungsaufgaben.- 1.3 Warum spezielle Verfahren?.- 2 Konvexe Mengen und Funktionen.- 2.1 Konvexe Mengen.- 2.2 Projektion auf konvexe Mengen.- 2.3 Trennungssätze.- 2.4 Konvexe Funktionen.- 2.5 Operationen mit konvexen Funktionen.- 2.6 Affine Minoranten.- 2.7 Lokale Lipschitz-Stetigkeit.- 2.8 Subdifferential und Richtungsableitung.- 2.9 Maximumfunktionen.- 3 Konvexe Optimierungsprobleme.- 3.1 Unrestringierte Probleme.- 3.2 Abstiegsrichtungen.- 3.3 Probleme mit allgemeinen konvexen Restriktionen.- 3.4 Lineare Nebenbedingungen.- 4 Das Subgradientenverfahren.- 4.1 Das Verfahren.- 4.2 Konvergenzbetrachtungen.- 4.3 Numerische Beispiele.- 5 Approximative Ableitungen.- 5.1 Approximation des Subdifferentials.- 5.2 Approximation der Richtungsableitung.- 5.3 Approximative Minima.- 5.4 Approximative Abstiegsrichtungen.- 6 Approximative Abstiegsverfahren.- 6.1 Grundlegende Verfahrenskonzepte.- 6.1.1 Verwendung eines Bundles.- 6.1.2 Approximative Suchrichtungen.- 6.1.3 Verfahren mit approximativer Suchrichtung.- 6.2 Das Schrittweitenverfahren.- 6.2.1 Iterative Berechnung der Schrittweite.- 6.2.2 Das Verfahren.- 6.2.3 Konvergenz des Schrittweitenverfahrens.- 6.3 Konstruktion des Bundles.- 6.4 Ein implementierbares Abstiegsverfahren.- 6.4.1 Das Verfahren.- 6.4.2 Konvergenz des Verfahrens.- 7 Bundle-Verfahren.- 7.1 Stopp-Kriterien.- 7.2 Allgemeiner Verfahrensablauf.- 7.3 Numerische Beispiele.- 8 Bundle-Trust-Region-Verfahren.- 8.1 Grundlage des Verfahrens.- 8.2 Das Trust-Region-Problem.- 8.3 Das Verfahrenskonzept.- 8.4 Implementierung des Verfahrens.- 8.4.1 Anpassung des Trust-Region-Parameters.- 8.4.2 Ein Abbruchkriterium.- 8.4.3 Kriterien für einen Abstiegsschritt.- 8.4.4 Kriterien für einen Nullschritt.- 8.4.5 Berechnung des Trust-Region-Parameters.- 8.4.6 Konstruktion des Bundles.- 8.5 Das Bundle-Trust-Region-Verfahren.- 8.6 Konvergenz des Verfahrens.- 8.7 Numerische Beispiele.- 8.8 Probleme mit linearen Restriktionen.- Übungsaufgaben.
£26.59
Springer Fachmedien Wiesbaden Numerik der Optimierung
Book Synopsis1 Optimierungsaufgaben und Optimalitätskriterien.- 1.1 Globale und lokale Optima, Konvexität.- 1.2 Optimalitätsbedingungen.- 1.3 Semiinfinite Probleme.- 1.4 Ganzzahlige Probleme.- 1.5 Optimierung über Graphen.- 2 Dualität.- 2.1 Duale Probleme.- 2.2 Gestörte Optimierungsprobleme.- 2.3 Anwendungen der Dualität.- 3 Minimierung ohne Restriktionen.- 3.1 Gradientenverfahren.- 3.2 Das Newton-Verfahren.- 3.3 Quasi-Newton-Verfahren.- 3.4 CG-Verfahren.- 3.5 Minimierung nichtglatter Funktionen.- 4 Linear restringierte Probleme.- 4.1 Polyedrische Mengen.- 4.2 Lineare Optimierung.- 4.3 Minimierung über Mannigfaltigkeiten.- 4.4 Probleme mit Ungleichungsrestriktionen.- 5 Strafmethoden.- 5.1 Das Grundprinzip von Strafmethoden.- 5.2 Konvergenzabschätzungen.- 5.3 Modifizierte Lagrange-Funktionen.- 5.4 Strafmethoden und elliptische Randwertprobleme.- 6 Approximationsverfahren.- 6.1 Verfahren der zulässigen Richtungen.- 6.2 Überlinear konvergente Verfahren.- 7 Komplexität.- 7.1 Definitionen, Polynomialität.- 7.2 Nichtdeterministisch polynomiale Algorithmen.- 7.3 Optimierungsprobleme und die Klasse NP-hart.- 7.4 Komplexität in der linearen Optimierung.- 8 Innere-Punkt-Methoden.- 8.1 Innerer-Pfad-Methode für lineare Probleme.- 8.2 Parameterfreies Potential.- 8.3 Der Algorithmus von Karmarkar.- 8.4 Komplementaritätsprobleme.- 8.5 Komplexität der linearen Optimierung.- 9 Aufgaben über Graphen.- 9.1 Definitionen.- 9.2 Graphen und lineare Optimierung.- 9.3 Aufdatierungen in Graphen.- 9.4 Probleme aus der Klasse NP-vollständig.- 10 Die Methode branch and bound.- 10.1 Relaxation, Separation, Strategien.- 10.2 Branch and bound für GLO.- 10.3 Das Rundreiseproblem.- 11 Dekomposition.- 11.1 Dekompositionsprinzipien.- 11.2 Dynamische Optimierung.- 11.3 Ausgewählte Anwendungen.- 12Strukturuntersuchungen.- 12.1 Ganzzahlige Polyeder.- 12.2 Gültige Ungleichungen.- 12.3 Matroide, Greedy-Algorithmus.Table of ContentsAufgabenstellung und Optimalitätskriterien - Dualität, Minimierung ohne Restriktionen - Optimierungsverfahren für linear restringierte Probleme - Penalty-Verfahren und modifizierte Lagrange-Methoden - Verfahren auf der Basis lokaler Approximationen - Komplexität - Innere-Punkt-Methoden - Graphentheoretische Modelle und Verfahren - Die Methode branch and bound - Dekompositionstechniken für strukturierte Optimierungsprobleme - Strukturuntersuchungen in ganzzahligen Optimierungsproblemen
£26.59
Springer Fachmedien Wiesbaden Grundlagen der Mathematischen Optimierung:
Book SynopsisDas Buch stellt wesentliche Ansätze, Ergebnisse und Methoden der linearen und ganzzahligen Optimierung dar. Ziel ist es, eine solide mathematische Grundlage des Gebietes und seiner wichtigsten algorithmischen Ansätze zu entwickeln. Methodisch zentral ist der geometrische Zugang.Table of ContentsEinleitung. - Einstiege: Ungleichungssysteme und diskrete Strukturen. - Einstiege: Algorithmen und Komplexität. - Konvexitätstheorie - Der Simplex-Algorithmus. - LP-Dualität.
£32.99
Springer Fachmedien Wiesbaden Mathematische Optimierung: Eine Einführung in die
Book SynopsisDas essential gibt Bachelor- und Masterstudierenden der Ingenieur- und Naturwissenschaften sowie der Wirtschaftswissenschaften eine kurze, anschauliche Einführung in die mathematische Optimierung. Anhand zahlreicher Beispiele werden die Grundbegriffe und Kernaussagen der kontinuierlichen Optimierung erläutert und angewendet. Es werden sowohl Optimierungsprobleme mit und ohne Nebenbedingungen als auch mehrkriterielle Probleme mit mehr als einer Zielfunktion behandelt.Table of ContentsBeispielprobleme: Ausgleichskurve, Standortplanung, Haltestellenproblem.- Optimalitätskriterien erster und zweiter Ordnung.- Karush-Kuhn-Tucker-Bedingungen, Pareto-optimale Lösungen und Pareto-Menge.- Methode der gewichteten Summe.
£11.77
Birkhauser Verlag AG Variational methods in some shape optimization
Book SynopsisThe study of shape optimization problems is a very wide field, both classical, as the isoperimetric problem and Newton problem of the best aerodynamical shape show, and modern, for all the recent results obtained in the last two, three decades. The fascinating feature is that the competing objects are shapes, i.e. domains of Rn, instead of functions, as usually occurs in problems of calculus of variations. This constraint often produces additional difficulties that lead to a lack of existence of a solution and the introduction of suitable relaxed formulations of the problem. However, in a few cases an optimal solution exists, due to the special form of the cost functional and to the geometrical restriction on the class of competing domains. This volume collects the lecture notes of two courses given in the academic year 2000/01 by the authors at the University of Pisa and at the Scuola Normale Superiore respectively. The courses were mainly addressed to Ph. D. students and required a background in the topics in functional analysis that are usually taught in undergraduate courses.
£18.19
Birkhauser Verlag AG Optimal Transportation, Geometry and Functional
Book SynopsisIn 2008, a school on the theory of optimal transportation and its applications took place in Pisa, with lectures by F. Barthe, W. Gangbo, F. Maggi and R. McCann. In this book, the notes of the first three lecturers are collected. They provide a deep insight on concentration inequalities, evolution PDEs of Hamiltonian type, geometric and functional inequalities.
£25.64
Birkhauser Verlag AG Geometric Partial Differential Equations
Book SynopsisThis book is the outcome of a conference held at the Centro De Giorgi of the Scuola Normale of Pisa in September 2012. The aim of the conference was to discuss recent results on nonlinear partial differential equations, and more specifically geometric evolutions and reaction-diffusion equations. Particular attention was paid to self-similar solutions, such as solitons and travelling waves, asymptotic behaviour, formation of singularities and qualitative properties of solutions. These problems arise in many models from Physics, Biology, Image Processing and Applied Mathematics in general, and have attracted a lot of attention in recent years.Table of ContentsN. Alikakos: On the structure of phase transition maps for three or more coexisting phases.- S. Amato, G. Bellettini, M. Paolini: The nonlinear multidomain model: a new formal asymptotic analysis.- A. Chambolle, M. Goldman, M. Novaga: Existence and qualitative properties of isoperimetric sets in periodic media.- A. Chambolle, M. Morini, M. Ponsiglione: Minimizing movements and level set approach to geometric flow of nonlocal perimeters.- S. Choi, I. Kim: Homogenization with oscillatory Neumann boundary data in general domain.- D. Christodoulou: The Analysis of Shock Formation in 3-Dimensional Fluids.- L. Dupaigne, A. Farina, B. Sirakov: Regularity of the extremal solutions for the Liouville system.- M.-H. Giga, Y. Giga, A. Nakayasu: On general existence results for one-dimensional singular diffusion equations with spatially inhomogeneous driving force.- Y. Giga, G. Pisante: On representation of boundary integrals involving the mean curvature for mean-convex domains.- A. Lemenant, Y. Sire: Elliptic problem in nonsmooth domain, Reifenberg-flat domains, Regularity.- A. Pisante: Maximally localized wannier functions: existence and exponential localization.- A. Stancu: Flows by powers of centro-affine curvature.
£24.69
