{"title":"Mathematical theory of computation Books","description":"","products":[{"product_id":"discrete-mathematics-9780198507178","title":"Discrete Mathematics","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eBiggs'' Discrete Mathematics has been a best-selling textbook since the first and revised editions were published in 1986 and 1990, respectively. This second edition has been developed in response to undergraduate course changes and changes in students'' needs. New to this edition are chapters on statements and proof, logical framework, and natural numbers and the integers, in addition to updated chapters from the previous edition. The new chapters are presented at a level suitable for mathematics and computer science students seeking a first approach to this broad and highly relevant topic. Each chapter contains newly developed tailored exercises, and miscellaneous exercises are presented throughout, providing the student with over 1000 individual tailored exercises. This edition is accompanied by a website www.oup.com\/mathematics\/discretemath containing hints and solutions to all exercises presented in the text, providing an invaluable resource for students and lecturers alike. The b\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003eThis is a new edition of a successful textbook ... this revision is particularly welcome ... The text is written in a fluent but rigorous style and should appeal to sixthformers and undergraduates who are alienated by more formal presentations. There are plenty of approachable exercises, ranging from easy riders to establish technique to more challenging problems which introduce new ideas, and a bonus is that all the answers are available on a companion web-site. I can thoroughly recommend this text. * The Mathematical Gazette *\u003cbr\u003eA well known definition says that a textbook is a book such that everybody thinks he can write a better one. Biggs' Discrete Mathematics is an exception - not only for its wide range of topics and its clear organization but notably for its excellent style of explanation. * EMS *\u003cbr\u003e... the ideal choice for introductory courses to discrete mathematicians. * Zentralblatt MATH *\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003eTHE LANGUAGE OF MATHEMATICS; TECHNIQUES; ALGORITHMS AND GRAPHS; ALGEBRAIC METHODS","brand":"Oxford University Press","offers":[{"title":"Default Title","offer_id":48732762997079,"sku":"9780198507178","price":62.7,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780198507178.jpg?v=1719998292"},{"product_id":"mathematics-of-big-data-9780262038393","title":"Mathematics of Big Data","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e","brand":"MIT Press Ltd","offers":[{"title":"Default Title","offer_id":48733448765783,"sku":"9780262038393","price":72.2,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780262038393.jpg?v=1720000119"},{"product_id":"compressive-imaging-structure-sampling-learning-9781108421614","title":"Compressive Imaging Structure Sampling Learning","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eAccurate, robust and fast image reconstruction is a critical task in many scientific, industrial and medical applications. Over the last decade, image reconstruction has been revolutionized by the rise of compressive imaging. It has fundamentally changed the way modern image reconstruction is performed. This in-depth treatment of the subject commences with a practical introduction to compressive imaging, supplemented with examples and downloadable code, intended for readers without extensive background in the subject. Next, it introduces core topics in compressive imaging  including compressed sensing, wavelets and optimization  in a concise yet rigorous way, before providing a detailed treatment of the mathematics of compressive imaging. The final part is devoted to recent trends in compressive imaging: deep learning and neural networks. With an eye to the next decade of imaging research, and using both empirical and mathematical insights, it examines the potential benefits and the pi\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e1. Introduction; Part I. The Essentials of Compressive Imaging: 2. Images, transforms and sampling; 3. A short guide to compressive imaging; 4. Techniques for enhancing performance; Part II. Compressed Sensing, Optimization and Wavelets: 5. An introduction to conventional compressed sensing; 6. The LASSO and its cousins; 7. Optimization for compressed sensing; 8. Analysis of optimization algorithms; 9. Wavelets; 10. A taste of wavelet approximation theory; Part III. Compressed Sensing with Local Structure: 11. From global to local; 12. Local structure and nonuniform recovery; 13. Local structure and uniform recovery; 14. Infinite-dimensional compressed sensing; Part IV. Compressed Sensing for Imaging: 15. Sampling strategies for compressive imaging; 16. Recovery guarantees for wavelet-based compressive imaging; 17. Total variation minimization; Part V. From Compressed Sensing to Deep Learning: 18. Neural networks and deep learning; 19. Deep learning for compressive imaging; 20. Accuracy and stability of deep learning for compressive imaging; 21. Stable and accurate neural networks for compressive imaging; 22. Epilogue; Appendices: A. Linear Algebra; B. Functional analysis; C. Probability; D. Convex analysis and convex optimization; E. Fourier transforms and series; F. Properties of Walsh functions and the Walsh transform; Notation; Abbreviations; References; Index.","brand":"Cambridge University Press","offers":[{"title":"Default Title","offer_id":48738287419735,"sku":"9781108421614","price":59.84,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781108421614.jpg?v=1723811892"},{"product_id":"mathematical-aspects-of-deep-learning-9781316516782","title":"Mathematical Aspects of Deep Learning","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eIn recent years the development of new classification and regression algorithms based on deep learning has led to a revolution in the fields of artificial intelligence, machine learning, and data analysis. The development of a theoretical foundation to guarantee the success of these algorithms constitutes one of the most active and exciting research topics in applied mathematics. This book presents the current mathematical understanding of deep learning methods from the point of view of the leading experts in the field. It serves both as a starting point for researchers and graduate students in computer science, mathematics, and statistics trying to get into the field and as an invaluable reference for future research.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e1. The modern mathematics of deep learning Julius Berner, Philipp Grohs, Gitta Kutyniok and Philipp Petersen; 2. Generalization in deep learning Kenji Kawaguchi, Leslie Pack Kaelbling, and Yoshua Bengio; 3. Expressivity of deep neural networks Ingo Gühring, Mones Raslan and Gitta Kutyniok; 4. Optimization landscape of neural networks René Vidal, Zhihui Zhu and Benjamin D. Haeffele; 5. Explaining the decisions of convolutional and recurrent neural networks Wojciech Samek, Leila Arras, Ahmed Osman, Grégoire Montavon and Klaus-Robert Müller; 6. Stochastic feedforward neural networks: universal approximation Thomas Merkh and Guido Montúfar; 7. Deep learning as sparsity enforcing algorithms A. Aberdam and J. Sulam; 8. The scattering transform Joan Bruna; 9. Deep generative models and inverse problems Alexandros G. Dimakis; 10. A dynamical systems and optimal control approach to deep learning Weinan E, Jiequn Han and Qianxiao Li; 11. Bridging many-body quantum physics and deep learning via tensor networks Yoav Levine, Or Sharir, Nadav Cohen and Amnon Shashua.","brand":"Cambridge University Press","offers":[{"title":"Default Title","offer_id":48738561392983,"sku":"9781316516782","price":66.49,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781316516782.jpg?v=1720049471"},{"product_id":"matlab-deep-learning-9781484228449","title":"MATLAB Deep Learning","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cdiv\u003eGet started with MATLAB for deep learning and AI with this in-depth primer. In this book, you start with machine learning fundamentals, then move on to neural networks, deep learning, and then convolutional neural networks. In a blend of fundamentals and applications, \u003ci\u003eMATLAB Deep Learning\u003c\/i\u003e employs MATLAB as the underlying programming language and tool for the examples and case studies in this book.  \u003c\/div\u003e\u003cdiv\u003e\u003cbr\u003e\u003c\/div\u003e\u003cdiv\u003eWith this book, you''ll be able to tackle some of today''s real world big data, smart bots, and other complex data problems. You''ll see how deep learning is a complex and more intelligent aspect of machine learning for modern smart data analysis and usage.\u003c\/div\u003e\u003cdiv\u003e\u003cbr\u003e\u003c\/div\u003e\u003cdiv\u003e\u003cb\u003eWhat You''ll Learn\u003c\/b\u003e\u003c\/div\u003e\u003cdiv\u003e\u003cul\u003e\n\u003cli\u003eUse MATLAB for deep learning\u003cbr\u003e\n\u003c\/li\u003e\n\u003cli\u003eDiscover neural networks and multi-layer neural networks\u003cbr\u003e\n\u003c\/li\u003e\n\u003cli\u003eWork with convolution and pooling layers\u003cbr\u003e\n\u003c\/li\u003e\n\u003cli\u003eBuild a MNIST example with these layers\u003cbr\u003e\n\u003c\/li\u003e\n\u003c\/ul\u003e\u003c\/div\u003e\u003cb\u003eWho\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e1. Machine Learning2. Neural Network3. Training of Multi-Layer Neural Network4. Neural Network and Classification5. Deep Learning6. Convolutional Neural Network\u003c\/b\u003e","brand":"APress","offers":[{"title":"Default Title","offer_id":48739662594391,"sku":"9781484228449","price":49.49,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781484228449.jpg?v=1720052848"},{"product_id":"will-we-ever-have-a-quantum-computer-9783030420185","title":"Will We Ever Have a Quantum Computer?","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003eThis book addresses a broad community of physicists, engineers, computer scientists and industry professionals, as well as the general public, who are aware of the unprecedented media hype surrounding the supposedly imminent new era of quantum computing. The central argument of this book is that the feasibility of\u003ci\u003e \u003c\/i\u003equantum computing in the physical world is extremely doubtful. The hypothetical quantum computer is not simply a quantum variant of the conventional digital computer, but rather a quantum extension of a classical \u003ci\u003eanalog\u003c\/i\u003e computer operating with continuous parameters. In order to have a useful machine, the number of continuous parameters to control would have to be of such an astronomically large magnitude as to render the endeavor virtually infeasible. This viewpoint is based on the author’s expert understanding of the gargantuan challenges that would have to be overcome to ever make quantum computing a reality. Knowledge of secondary-school-level physics and math will be sufficient for understanding most of the text.\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eIntroduction.- Brief history of quantum computing, starting with the invention of Shor's algorithm (1994).- Introduction to quantum mechanics for pedestrians.- Electron spin as a qubit.- The main ideas and promises of quantum computing.- Current state of the art.\u003c\/p\u003e","brand":"Springer Nature Switzerland AG","offers":[{"title":"Default Title","offer_id":48743035470167,"sku":"9783030420185","price":54.99,"currency_code":"GBP","in_stock":true}]},{"product_id":"graph-transformation-for-software-engineers-with-applications-to-model-based-development-and-domain-specific-language-engineering-9783030439156","title":"Graph Transformation for Software Engineers: With","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eThis book is an introduction to graph transformation as a foundation to model-based software engineering at the level of both individual systems and domain-specific modelling languages.\u003c\/p\u003e\u003cp\u003eThe first part of the book presents the fundamentals in a precise, yet largely informal way. Besides serving as prerequisite for describing the applications in the second part, it also provides a comprehensive and systematic survey of the concepts, notations and techniques of graph transformation. The second part presents and discusses a range of applications to both model-based software engineering and domain-specific language engineering. The variety of these applications demonstrates how broadly graphs and graph transformations can be used to model, analyse and implement complex software systems and languages. \u003c\/p\u003e\u003cp\u003eThis is the first textbook that explains the most commonly used concepts, notations, techniques and applications of graph transformation without focusing on one particular mathematical representation or implementation approach. Emphasising the research and engineering methodologies used, it will be a valuable resource for graduate students, practitioners and researchers in software engineering, foundations of programming and formal methods.\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003ePart I, Graph Transformation.- Graphs for Modeling and Specification.- Graph Transformation Concepts.- Beyond Individual Rules: Usage Scenarios and Control Structures.- Analysis and Improvement of Graph Transformation Systems.- Part II, Graph Transformation in Software Engineering.- Detecting Inconsistent Requirements in a Use Case-Driven Approach.- Service Specification and Matching.- Model-Based Testing.- Reverse Engineering: Inferring Visual Contracts from Java Programs.- Stochastic Analysis of Dynamic Software Architectures.- Advanced Modeling Language Definition: Integrating Meta-modeling with Graph Transformation.- Improving Models and Understanding Model Changes.- Translating and Synchronizing Models.","brand":"Springer Nature Switzerland AG","offers":[{"title":"Default Title","offer_id":48743036059991,"sku":"9783030439156","price":75.99,"currency_code":"GBP","in_stock":true}]},{"product_id":"modelling-puzzles-in-first-order-logic-9783030625467","title":"Modelling Puzzles in First Order Logic","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eKeeping students involved and actively learning is challenging. Instructors in computer science are aware of the cognitive value of modelling puzzles and often use logical puzzles as an efficient pedagogical instrument to engage students and develop problem-solving skills. \u003c\/p\u003e  \u003cp\u003eThis unique book is a comprehensive resource that offers teachers and students fun activities to teach and learn logic. It provides new, complete, and running formalisation in Propositional and First Order Logic for over 130 logical puzzles, including Sudoku-like puzzles, zebra-like puzzles, island of truth, lady and tigers, grid puzzles, strange numbers, or self-reference puzzles.\u003c\/p\u003e  Solving puzzles with theorem provers can be an effective cognitive incentive to motivate students to learn logic. They will find a ready-to-use format which illustrates how to model each puzzle, provides running implementations, and explains each solution.\u003cp\u003e\u003c\/p\u003e  \u003cp\u003eThis concise and easy-to-follow textbook is a much-needed support tool for students willing to explore beyond the introductory level of learning logic and lecturers looking for examples to heighten student engagement in their computer science courses.  \u003c\/p\u003e  \u003cp\u003e \u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e“The purpose of this book is to introduce first-order logic (FOL) to newcomers. … The book is a treasure trove of puzzles like this. … All of these are motivated in an approachable, fun way. … the book is a hands-on guide to Prover9 and Mace4 … . It is quite valuable to have so many puzzles in a single book.” (Jesse Adam Alama, Mathematical Reviews, October, 2022)\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003ePreface.- Getting Started with Prover9 and Mace4.- Micro Arithmetic Puzzles.- Strange Numbers.- Practical Puzzles.- Lady and Tigers.- Einstein Puzzles.- Island of Truth.- Love and Marriage.- Grid Puzzles.- Japanese Puzzles.- Russian Puzzles.- Polyomino Puzzles.- Self-reference and Other Puzzles.- Epigraph in Natural Language","brand":"Springer Nature Switzerland AG","offers":[{"title":"Default Title","offer_id":48743042646359,"sku":"9783030625467","price":40.49,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9783030625467.jpg?v=1720063860"},{"product_id":"fundamentals-of-quantum-computing-theory-and-practice-9783030636883","title":"Fundamentals of Quantum Computing: Theory and","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eThis introductory book on quantum computing includes an emphasis on the development of algorithms.  Appropriate for both university students as well as software developers interested in programming a quantum computer, this practical approach to modern quantum computing takes the reader through the required background and up to the latest developments.\u003c\/p\u003e  \u003cp\u003eBeginning with introductory chapters on the required math and quantum mechanics, \u003ci\u003eFundamentals of Quantum Computing\u003c\/i\u003e proceeds to describe four leading qubit modalities and explains the core principles of quantum computing in detail. Providing a step-by-step derivation of math and source code, some of the well-known quantum algorithms are explained in simple ways so the reader can try them either on IBM Q or Microsoft QDK. The book also includes a chapter on adiabatic quantum computing and modern concepts such as topological quantum computing and surface codes.\u003c\/p\u003e\u003cp\u003eFeatures:\u003c\/p\u003e\u003cp\u003eo   Foundational chapters that build the necessary background on math and quantum mechanics.\u003c\/p\u003e\u003cp\u003eo   Examples and illustrations throughout provide a practical approach to quantum programming with end-of-chapter exercises.\u003c\/p\u003e\u003cp\u003eo   Detailed treatment on four leading qubit modalities -- trapped-ion, superconducting transmons, topological qubits, and quantum dots -- teaches how qubits work so that readers can understand how quantum computers work under the hood and devise efficient algorithms and error correction codes. Also introduces protected qubits - 0-π qubits, fluxon parity protected qubits, and charge-parity protected qubits.\u003c\/p\u003e\u003cp\u003e \u003c\/p\u003e\u003cp\u003eo   Principles of quantum computing, such as quantum superposition principle, quantum entanglement, quantum teleportation, no-cloning theorem, quantum parallelism, and quantum interference are explained in detail.  \u003cbr\u003e\u003c\/p\u003eA dedicated chapter on quantum algorithm explores both oracle-based, and Quantum Fourier Transform-based algorithms in detail with step-by-step math and working code that runs on IBM QisKit and Microsoft QDK. Topics on EPR Paradox, Quantum Key Distribution protocols, Density Matrix formalism, and Stabilizer formalism are intriguing. While focusing on the universal gate model of quantum computing, this book also introduces adiabatic quantum computing and quantum annealing.\u003cp\u003e\u003c\/p\u003e\u003cp\u003eThis book includes a section on fault-tolerant quantum computing to make the discussions complete. The topics on Quantum Error Correction, Surface codes such as Toric code and Planar code, and protected qubits help explain how fault tolerance can be built at the system level.\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e“The book represents a new and fresh approach to quantum computing, starting with theoretical physical knowledge that is highlighted by beautiful figures. Then, quantum computing is explained by quantum programing languages and extensive languages. It is recommended to everyone interested in quantum computing. It is easy to follow through a beautiful and clear presentation, programming examples and additional exercises.” (Andreas Wichert, zbMATH 1477.68005, 2022)\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003ePART ONE\t1 Foundations of Quantum Mechanics\t1.1 Matter\t1.2 Atoms, Elementary Particles, and Molecules\t1.3 Light and Quantization of Energy\t1.4 Electron Configuration\t1.5 Wave-Particle Duality and Probabilistic Nature\t1.6 Wavefunctions and Probability Amplitudes\t1.7 Some exotic states of matter\t1.8 Summary\t1.9 Practice Problems\t1.10 References and further reading\t2 Dirac’s bra-ket notation and Hermitian Operators2.1 Scalars\t2.2 Complex Numbers\t2.3 Vectors\t2.4 Matrices\t2.5 Linear Vector Spaces\t2.6 Using Dirac’s bra-ket notation\t2.7 Expectation Values and Variances2.8 Eigenstates, Eigenvalues and Eigenfunctions2.9 Characteristic Polynomial\t2.10 Definite Symmetric Matrices\t2.11 Tensors2.12 Statistics and Probability2.13 Summary\t2.14 Practice problems2.15 References and further reading3 The Quantum Superposition Principle and Bloch Sphere Representation3.1 Euclidian Space3.2 Metric Space3.3 Hilbert space.3.4 Schrodinger Equation3.5 Postulates of Quantum Mechanics3.6 Quantum Tunneling3.7 Stern and Gerlach Experiment3.8 Bloch sphere representation3.9 Projective Measurements3.10 Qudits3.11 Summary3.12 Practice Problems3.13 References and further readingPART TWO4 Qubit Modalities4.1 The vocabulary of quantum computing4.2 Classical Computers – a recap\t4.3 Qubits and usability4.4 Noisy Intermediate Scale Quantum Technology4.5 Qubit Metrics4.6 Leading Qubit Modalities4.7 A note on the dilution refrigerator4.8 Summary4.9 Practice Problems4.10 References and further reading5 Quantum Circuits and DiVincenzo Criteria5.1 Setting up the development environment5.2 Learning Quantum Programming Languages\t5.3 Introducing Quantum Circuits\t5.4 Quantum Gates\t5.5 The Compute Stage5.6 Quantum Entanglement5.7 No-Cloning theorem5.8 Quantum Teleportation5.9 Superdense coding5.10 Greenberger–Horne–Zeilinger state (GHZ state)5.11 Walsh-Hadamard Transform5.12 Quantum Interference5.13 Phase kickback5.14 DiVincenzo’s criteria for quantum computation5.15 Summary\t5.16 Practice Problems5.17 References and further reading6 Quantum Communications6.1 EPR Paradox6.2 Density Matrix Formalism6.3 Von Neumann Entropy6.4 Photons6.5 Quantum Communication6.6 The Quantum Channel6.7 Quantum Communication Protocols6.8 RSA Security6.9 Summary6.10 Practice Problems6.11 References and further reading7 Quantum Algorithms7.1 Quantum Ripple Adder Circuit7.2 Quantum Fourier Transformation7.3 Deutsch-Jozsa oracle7.4 The Bernstein-Vazirani Oracle7.5 Simon’s algorithm7.6 Quantum arithmetic using QFT7.7 Modular exponentiation7.8 Grover’s search algorithm\t7.9 Shor’s algorithm7.10 A quantum algorithm for k-means7.11 Quantum Phase Estimation (QPE)7.12 HHL algorithm for solving linear equations7.13 Quantum Complexity Theory7.14 Summary\t7.15 Practice Problems7.16 References and further reading8 Adiabatic Optimization and Quantum Annealing8.1 Adiabatic evolution8.2 Proof of the Adiabatic Theorem8.3 Adiabatic optimization8.4 Quantum Annealing8.5 Summary8.6 Practice Problems8.7 References and further reading9 Quantum Error Correction9.1 Classical Error Correction9.2 Quantum Error Codes9.3 Stabilizer formalism9.4 The path forward – fault-tolerant quantum computing9.5 Surface codes9.6 Protected qubits9.7 Practice Problems9.8 References and further reading10 Conclusion10.1 How many qubits do we need?10.2 Classical simulation10.3 Backends today10.4 Future state10.5 References\u003cbr\u003e","brand":"Springer Nature Switzerland AG","offers":[{"title":"Default Title","offer_id":48743043498327,"sku":"9783030636883","price":75.99,"currency_code":"GBP","in_stock":true}]},{"product_id":"multivariate-data-analysis-on-matrix-manifolds-with-manopt-9783030769734","title":"Multivariate Data Analysis on Matrix Manifolds:","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eThis graduate-level textbook aims to give a unified presentation and solution of several commonly used techniques for multivariate data analysis (MDA). Unlike similar texts, it treats the MDA problems as optimization problems on matrix manifolds defined by the MDA model parameters, allowing them to be solved using (free) optimization software Manopt. The book includes numerous in-text examples as well as Manopt codes and software guides, which can be applied directly or used as templates for solving similar and new problems. The first two chapters provide an overview and essential background for studying MDA, giving basic information and notations. Next, it considers several sets of matrices routinely used in MDA as parameter spaces, along with their basic topological properties. A brief introduction to matrix (Riemannian) manifolds and optimization methods on them with Manopt complete the MDA prerequisite. The remaining chapters study individual MDA techniques in depth. The number of exercises complement the main text with additional information and occasionally involve open and\/or challenging research questions. Suitable fields include computational statistics, data analysis, data mining and data science, as well as theoretical computer science, machine learning and optimization. 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Beginning with an introduction to the mathematisation of “mechanical process” using URM programs, this textbook explains basic theory such as primitive recursive functions and predicates and sequence-coding, partial recursive functions and predicates, and loop programs. \u003c\/p\u003e\u003cp\u003eAdvanced chapters cover the Ackerman function, Tarski’s theorem on the non-representability of truth, Goedel’s incompleteness and Rosser’s incompleteness theorems, two short proofs of the incompleteness theorem that are based on Lob's deliverability conditions, Church’s thesis, the second recursion theorem and applications, a provably recursive universal function for the primitive recursive functions, Oracle computations and various classes of computable functionals, the Arithmetical hierarchy, Turing reducibility and Turing degrees and the priority method, a thorough exposition of various versions of the first recursive theorem, Blum’s complexity, Hierarchies of primitive recursive functions, and a machine-independent characterisation of Cobham's feasibly computable functions.\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e“This textbook is suited for self-study … . As a second reading however a reader interested in rigorous proofs and\/or different approaches to known concepts will benefit from this wealth of material.” (Dieter Riebesehl, zbMATH 1507.03002, 2023)\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003eMathematical Background; a Review.- A Theory of Computability.- Primitive Recursive Functions.- Loop Programs.-The Ackermann Function.- (Un)Computability via Church's Thesis.- Semi-Recursiveness.- Yet another number-theoretic characterisation of P.- Godel's Incompleteness Theorem via the Halting Problem.- The Recursion Theorem.- A Universal (non-PR) Function for PR.- Enumerations of Recursive and Semi-Recursive Sets.- Creative and Productive Sets Completeness.- Relativised Computability.- POSSIBILITY: Complexity of P Functions.- Complexity of PR Functions.- Turing Machines and NP-Completeness.","brand":"Springer Nature Switzerland AG","offers":[{"title":"Default Title","offer_id":48743052575063,"sku":"9783030832018","price":71.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9783030832018.jpg?v=1720063903"},{"product_id":"computability-9783030832049","title":"Computability","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eThis survey of computability theory offers the techniques and tools that computer scientists (as well as mathematicians and philosophers studying the mathematical foundations of computing) need to mathematically analyze computational processes and investigate the theoretical limitations of computing. Beginning with an introduction to the mathematisation of “mechanical process” using URM programs, this textbook explains basic theory such as primitive recursive functions and predicates and sequence-coding, partial recursive functions and predicates, and loop programs. \u003c\/p\u003e\u003cp\u003eAdvanced chapters cover the Ackerman function, Tarski’s theorem on the non-representability of truth, Goedel’s incompleteness and Rosser’s incompleteness theorems, two short proofs of the incompleteness theorem that are based on Lob's deliverability conditions, Church’s thesis, the second recursion theorem and applications, a provably recursive universal function for the primitive recursive functions, Oracle computations and various classes of computable functionals, the Arithmetical hierarchy, Turing reducibility and Turing degrees and the priority method, a thorough exposition of various versions of the first recursive theorem, Blum’s complexity, Hierarchies of primitive recursive functions, and a machine-independent characterisation of Cobham's feasibly computable functions.\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e“This textbook is suited for self-study … . As a second reading however a reader interested in rigorous proofs and\/or different approaches to known concepts will benefit from this wealth of material.” (Dieter Riebesehl, zbMATH 1507.03002, 2023)\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003eMathematical Background; a Review.- A Theory of Computability.- Primitive Recursive Functions.- Loop Programs.-The Ackermann Function.- (Un)Computability via Church's Thesis.- Semi-Recursiveness.- Yet another number-theoretic characterisation of P.- Godel's Incompleteness Theorem via the Halting Problem.- The Recursion Theorem.- A Universal (non-PR) Function for PR.- Enumerations of Recursive and Semi-Recursive Sets.- Creative and Productive Sets Completeness.- Relativised Computability.- POSSIBILITY: Complexity of P Functions.- Complexity of PR Functions.- Turing Machines and NP-Completeness.","brand":"Springer Nature Switzerland AG","offers":[{"title":"Default Title","offer_id":48743052673367,"sku":"9783030832049","price":52.24,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9783030832049.jpg?v=1720063903"},{"product_id":"algebraic-graph-algorithms-a-practical-guide-using-python-9783030878857","title":"Algebraic Graph Algorithms: A Practical Guide Using Python","description":"\u003cp\u003eThis textbook discusses the design and implementation of basic algebraic graph algorithms, and algebraic graph algorithms for complex networks, employing matroids whenever possible. The text describes the design of a simple parallel matrix algorithm kernel that can be used for parallel processing of algebraic graph algorithms. Example code is presented in pseudocode, together with case studies in Python and MPI. The text assumes readers have a background in graph theory and\/or graph algorithms.\u003c\/p\u003e","brand":"Springer Nature Switzerland AG","offers":[{"title":"Default Title","offer_id":48743056114007,"sku":"9783030878856","price":32.99,"currency_code":"GBP","in_stock":true}]},{"product_id":"understanding-computation-pillars-paradigms-principles-9783031100574","title":"Understanding Computation: Pillars, Paradigms,","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eComputation theory is a discipline that uses mathematical concepts and tools to expose the nature of \"computation\" and to explain a broad range of computational phenomena: Why is it harder to perform some computations than others?  Are the differences in difficulty that we observe inherent, or are they artifacts of the way we try to perform the computations?  How does one reason about such questions?\u003c\/p\u003e  \u003cp\u003eThis unique textbook strives to endow students with conceptual and manipulative tools necessary to make computation theory part of their professional lives. The work achieves this goal by means of three stratagems that set its approach apart from most other texts on the subject.\u003c\/p\u003e  \u003cp\u003eFor starters, it develops the necessary mathematical concepts and tools from the concepts' simplest instances, thereby helping students gain operational control over the required mathematics. Secondly, it organizes development of theory around four \"pillars,\" enabling students to see computational topics that have the same intellectual origins in physical proximity to one another. Finally, the text illustrates the \"big ideas\" that computation theory is built upon with applications of these ideas within \"practical\" domains in mathematics, computer science, computer engineering, and even further afield.\u003c\/p\u003e  \u003cp\u003eSuitable for advanced undergraduate students and beginning graduates, this textbook augments the \"classical\" models that traditionally support courses on computation theory with novel models inspired by \"real, modern\" computational topics,such as  crowd-sourced computing, mobile computing, robotic path planning, and volunteer computing.\u003c\/p\u003e  \u003cp\u003e\u003cb\u003eArnold L. Rosenberg\u003c\/b\u003e is Distinguished Univ. Professor Emeritus at University of Massachusetts, Amherst, USA. \u003cb\u003eLenwood S. Heath\u003c\/b\u003e is Professor at Virgina Tech, Blacksburg, USA.            \u003cbr\u003e\u003c\/p\u003e\u003cbr\u003e\u003cp\u003e\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003ePreface.-  I: Introduction.- 1 Introducing Computation Theory.- 2 Introducing the Book.- II: Pillar S: STATE.- 3 Pure State-Based Computational Models.- 4 The Myhill-Nerode Theorem: Implications and Applications.- 5 Online Turing Machines and the Implications of \u003ci\u003eOnline\u003c\/i\u003e Computing.- 6 Pumping: Computational Pigeonholes in Finitary Systems.- 7 Mobility in Computing: An FA Navigates a Mesh.- 8 The Power of Cooperation: Teams of MFAs on a Mesh.- III: Pillar E: ENCODING.- 9 Countability and Uncountability: The Precursors of \u003ci\u003eENCODING\u003c\/i\u003e.- 10 Computability Theory.- 11 A Church-Turing Zoo of Computational Models.- 12 Pairing Functions as Encoding Mechanisms.- IV: Pillar N: NONDETERMINISM.- 13 Nondeterminism as Unbounded Parallelism.- 14 Nondeterministic Finite Automata.- 15 Nondeterminism as Unbounded Search.- 16 Complexity Theory.- V: Pillar P: PRESENTATION\/SPECIFICATION.- 17 The Elements of Formal Language Theory.- A A Chapter-Long Text on Discrete Mathematics.- B Selected Exercises, by Chapter.- List of ACRONYMS and SYMBOLS.- References.- Index.\u003c\/p\u003e","brand":"Springer International Publishing AG","offers":[{"title":"Default Title","offer_id":48743069253975,"sku":"9783031100574","price":62.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9783031100574.jpg?v=1720063974"},{"product_id":"introduction-to-combinatorial-optimization-9783031105944","title":"Introduction to Combinatorial Optimization","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eIntroductory courses in combinatorial optimization are popular at the upper undergraduate\/graduate levels in computer science, industrial engineering, and business management\/OR, owed to its wide applications in these fields. There are several published textbooks that treat this course and the authors have used many of them in their own teaching experiences.  This present text fills a gap and is organized with a stress on methodology and relevant content, providing a step-by-step approach for the student to become proficient in solving combinatorial optimization problems. Applications and problems are considered via recent technology developments including wireless communication, cloud computing, social networks, and machine learning, to name several, and the reader is led to the frontiers of combinatorial optimization. Each chapter presents common problems, such as minimum spanning tree, shortest path, maximum matching, network flow, set-cover, as well as key algorithms, such as greedy algorithm, dynamic programming, augmenting path, and divide-and-conquer. Historical notes, ample exercises in every chapter, strategically placed graphics, and an extensive bibliography are amongst the gems of this textbook.\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e“This book introduces combinatorial optimization with a methodology-oriented organization. It targets undergraduate and graduate students and contains a good mix of theoretical results (with proof) and examples, which helps the reader acquire ideas and concepts. The chapters end with a list of exercises for the students.” (Francisco Chicano, Mathematical Reviews, January, 2024)\u003cbr\u003e“The book can appropriately be used as a textbook in a graduate course. All the algorithms are clearly explained and presented. It is a very valuable book for successful application of real problems from combinatorial optimization. … this book is an excellent contribution to the field of combinatorial optimization, and it is highly recommended to the students and researchers in optimization.” (Samir Kumar Neogy, zbMATH 1512.90001, 2023)\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e1. Introduction.-2. Divide-and-Conquer.- 3. Dynamic Programming and Shortest Path.- 4. Greedy Algorithm and Spanning Tree.- 5. Incremental Method and Maximum Network Flow.- 6. Linear Programming.- 7. Primal-Dual Methods and Minimum Cost Flow.- 8. NP-hard Problems and Approximation Algorithms.- 9. Restriction and Steiner Tree.- 10. Greedy Approximation and Submodular Optimization.- 11. Relaxation and Rounding. 12. Nonsubmodular Optimization.- Bibliography.","brand":"Springer International Publishing AG","offers":[{"title":"Default Title","offer_id":48743069811031,"sku":"9783031105944","price":38.24,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9783031105944.jpg?v=1720063980"},{"product_id":"introduction-to-combinatorial-optimization-9783031116841","title":"Introduction to Combinatorial Optimization","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eIntroductory courses in combinatorial optimization are popular at the upper undergraduate\/graduate levels in computer science, industrial engineering, and business management\/OR, owed to its wide applications in these fields. There are several published textbooks that treat this course and the authors have used many of them in their own teaching experiences.  This present text fills a gap and is organized with a stress on methodology and relevant content, providing a step-by-step approach for the student to become proficient in solving combinatorial optimization problems. Applications and problems are considered via recent technology developments including wireless communication, cloud computing, social networks, and machine learning, to name several, and the reader is led to the frontiers of combinatorial optimization. Each chapter presents common problems, such as minimum spanning tree, shortest path, maximum matching, network flow, set-cover, as well as key algorithms, such as greedy algorithm, dynamic programming, augmenting path, and divide-and-conquer. Historical notes, ample exercises in every chapter, strategically placed graphics, and an extensive bibliography are amongst the gems of this textbook.\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e“This book introduces combinatorial optimization with a methodology-oriented organization. It targets undergraduate and graduate students and contains a good mix of theoretical results (with proof) and examples, which helps the reader acquire ideas and concepts. The chapters end with a list of exercises for the students.” (Francisco Chicano, Mathematical Reviews, January, 2024)\u003cbr\u003e“The book can appropriately be used as a textbook in a graduate course. All the algorithms are clearly explained and presented. It is a very valuable book for successful application of real problems from combinatorial optimization. … this book is an excellent contribution to the field of combinatorial optimization, and it is highly recommended to the students and researchers in optimization.” (Samir Kumar Neogy, zbMATH 1512.90001, 2023)\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e1. Introduction.-2. Divide-and-Conquer.- 3. Dynamic Programming and Shortest Path.- 4. Greedy Algorithm and Spanning Tree.- 5. Incremental Method and Maximum Network Flow.- 6. Linear Programming.- 7. Primal-Dual Methods and Minimum Cost Flow.- 8. NP-hard Problems and Approximation Algorithms.- 9. Restriction and Steiner Tree.- 10. Greedy Approximation and Submodular Optimization.- 11. Relaxation and Rounding. 12. Nonsubmodular Optimization.- Bibliography.","brand":"Springer International Publishing AG","offers":[{"title":"Default Title","offer_id":48743070630231,"sku":"9783031116841","price":38.24,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9783031116841.jpg?v=1720063981"},{"product_id":"algorithms-and-discrete-applied-mathematics-9th-international-conference-caldam-2023-gandhinagar-india-february-9-11-2023-proceedings-9783031252105","title":"Algorithms and Discrete Applied Mathematics: 9th","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eThis book constitutes the proceedings of the 9th International Conference on Algorithms and Discrete Applied Mathematics, CALDAM 2023, which was held in Gandhinagar, India, during February 9-11, 2023.\u003cp\u003eThe 32 papers presented in this volume were carefully reviewed and selected from 67 submissions. The papers were organized in topical sections named: algorithms and optimization; computational geometry; game theory; graph coloring; graph connectivity; graph domination; graph matching; graph partition and graph covering.\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003eStable Approximation Schemes.- A whirlwind tour of intersection graph enumeration.- Graph modification problems with forbidden minors.- Algorithms \u0026amp; Optimization Efficient reductions and algorithms for Subset Product.- Optimal length cutting plane refutations of integer programs.- Fault-Tolerant Dispersion Resource management in device-to-device communications.- Computational Geometry Algorithms for k-Dispersion for Points in Convex Position in the Plane.- Arbitrary oriented color spanning region for line segments.- Games with a Simple Rectilinear Obstacle in Plane.- Diverse Fair Allocations: Complexity and Algorithms.- Graph Coloring New bounds and constructions for neighbor-locating colorings of graphs.- D K 5-list coloring toroidal 6-regular triangulations in linear time.- On Locally Identifying Coloring of Graphs.- On Structural Parameterizations of Star Coloring.- Reddy Perfectness of G-generalized join of graphs.- Coloring of a superclass of 2K2-free graphs.- The Weak (2,2)-Labelling Problem for graphs with forbidden induced structures.- Graph Connectivity Short cycles dictate dichotomy status of the Steiner tree problem on Bisplit graphs.- Some insights on dynamic maintenance of Gomory-Hu tree in cactus graphs and general graphs.- Monitoring edge-geodetic sets in graphs.- Cyclability, Connectivity and Circumference.- Graph Domination On three domination-based identification problems in block graphs.- Graph modification problems with forbidden minors.- Computational Aspects of Double Dominating Sequences in Graph.- Relation between broadcast domination and multipacking numbers on chordal graphs.- Pushing Cops and Robber on Oriented Graphs.- Mind the Gap: Edge Facility Location Problems in Theory and Practice.- Complexity Results on Cosecure Domination in Graphs.- Kusum and Arti Pandey Graph Matching Latin Hexahedra and Related Combinatorial Structures.- Minimum Maximal Acyclic Matching in Proper Interval Graphs.- Graph Partition \u0026amp; Graph Covering Transitivity on subclasses of chordal graphs.- Maximum subgraph problem for 3-regular Knödel graphs and its wirelength.- Covering using Bounded Size Subgraphs.- Axiomatic characterization of the the toll walk function of some graph classes.- Structural Parameterization of Alliance Problems.","brand":"Springer International Publishing AG","offers":[{"title":"Default Title","offer_id":48743077249367,"sku":"9783031252105","price":61.74,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9783031252105.jpg?v=1720064009"},{"product_id":"mathematical-principles-of-topological-and-geometric-data-analysis-9783031334399","title":"Mathematical Principles of Topological and","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eThis book explores and demonstrates how geometric tools can be used in data analysis. Beginning with a systematic exposition of the mathematical prerequisites, covering topics ranging from category theory to algebraic topology, Riemannian geometry, operator theory and network analysis, it goes on to describe and analyze some of the most important machine learning techniques for dimension reduction, including the different types of manifold learning and kernel methods. It also develops a new notion of curvature of generalized metric spaces, based on the notion of hyperconvexity, which can be used for the topological representation of geometric information.\u003c\/p\u003e\u003cp\u003eIn recent years there has been a fascinating development: concepts and methods originally created in the context of research in pure mathematics, and in particular in geometry, have become powerful tools in machine learning for the analysis of data. The underlying reason for this is that data are typically equipped with some kind of notion of distance, quantifying the differences between data points. Of course, to be successfully applied, the geometric tools usually need to be redefined, generalized, or extended appropriately.\u003c\/p\u003e\u003cp\u003ePrimarily aimed at mathematicians seeking an overview of the geometric concepts and methods that are useful for data analysis, the book will also be of interest to researchers in machine learning and data analysis who want to see a systematic mathematical foundation of the methods that they use.\u003c\/p\u003e\u003cp\u003e \u003c\/p\u003e\u003cp\u003e\u003cbr\u003e\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eIntroduction.- Topological foundations, hypercomplexes and homology.- Weighted complexes, cohomology and Laplace operators.- The Laplace operator and the geometry of graphs.- Metric spaces and manifolds.- Linear methods: Kernels, variations, and averaging.- Nonlinear schemes: Clustering, feature extraction and dimension reduction.- Manifold learning, the scheme of Laplacian eigenmaps.- Metrics and curvature.\u003c\/p\u003e\u003cp\u003e\u003cbr\u003e\u003c\/p\u003e","brand":"Springer International Publishing AG","offers":[{"title":"Default Title","offer_id":48743082164567,"sku":"9783031334399","price":53.99,"currency_code":"GBP","in_stock":true}]},{"product_id":"don-pigozzi-on-abstract-algebraic-logic-universal-algebra-and-computer-science-9783319747712","title":"Don Pigozzi on Abstract Algebraic Logic,","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eThis book celebrates the work of Don Pigozzi on the occasion of his 80th birthday. In addition to articles written by leading specialists and his disciples, it presents Pigozzi’s scientific output and discusses his impact on the development of science. The book both catalogues his works and offers an extensive profile of Pigozzi as a person, sketching the most important events, not only related to his scientific activity, but also from his personal life.\u003c\/p\u003e  \u003cp\u003eIt reflects Pigozzi's contribution to the rise and development of areas such as abstract algebraic logic (AAL), universal algebra and computer science, and introduces new scientific results. Some of the papers also present chronologically ordered facts relating to the development of the disciplines he contributed to, especially abstract algebraic logic. The book offers valuable source material for historians of science, especially those interested in history of mathematics and logic.\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003eA Mathematical Life; Pigozzi, Don.- Assertional logics, truth-equational logics, and the hierarchiesof abstract algebraic logic; Albuquerque, Hugo, Font, Josep Maria, Jansana, Ramon, and Moraschini, Tommaso.- Deduction-Detachment Theorem and Gentzen-Style Deductive Systems; Babenyshev, Sergey.- Introducing Boolean Semilattices; Bergman, Clifford.- The Equationally-Defined Commutator in Quasivarieties Generated by Two-Element Algebras; Czelakowski, Janusz.- A short overview of Hidden Logic; Ferreirim, Isabel and Martins, Manuel A.- Absorption and directed J´onsson terms; Kazda, Alexandr, Kozik, Marcin, McKenzie, Ralph and Moore, Matthew.- Relatively congruence modular quasivarieties of modules; Kearnes, Keith A. - The computational complexity of deciding whether a finite algebra generates a minimal variety; McNulty, George F.- Characterization of protoalgebraic \u003ci\u003ek\u003c\/i\u003e-deductive systems; Palasinska; Katarzyna.- Diagrammatic duality; Romanowska, Anna B. and Smith, Jonathan D.H.- Boolean product representations of algebras via binary polynomials; Salibra, Antonino, Ledda, Antonio, and Paoli, Francesco.- Paraconsistent constructive logic with strong negation as a contraction-free relevant logic; Spinks, Matthew and Veroff, Robert.- Possible classification of finite-dimensional compact Hausdorfftopological algebras; Taylor, Walter.- Categorical Abstract Algebraic Logic: Compatibility Operators and Correspondence Theorems; Voutsadakis; George.","brand":"Springer International Publishing AG","offers":[{"title":"Default Title","offer_id":48743104282967,"sku":"9783319747712","price":82.49,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9783319747712.jpg?v=1720064130"},{"product_id":"computational-thinking-a-perspective-on-computer-science-9789811638473","title":"Computational Thinking: A Perspective on Computer","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eThis textbook is intended as a textbook for one-semester, introductory computer science courses aimed at undergraduate students from all disciplines. Self-contained and with no prerequisites, it focuses on elementary knowledge and thinking models. The content has been tested in university classrooms for over six years, and has been used in summer schools to train university and high-school teachers on teaching introductory computer science courses using computational thinking.\u003c\/p\u003e  \u003cp\u003eThis book introduces computer science from a computational thinking perspective. In computer science the way of thinking is characterized by three external and eight internal features, including automatic execution, bit-accuracy and abstraction. The book is divided into chapters on logic thinking, algorithmic thinking, systems thinking, and network thinking. It also covers societal impact and responsible computing material – from ICT industry to digital economy, from the wonder of exponentiation to wonder of cyberspace, and from code of conduct to best practices for independent work.\u003c\/p\u003e  \u003cp\u003eThe book’s structure encourages active, hands-on learning using the pedagogic tool Bloom's taxonomy to create computational solutions to over 200 problems of varying difficulty. Students solve problems using a combination of thought experiment, programming, and written methods. Only 300 lines of code in total are required to solve most programming problems in this book.\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e“The book companion website includes the course’s lecture and project slides as well as Go source code. … Xu and Zhang validate the view that it is indeed more concerned with … CS instruction itself and provides an effective framework for teaching the subject from introductory to advanced courses. Thus they agree with other notable CT researchers and practitioners, such as Denning and Tedre [1], and their textbook is a most valuable contribution to CS education.” (Harry J. Foxwell, Computing Reviews, October 12, 2022)\u003cbr\u003e“The book has a companion website from which readers can pull down over 200 MB of zip files with lecture notes, lab notes … and project notes. This is a puzzling book in some respects. On the one hand it covers basic concepts and terminology for the beginning student, yet on the other hand plunges into sophisticated topics without drawing a breadth. Likewise, it claims that programing experience is not a prerequisite … .” (Anthony J. Duben, Computing Reviews, August 30, 2022)\u003cbr\u003e“The most preeminent characteristic of this book is its ‘thinking’-perspective, which the reader may or may not like, and which perhaps leads to a suboptimal arrangements of topics. … the material presented is impressive, and at least the fundamentals are covered in great detail.” (Dieter Riebesehl, zbMATH 1490.68001, 2022)\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e1 Overview of Computer Science.-2 Processes of Digital Symbol Manipulation.- 3 Logic Thinking.- 4 Algorithmic Thinking.- 5 Systems Thinking.- 6 Network Thinking.- 7 Projects.- 8 Appendices.","brand":"Springer Verlag, Singapore","offers":[{"title":"Default Title","offer_id":48743290569047,"sku":"9789811638473","price":999.99,"currency_code":"GBP","in_stock":false}]},{"product_id":"multivariate-nonparametric-regression-and-visualization-9780470384428","title":"Multivariate Nonparametric Regression and","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eCovering classification and regression,   Statistical Learning is the first of its kind to use visualization techniques to identify, test, and analyze classifiers for their most accurate exploration of data.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e“Altogether, the book provides a very nice overview of nonparametric and semiparametric regression methods with interesting applications to problems in quantitative finance.”  (\u003ci\u003eMathematical Reviews\u003c\/i\u003e, 1 October 2015)\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003ePreface xvii\u003c\/p\u003e \u003cp\u003eIntroduction xix\u003c\/p\u003e \u003cp\u003eI.1 Estimation of Functionals of Conditional Distributions xx\u003c\/p\u003e \u003cp\u003eI.2 Quantitative Finance xxi\u003c\/p\u003e \u003cp\u003eI.3 Visualization xxi\u003c\/p\u003e \u003cp\u003eI.4 Literature xxiii\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePART I METHODS OF REGRESSION AND CLASSIFICATION\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Overview of Regression and Classification 3\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 Regression 3\u003c\/p\u003e \u003cp\u003e1.2 Discrete Response Variable 29\u003c\/p\u003e \u003cp\u003e1.3 Parametric Family Regression 33\u003c\/p\u003e \u003cp\u003e1.4 Classification 37\u003c\/p\u003e \u003cp\u003e1.5 Applications in Quantitative Finance 42\u003c\/p\u003e \u003cp\u003e1.6 Data Examples 52\u003c\/p\u003e \u003cp\u003e1.7 Data Transformations 53\u003c\/p\u003e \u003cp\u003e1.8 Central Limit Theorems 58\u003c\/p\u003e \u003cp\u003e1.9 Measuring the Performance of Estimators 61\u003c\/p\u003e \u003cp\u003e1.10 Confidence Sets 73\u003c\/p\u003e \u003cp\u003e1.11 Testing 75\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Linear Methods and Extensions 77\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 Linear Regression 78\u003c\/p\u003e \u003cp\u003e2.2 Varying Coefficient Linear Regression 97\u003c\/p\u003e \u003cp\u003e2.3 Generalized Linear and Related Models 102\u003c\/p\u003e \u003cp\u003e2.4 Series Estimators 107\u003c\/p\u003e \u003cp\u003e2.5 Conditional Variance and ARCH models 111\u003c\/p\u003e \u003cp\u003e2.6 Applications in Volatility and Quantile Estimation 115\u003c\/p\u003e \u003cp\u003e2.7 Linear Classifiers 124\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Kernel Methods and Extensions 127\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Regressogram 129\u003c\/p\u003e \u003cp\u003e3.2 Kernel Estimator 130\u003c\/p\u003e \u003cp\u003e3.3 Nearest Neighborhood Estimator 147\u003c\/p\u003e \u003cp\u003e3.4 Classification with Local Averaging 148\u003c\/p\u003e \u003cp\u003e3.5 Median Smoothing 151\u003c\/p\u003e \u003cp\u003e3.6 Conditional Density Estimators 152\u003c\/p\u003e \u003cp\u003e3.7 Conditional Distribution Function Estimation 158\u003c\/p\u003e \u003cp\u003e3.8 Conditional Quantile Estimation 160\u003c\/p\u003e \u003cp\u003e3.9 Conditional Variance Estimation 162\u003c\/p\u003e \u003cp\u003e3.10 Conditional Covariance Estimation 176\u003c\/p\u003e \u003cp\u003e3.11 Applications in Risk Management 181\u003c\/p\u003e \u003cp\u003e3.12 Applications in Portfolio Selection 205\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Semiparametric and Structural Models 229\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Single Index Model 230\u003c\/p\u003e \u003cp\u003e4.2 Additive Model 234\u003c\/p\u003e \u003cp\u003e4.3 Other Semiparametric Models 237\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Empirical Risk Minimization 241\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Empirical Risk 243\u003c\/p\u003e \u003cp\u003e5.2 Local Empirical Risk 247\u003c\/p\u003e \u003cp\u003e5.3 Support Vector Machines 257\u003c\/p\u003e \u003cp\u003e5.4 Stagewise Methods 259\u003c\/p\u003e \u003cp\u003e5.5 Adaptive Regressograms 264\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePART II VISUALIZATION\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Visualization of Data 277\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Scatter Plots 278\u003c\/p\u003e \u003cp\u003e6.2 Histogram and Kernel Density Estimator 282\u003c\/p\u003e \u003cp\u003e6.3 Dimension Reduction 284\u003c\/p\u003e \u003cp\u003e6.4 Observations as Objects 288\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Visualization of Functions 295\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Slices 296\u003c\/p\u003e \u003cp\u003e7.2 Partial Dependence Functions 296\u003c\/p\u003e \u003cp\u003e7.3 Reconstruction of Sets 299\u003c\/p\u003e \u003cp\u003e7.4 Level Set Trees 303\u003c\/p\u003e \u003cp\u003e7.5 Unimodal Densities 326\u003c\/p\u003e \u003cp\u003e7.5.1 Probability Content of Level Sets 327\u003c\/p\u003e \u003cp\u003e7.5.2 Set Visualization 328\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAppendix A: R Tutorial 329\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eA.1 Data Visualization 329\u003c\/p\u003e \u003cp\u003eA.2 Linear Regression 331\u003c\/p\u003e \u003cp\u003eA.3 Kernel Regression 332\u003c\/p\u003e \u003cp\u003eA.4 Local Linear Regression 341\u003c\/p\u003e \u003cp\u003eA.5 Additive Models: Backfitting 344\u003c\/p\u003e \u003cp\u003eA.6 Single Index Regression 345\u003c\/p\u003e \u003cp\u003eA.7 Forward Stagewise Modeling 347\u003c\/p\u003e \u003cp\u003eA.8 Quantile Regression 349\u003c\/p\u003e \u003cp\u003eReferences 351\u003c\/p\u003e \u003cp\u003eAuthor Index 361\u003c\/p\u003e \u003cp\u003eTopic Index 365\u003c\/p\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":48864626966871,"sku":"9780470384428","price":91.76,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780470384428.jpg?v=1722272793"},{"product_id":"programming-in-haskell-9781316626221","title":"Programming in Haskell","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eHaskell is a purely functional language that allows programmers to rapidly develop clear, concise, and correct software. The language has grown in popularity in recent years, both in teaching and in industry. This book is based on the author''s experience of teaching Haskell for more than twenty years. All concepts are explained from first principles and no programming experience is required, making this book accessible to a broad spectrum of readers. While Part I focuses on basic concepts, Part II introduces the reader to more advanced topics. This new edition has been extensively updated and expanded to include recent and more advanced features of Haskell, new examples and exercises, selected solutions, and freely downloadable lecture slides and example code. The presentation is clean and simple, while also being fully compliant with the latest version of the language, including recent changes concerning applicative, monadic, foldable, and traversable types.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e'The skills you acquire by studying this book will make you a much better programmer no matter what language you use to actually program in.' Erik Meijer, Facebook, from the Foreword\u003cbr\u003eReview of previous edition: 'The best introduction to Haskell available. There are many paths towards becoming comfortable and competent with the language but I think studying this book is the quickest path. I urge readers of this magazine to recommend Programming in Haskell to anyone who has been thinking about learning the language.' Duncan Coutts, The Monad.Reader\u003cbr\u003eReview of previous edition: 'Where this book excels is in the order and style of its exposition … With its ripe selection of examples and its careful clarity of exposition, the book is a welcome addition to the introductory functional programming literature.' Journal of Functional Programming\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003eForeword; Preface; Part I. Basic Concepts: 1. Introduction; 2. First steps; 3. Types and classes; 4. Defining functions; 5. List comprehensions; 6. Recursive functions; 7. Higher-order functions; 8. Declaring types and classes; 9. The countdown problem; Part II. Going Further: 10. Interactive programming; 11. Unbeatable tic-tac-toe; 12. Monads and more; 13. Monadic parsing; 14. Foldables and friends; 15. Lazy evaluation; 16. Reasoning about programs; 17. Calculating compilers; Appendix A. Selected solutions; Appendix B. Standard prelude; Bibliography; Index.","brand":"Cambridge University Press","offers":[{"title":"Default Title","offer_id":48866553856343,"sku":"9781316626221","price":33.24,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781316626221.jpg?v=1722279194"},{"product_id":"effective-devops-9781491926307","title":"Effective DevOps","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eSome companies think that adopting devops means bringing in specialists or a host of new tools. With this practical guide, you'll learn why devops is a professional and cultural movement that calls for change from inside your organization.","brand":"O'Reilly Media","offers":[{"title":"Default Title","offer_id":48867304669527,"sku":"9781491926307","price":31.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781491926307.jpg?v=1722282697"},{"product_id":"deep-learning-with-r-second-edition-9781633439849","title":"Deep Learning with R, Second Edition","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cb\u003eDeep learning from the ground up using R and the powerful Keras library!\u003c\/b\u003e   \u003cbr\u003e   \u003cbr\u003eIn       \u003ci\u003eDeep Learning with R, Second Edition\u003c\/i\u003e    you will learn:   \u003cbr\u003e   \u003cbr\u003e   \u003cul\u003e\n\u003cli\u003eDeep learning from first principles\u003c\/li\u003e\n\u003cli\u003eImage classification and image segmentation\u003c\/li\u003e\n\u003cli\u003eTime series forecasting\u003c\/li\u003e\n\u003cli\u003eText classification and machine translation\u003c\/li\u003e\n\u003cli\u003eText generation, neural style transfer, and image generation\u003c\/li\u003e\n\u003c\/ul\u003e   \u003ci\u003eDeep Learning with R, Second Edition\u003c\/i\u003e    shows you how to put deep learning into action. It's based on the revised new edition of François Chollet's bestselling       \u003ci\u003eDeep Learning with Python\u003c\/i\u003e. All code and examples have been expertly translated to the R language by Tomasz Kalinowski, who maintains the Keras and Tensorflow R packages at RStudio. Novices and experienced ML practitioners will love the expert insights, practical techniques, and important theory for building neural networks.      about the technology  Deep learning has become essential knowledge for data scientists, researchers, and software developers. The R language APIs for Keras and TensorFlow put deep learning within reach for all R users, even if they have no experience with advanced machine learning or neural networks. This book shows you how to get started on core DL tasks like computer vision, natural language processing, and more using R.       what's inside      \u003cul\u003e\n\u003cli\u003eImage classification and image segmentation\u003c\/li\u003e\n\u003cli\u003eTime series forecasting\u003c\/li\u003e\n\u003cli\u003eText classification and machine translation\u003c\/li\u003e\n\u003cli\u003eText generation, neural style transfer, and image generation\u003c\/li\u003e\n\u003c\/ul\u003e       about the reader   For readers with intermediate R skills. No previous experience with Keras, TensorFlow, or deep learning is required.       ","brand":"Manning Publications","offers":[{"title":"Default Title","offer_id":48867861758295,"sku":"9781633439849","price":41.39,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781633439849.jpg?v=1722285316"},{"product_id":"the-algorithm-design-manual-9783030542559","title":"The Algorithm Design Manual","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e\"My absolute favorite for this kind of interview preparation is Steven Skiena’s The Algorithm Design Manual. More than any other book it helped me understand just how astonishingly commonplace … graph problems are -- they should be part of every working programmer’s toolkit. The book also covers basic data structures and sorting algorithms, which is a nice bonus. … every 1 – pager has a simple picture, making it easy to remember. This is a great way to learn how to identify hundreds of problem types.\" (Steve Yegge, Get that Job at Google)\u003c\/p\u003e\u003cp\u003e\"Steven Skiena’s Algorithm Design Manual retains its title as the best and most comprehensive practical algorithm guide to help identify and solve problems. … Every programmer should read this book, and anyone working in the field should keep it close to hand. … This is the best investment … a programmer or aspiring programmer can make.\" (Harold Thimbleby, Times Higher Education)\u003c\/p\u003e\u003cp\u003e\"It is wonderful to open to a random spot and discover an interesting algorithm. This is the only textbook I felt compelled to bring with me out of my student days.... The color really adds a lot of energy to the new edition of the book!\" (Cory Bart, University of Delaware)\u003c\/p\u003e\u003cp\u003e\"The is the most approachable book on algorithms I have.\"   (Megan Squire, Elon University)\u003c\/p\u003e\u003cp\u003e---\u003c\/p\u003e\u003cp\u003eThis newly expanded and updated third edition of the best-selling classic continues to take the \"mystery\" out of designing algorithms, and analyzing their efficiency.  It serves as the primary textbook of choice for algorithm design courses and interview self-study, while maintaining its status as the premier practical reference guide to algorithms for programmers, researchers, and students.\u003cbr\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e \u003c\/p\u003e\u003cp\u003eThe reader-friendly \u003cb\u003e\u003ci\u003eAlgorithm Design Manual\u003c\/i\u003e\u003c\/b\u003e provides straightforward access to combinatorial algorithms technology, stressing design over analysis.  The first part, Practical Algorithm Design, provides accessible instruction on methods for designing and analyzing computer algorithms.  The second part, the Hitchhiker's Guide to Algorithms, is intended for browsing and reference, and comprises the catalog of algorithmic resources, implementations, and an extensive bibliography. \u003c\/p\u003e\u003cp\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003e\u003cb\u003eNEW \u003c\/b\u003eto the third edition: \u003c\/p\u003e\u003cp\u003e-- \u003cb\u003eNew and expanded coverage\u003c\/b\u003e of randomized algorithms, hashing, divide and conquer, approximation algorithms, and quantum computing \u003c\/p\u003e\u003cp\u003e-- Provides \u003cb\u003efull online support\u003c\/b\u003e for lecturers, including \u003cb\u003ean improved website component\u003c\/b\u003e with lecture slides and videos \u003c\/p\u003e\u003cp\u003e-- \u003cb\u003eFull color illustrations and code\u003c\/b\u003e instantly clarify difficult concepts \u003c\/p\u003e\u003cp\u003e-- Includes several new \"war stories\" \u003cb\u003erelating experiences from real-world applications\u003c\/b\u003e\u003c\/p\u003e\u003cp\u003e -- Over \u003cb\u003e100 new problems\u003c\/b\u003e, including programming-challenge problems from LeetCode and Hackerrank. \u003c\/p\u003e\u003cp\u003e-- Provides \u003cb\u003eup-to-date links \u003c\/b\u003eleading to the best implementations available in C, C++, and Java\u003c\/p\u003e\u003cp\u003e \u003c\/p\u003e\u003cp\u003e\u003cb\u003eAdditional Learning Tools: \u003c\/b\u003e\u003c\/p\u003e\u003cp\u003e-- Contains a unique catalog identifying the 75 algorithmic problems that arise most often in practice, leading the reader down the right path to solve them \u003c\/p\u003e\u003cp\u003e-- Exercises include \"job interview problems\" from major software companies \u003c\/p\u003e\u003cp\u003e-- Highlighted \"take home lessons\" emphasize essential concepts \u003c\/p\u003e\u003cp\u003e-- The \"no theorem-proof\" style provides a uniquely accessible and intuitive approach to a challenging subject \u003c\/p\u003e\u003cp\u003e-- Many algorithms are presented with actual code (written in C) \u003c\/p\u003e-- Provides comprehensive references to both survey articles and the primary literature\u003cp\u003e\u003c\/p\u003e\u003cp\u003e \u003c\/p\u003e\u003cp\u003eWritten by a well-known algorithms researcher who received the IEEE Computer Science and Engineering Teaching Award, this substantially enhanced third edition of \u003ci\u003e\u003cb\u003eThe Algorithm Design Manual\u003c\/b\u003e\u003c\/i\u003e is an essential learning tool for students and professionals needed a solid grounding in algorithms.   Professor Skiena is also the author of the popular Springer texts, \u003ci\u003e\u003cb\u003eThe Data Science Design Manual \u003c\/b\u003e\u003c\/i\u003eand\u003ci\u003e\u003cb\u003e Programming Challenges: The Programming Contest Training Manual.\u003c\/b\u003e\u003c\/i\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eIntroduction to Algorithm Design\u003cbr\u003e\u003c\/p\u003e\u003cp\u003eAlgorithm Analysis\u003cbr\u003e\u003c\/p\u003e\u003cp\u003eData Structures\u003cbr\u003e\u003c\/p\u003e\u003cp\u003eSorting and Searching\u003cbr\u003e\u003c\/p\u003e\u003cp\u003eDivide and Conquer\u003cbr\u003e\u003c\/p\u003e\u003cp\u003eRandomized Algorithms and Hashing\u003cbr\u003e\u003c\/p\u003e\u003cp\u003eGraph Traversal\u003cbr\u003e\u003c\/p\u003eWeighted Graph Algorithms\u003cbr\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003eCombinatorial Search and Heuristic Methods\u003cbr\u003e\u003c\/p\u003e\u003cp\u003eDynamic Programming\u003cbr\u003e\u003c\/p\u003e\u003cp\u003eNP-Completeness\u003cbr\u003e\u003c\/p\u003e\u003cp\u003eDealing with Hard Problems \u003cbr\u003e\u003c\/p\u003e\u003cp\u003eHow to Design Algorithms\u003c\/p\u003e\u003cp\u003e14 A Catalog of Algorithmic Problems 437\u003c\/p\u003e\u003cp\u003e15 Data Structures 439\u003c\/p\u003e\u003cp\u003e15.1 Dictionaries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 440\u003c\/p\u003e\u003cp\u003e15.2 Priority Queues . . . . . . . . . . . . . . . . . . . . . . . . . . . . 445\u003c\/p\u003e\u003cp\u003e15.3 Sux Trees and Arrays . . . . . . . . . . . . . . . . . . . . . . . 448\u003c\/p\u003e\u003cp\u003e15.4 Graph Data Structures . . . . . . . . . . . . . . . . . . . . . . . . 452\u003c\/p\u003e\u003cp\u003e15.5 Set Data Structures . . . . . . . . . . . . . . . . . . . . . . . . . 456\u003c\/p\u003e\u003cp\u003e15.6 Kd-Trees . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 460\u003c\/p\u003e\u003cp\u003e16 Numerical Problems 465\u003c\/p\u003e\u003cp\u003e16.1 Solving Linear Equations . . . . . . . . . . . . . . . . . . . . . . 467\u003c\/p\u003e\u003cp\u003e16.2 Bandwidth Reduction . . . . . . . . . . . . . . . . . . . . . . . . 470\u003c\/p\u003e\u003cp\u003e16.3 Matrix Multiplication . . . . . . . . . . . . . . . . . . . . . . . . 472\u003c\/p\u003e\u003cp\u003e16.4 Determinants and Permanents . . . . . . . . . . . . . . . . . . . 475\u003c\/p\u003e\u003cp\u003e16.5 Constrained\/Unconstrained Optimization . . . . . . . . . . . . . 478\u003c\/p\u003e\u003cp\u003e16.6 Linear Programming . . . . . . . . . . . . . . . . . . . . . . . . . 482\u003c\/p\u003e\u003cp\u003e16.7 Random Number Generation . . . . . . . . . . . . . . . . . . . . 486\u003c\/p\u003e\u003cp\u003e16.8 Factoring and Primality Testing . . . . . . . . . . . . . . . . . . . 490\u003c\/p\u003e\u003cp\u003e16.9 Arbitrary-Precision Arithmetic . . . . . . . . . . . . . . . . . . . 493\u003c\/p\u003e\u003cp\u003e16.10Knapsack Problem . . . . . . . . . . . . . . . . . . . . . . . . . . 497\u003c\/p\u003e\u003cp\u003e16.11Discrete Fourier Transform . . . . . . . . . . . . . . . . . . . . . 501\u003c\/p\u003e\u003cp\u003e17 Combinatorial Problems 505\u003c\/p\u003e\u003cp\u003e17.1 Sorting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 506\u003c\/p\u003e\u003cp\u003e17.2 Searching . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 510\u003c\/p\u003e\u003cp\u003e17.3 Median and Selection . . . . . . . . . . . . . . . . . . . . . . . . . 514\u003c\/p\u003e\u003cp\u003e17.4 Generating Permutations . . . . . . . . . . . . . . . . . . . . . . 517\u003c\/p\u003e\u003cp\u003e17.5 Generating Subsets . . . . . . . . . . . . . . . . . . . . . . . . . . 521\u003c\/p\u003e\u003cp\u003e17.6 Generating Partitions . . . . . . . . . . . . . . . . . . . . . . . . 524\u003c\/p\u003e\u003cp\u003e17.7 Generating Graphs . . . . . . . . . . . . . . . . . . . . . . . . . . 528\u003c\/p\u003e\u003cp\u003e17.8 Calendrical Calculations . . . . . . . . . . . . . . . . . . . . . . . 532\u003c\/p\u003e\u003cp\u003e17.9 Job Scheduling . . . . . . . . . . . . . . . . . . . . . . . . . . . . 534\u003c\/p\u003e\u003cp\u003e17.10Satisability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 537\u003c\/p\u003e\u003cp\u003e18 Graph Problems: Polynomial-Time 541\u003c\/p\u003e\u003cp\u003e18.1 Connected Components . . . . . . . . . . . . . . . . . . . . . . . 542\u003c\/p\u003e\u003cp\u003e18.2 Topological Sorting . . . . . . . . . . . . . . . . . . . . . . . . . . 546\u003c\/p\u003e\u003cp\u003e18.3 Minimum Spanning Tree . . . . . . . . . . . . . . . . . . . . . . . 549\u003c\/p\u003e\u003cp\u003e18.4 Shortest Path . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 554\u003c\/p\u003e\u003cp\u003e18.5 Transitive Closure and Reduction . . . . . . . . . . . . . . . . . . 559\u003c\/p\u003e\u003cp\u003e18.6 Matching . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 562\u003c\/p\u003e\u003cp\u003e18.7 Eulerian Cycle\/Chinese Postman . . . . . . . . . . . . . . . . . . 565\u003c\/p\u003e\u003cp\u003e18.8 Edge and Vertex Connectivity . . . . . . . . . . . . . . . . . . . . 568\u003c\/p\u003e\u003cp\u003e16 CONTENTS\u003c\/p\u003e\u003cp\u003e18.9 Network Flow . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 571\u003c\/p\u003e\u003cp\u003e18.10Drawing Graphs Nicely . . . . . . . . . . . . . . . . . . . . . . . 574\u003c\/p\u003e\u003cp\u003e18.11Drawing Trees . . . . . . . . . . . . . . . . . . . . . . . . . . . . 578\u003c\/p\u003e\u003cp\u003e18.12Planarity Detection and Embedding . . . . . . . . . . . . . . . . 581\u003c\/p\u003e\u003cp\u003e19 Graph Problems: NP-Hard 585\u003c\/p\u003e\u003cp\u003e19.1 Clique . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 586\u003c\/p\u003e\u003cp\u003e19.2 Independent Set . . . . . . . . . . . . . . . . . . . . . . . . . . . 589\u003c\/p\u003e\u003cp\u003e19.3 Vertex Cover . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 591\u003c\/p\u003e\u003cp\u003e19.4 Traveling Salesman Problem . . . . . . . . . . . . . . . . . . . . . 594\u003c\/p\u003e\u003cp\u003e19.5 Hamiltonian Cycle . . . . . . . . . . . . . . . . . . . . . . . . . . 598\u003c\/p\u003e\u003cp\u003e19.6 Graph Partition . . . . . . . . . . . . . . . . . . . . . . . . . . . . 601\u003c\/p\u003e\u003cp\u003e19.7 Vertex Coloring . . . . . . . . . . . . . . . . . . . . . . . . . . . . 604\u003c\/p\u003e\u003cp\u003e19.8 Edge Coloring . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 608\u003c\/p\u003e\u003cp\u003e19.9 Graph Isomorphism . . . . . . . . . . . . . . . . . . . . . . . . . 610\u003c\/p\u003e\u003cp\u003e19.10Steiner Tree . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 614\u003c\/p\u003e\u003cp\u003e19.11Feedback Edge\/Vertex Set . . . . . . . . . . . . . . . . . . . . . . 618\u003c\/p\u003e\u003cp\u003e20 Computational Geometry 621\u003c\/p\u003e\u003cp\u003e20.1 Robust Geometric Primitives . . . . . . . . . . . . . . . . . . . . 622\u003c\/p\u003e\u003cp\u003e20.2 Convex Hull . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 626\u003c\/p\u003e\u003cp\u003e20.3 Triangulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 630\u003c\/p\u003e\u003cp\u003e20.4 Voronoi Diagrams . . . . . . . . . . . . . . . . . . . . . . . . . . 634\u003c\/p\u003e\u003cp\u003e20.5 Nearest Neighbor Search . . . . . . . . . . . . . . . . . . . . . . . 637\u003c\/p\u003e\u003cp\u003e20.6 Range Search . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 641\u003c\/p\u003e\u003cp\u003e20.7 Point Location . . . . . . . . . . . . . . . . . . . . . . . . . . . . 644\u003c\/p\u003e\u003cp\u003e20.8 Intersection Detection . . . . . . . . . . . . . . . . . . . . . . . . 648\u003c\/p\u003e\u003cp\u003e20.9 Bin Packing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 652\u003c\/p\u003e\u003cp\u003e20.10Medial-Axis Transform . . . . . . . . . . . . . . . . . . . . . . . . 655\u003c\/p\u003e\u003cp\u003e20.11Polygon Partitioning . . . . . . . . . . . . . . . . . . . . . . . . . 658\u003c\/p\u003e\u003cp\u003e20.12Simplifying Polygons . . . . . . . . . . . . . . . . . . . . . . . . . 661\u003c\/p\u003e\u003cp\u003e20.13Shape Similarity . . . . . . . . . . . . . . . . . . . . . . . . . . . 664\u003c\/p\u003e\u003cp\u003e20.14Motion Planning . . . . . . . . . . . . . . . . . . . . . . . . . . . 667\u003c\/p\u003e\u003cp\u003e20.15Maintaining Line Arrangements . . . . . . . . . . . . . . . . . . . 671\u003c\/p\u003e\u003cp\u003e20.16Minkowski Sum . . . . . . . . . . . . . . . . . . . . . . . . . . . . 674\u003c\/p\u003e\u003cp\u003e21 Set and String Problems 677\u003c\/p\u003e\u003cp\u003e21.1 Set Cover . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 678\u003c\/p\u003e\u003cp\u003e21.2 Set Packing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 682\u003c\/p\u003e\u003cp\u003e21.3 String Matching . . . . . . . . . . . . . . . . . . . . . . . . . . . 685\u003c\/p\u003e\u003cp\u003e21.4 Approximate String Matching . . . . . . . . . . . . . . . . . . . . 688\u003c\/p\u003e\u003cp\u003e21.5 Text Compression . . . . . . . . . . . . . . . . . . . . . . . . . . 693\u003c\/p\u003e\u003cp\u003e21.6 Cryptography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 697\u003c\/p\u003e\u003cp\u003e21.7 Finite State Machine Minimization . . . . . . . . . . . . . . . . . 702\u003c\/p\u003e\u003cp\u003e21.8 Longest Common Substring\/Subsequence . . . . . . . . . . . . . 706\u003c\/p\u003e\u003cp\u003e21.9 Shortest Common Superstring . . . . . . . . . . . . . . . . . . . . 709\u003c\/p\u003e\u003cp\u003eCONTENTS 17\u003c\/p\u003e\u003cp\u003e22 Algorithmic Resources 713\u003c\/p\u003e\u003cp\u003e22.1 Algorithm Libraries . . . . . . . . . . . . . . . . . . . . . . . . . 713\u003c\/p\u003e\u003cp\u003e22.1.1 LEDA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 713\u003c\/p\u003e\u003cp\u003e22.1.2 CGAL . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 714\u003c\/p\u003e\u003cp\u003e22.1.3 Boost Graph Library . . . . . . . . . . . . . . . . . . . . . 714\u003c\/p\u003e\u003cp\u003e22.1.4 Netlib . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 714\u003c\/p\u003e\u003cp\u003e22.1.5 Collected Algorithms of the ACM . . . . . . . . . . . . . 715\u003c\/p\u003e\u003cp\u003e22.1.6 GitHub and SourceForge . . . . . . . . . . . . . . . . . . . 715\u003c\/p\u003e\u003cp\u003e22.1.7 The Stanford GraphBase . . . . . . . . . . . . . . . . . . 715\u003c\/p\u003e\u003cp\u003e22.1.8 Combinatorica . . . . . . . . . . . . . . . . . . . . . . . . 716\u003c\/p\u003e\u003cp\u003e22.1.9 Programs from Books . . . . . . . . . . . . . . . . . . . . 716\u003c\/p\u003e\u003cp\u003e22.2 Data Sources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 717\u003c\/p\u003e\u003cp\u003e22.3 Online Bibliographic Resources . . . . . . . . . . . . . . . . . . . 718\u003c\/p\u003e\u003cp\u003e22.4 Professional Consulting Services . . . . . . . . . . . . . . . . . . 718\u003c\/p\u003e\u003cp\u003e23 Bibliography 719\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003eIndex 771\u003c\/p\u003e","brand":"Springer Nature Switzerland AG","offers":[{"title":"Default Title","offer_id":48869361254743,"sku":"9783030542559","price":58.49,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9783030542559.jpg?v=1722292373"},{"product_id":"quantum-computing-an-applied-approach-9783030832735","title":"Quantum Computing: An Applied Approach","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eThis book integrates the foundations of quantum computing with a hands-on coding approach to this emerging field; it is the first to bring these elements together in an updated manner. This work is suitable for both academic coursework and corporate technical training.\u003c\/p\u003e\u003cp\u003eThe second edition includes extensive updates and revisions, both to textual content and to the code. Sections have been added on quantum machine learning, quantum error correction, Dirac notation and more.  This new edition benefits from the input of the many faculty, students, corporate engineering teams, and independent readers who have used the first edition.\u003c\/p\u003e\u003cp\u003eThis volume comprises three books under one cover: Part I outlines the necessary foundations of quantum computing and quantum circuits. Part II walks through the canon of quantum computing algorithms and provides code on a range of quantum computing methods in current use. Part III covers the mathematical toolkit required to master quantum computing. Additional resources include a table of operators and circuit elements and a companion GitHub site providing code and updates.\u003c\/p\u003e\u003cp\u003e\u003cb\u003eJack D. Hidary\u003c\/b\u003e is a research scientist in quantum computing and in AI at Alphabet X, formerly Google X.\u003c\/p\u003e \u003cp\u003e\u003c\/p\u003e \u003cp\u003e\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e“This well-put-together book is a valuable addition to the literature in the field.” (Shrisha Rao, Computing Reviews, February 3, 2023)\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e-Preface the the Second Edition.- Preface to the First Edition.- Acknowledgements.- Navigating this Book.- I. Foundations.- 1. Superposition, Entanglement and Reversibility.- 2. A Brief History of Quantum Computing.- 3. Qubits, Operators and Measurement.- 4. Complexity Theory.- II. 5. Building a Quantum Computer.- 6. Development Libraries for Quantum Computer Programming.- 7. Teleportation, Superdense Coding and Bell’s Inequality.- 8. The Canon: Code Walkthroughs.- 9. Quantum Computing Methods.- 10. Applications and Quantum Supremacy.- III. Toolkit.- 11. Mathematical Tools for Quantum Computing I.- 12. Mathematical Tools for Quantum Computing II.- 13. Mathematical Tools for Quantum Computing III.-  14. Dirac Notation.- 15. Table of Quantum Operators and Core Circuits.- Works Cited.- Index.","brand":"Springer Nature Switzerland AG","offers":[{"title":"Default Title","offer_id":48869361942871,"sku":"9783030832735","price":28.49,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9783030832735.jpg?v=1722292378"},{"product_id":"architecture-of-advanced-numerical-analysis-systems-9781484288528","title":"Architecture of Advanced Numerical Analysis","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eThis unique open access book applies the functional OCaml programming language to numerical or computational weighted data science, engineering, and scientific applications. This book is based on the authors' first-hand experience building and maintaining Owl, an OCaml-based numerical computing library.You'll first learn the various components in a modern numerical computation library. Then, you will learn how these components are designed and built up and how to optimize their performance. After reading and using this book, you'll have the knowledge required to design and build real-world complex systems that effectively leverage the advantages of the OCaml functional programming language.    What You Will LearnOptimize core operations based on N-dimensional arraysDesign and implement an industry-level algorithmic differentiation moduleImplement mathematical optimization, regression, and deep neural network functionalities based on algorithmic differentiationDesign and optimize a comp\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003ePrologueA Brief HistoryReductionism vs. HolismKey FeaturesContact MePART 1: NUMERICAL TECHNIQUES1. IntroductionWhat Is Scientific ComputingWhat is Functional ProgrammingWho Is This Book ForStructure of the BookInstallationOption 1: Install from OPAMOption 2: Pull from Docker HubOption 3: Pin the Dev-RepoOption 4: Compile from SourceCBLAS\/LAPACKE DependencyInteracting with OwlUsing ToplevelUsing NotebookUsing Owl-JupyterSummary2. ConventionsPure vs. ImpureNdarray vs. ScalarInfix OperatorsOperator ExtensionModule StructuresNumber and PrecisionPolymorphic FunctionsModule ShortcutsType Casting3. VisualisationCreate PlotsSpecificationSubplotsMultiple LinesLegendDrawing PatternsLine PlotScatter PlotStairs PlotBox PlotStem PlotArea PlotHistogram \u0026amp; CDF PlotLog Plot3D PlotAdvanced Statistical PlotSummaryReferences4. Mathematical FunctionsBasic FunctionsBasic Unary Math FunctionsBasic Binary FunctionsExponential and Logarithmic FunctionsTrigonometric FunctionsOther Math FunctionsSpecial FunctionsAiry FunctionsBessel FunctionsElliptic FunctionsGamma FunctionsBeta FunctionsStruve FunctionsZeta FunctionsError FunctionsIntegral FunctionsFactorialsInterpolation and ExtrapolationIntegrationUtility FunctionsSummary5. Statistical FunctionsRandom VariablesDiscrete Random VariablesContinuous Random VariablesDescriptive StatisticsOrder StatisticsSpecial DistributionGamma DistributionBeta DistributionChi-Square DistributionStudent-t DistributionCauchy DistributionMultiple VariablesSamplingHypothesis TestsTheoryGaussian Distribution in Hypothesis TestingTwo-Sample InferencesGoodness-of-fit TestsNon-parametric StatisticsCovariance and CorrelationsAnalysis of VarianceSummary6. N-Dimensional ArraysNdarray TypesCreation FunctionsProperties FunctionsMap FunctionsFold FunctionsScan FunctionsComparison FunctionsVectorised FunctionsIteration FunctionsManipulation FunctionsSerialisationTensorsSummaryReferences7. Slicing and BroadcastingSlicingBasic SlicingFancy SlicingConventions in DefinitionExtended OperatorsAdvanced UsageBroadcastingWhat Is Broadcasting?Shape ConstraintsSupported OperationsSlicing in NumPy and JuliaInternal MechanismSummary8. Linear AlgebraVectors and MatricesCreating MatricesAccessing ElementsIterate, Map, Fold, and FilterMath OperationsGaussian EliminationLU FactorisationInverse and TransposeVector SpacesRank and BasisOrthogonalitySolving Ax = bMatrix SensitivityDeterminantsEigenvalues and EigenvectorsSolving Ax=λ xComplex MatricesSimilarity Transformation and DiagonalisationPositive Definite MatricesPositive DefinitenessSingular Value DecompositionInternal: CBLAS and LAPACKELow-level Interface to CBLAS \u0026amp; LAPACKESparse MatricesSummaryReferences9. Ordinary Differential EquationsWhat Is An ODEExact SolutionsLinear SystemsSolving An ODE NumericallyOwl-ODEExample: Linear Oscillator SystemSolver StructureSymplectic SolversFeatures and LimitsExamples of using Owl-ODEExplicit ODETwo Body ProblemLorenz AttractorDamped OscillationStiffnessSolve Non-Stiff ODEsSolve Stiff ODEsSummaryReferences10. Signal ProcessingDiscrete Fourier TransformFast Fourier TransformExamplesApplications of FFTFind period of sunspotsDecipher the ToneImage ProcessingFilteringExample: SmoothingGaussian FilterSignal ConvolutionFFT and Image ConvolutionSummaryReferences11. Algorithmic DifferentiationChain RuleDifferentiation MethodsHow Algorithmic Differentiation WorksForward ModeReverse ModeForward or Reverse?A Strawman AD EngineSimple Forward ImplementationSimple Reverse ImplementationUnified ImplementationsForward and Reverse Propagation APIExpressing ComputationExample: Forward ModeExample: Reverse ModeHigh-Level APIsDerivative and GradientJacobianHessian and LaplacianOther APIsInternal of Algorithmic DifferentiationGo Beyond Simple ImplementationExtend AD moduleLazy EvaluationSummaryReferences12. OptimisationIntroductionRoot FindingUnivariate Function OptimisationUse DerivativesGolden Section SearchMultivariate Function OptimisationNelder-Mead Simplex MethodGradient Descent MethodsConjugate Gradient MethodNewton and Quasi-Newton MethodsGlobal Optimisation and Constrained OptimisationSummaryReferences13. RegressionLinear RegressionProblem: Where to locate a new McDonald’s restaurant?Cost FunctionSolving Problem with Gradient DescentMultiple RegressionFeature NormalisationAnalytical SolutionNon-linear regressionsRegularisationOls, Ridge, Lasso, and Elastic_netLogistic RegressionSigmoid FunctionCost FunctionExampleMulti-class classificationSupport Vector MachineKernel and Non-linear BoundaryExampleModel error and selectionError MetricsModel SelectionSummaryReferences14. Deep Neural NetworksPerceptronYet Another RegressionModel RepresentationForward PropagationBack propagationFeed Forward NetworkLayersActivation FunctionsInitialisationTrainingTestNeural Network ModuleModule StructureNeuronsNeural GraphTraining ParametersConvolutional Neural NetworkRecurrent Neural NetworkLong Short Term Memory (LSTM)Generative Adversarial NetworkSummaryReferences15. Natural Language ProcessingIntroductionText CorpusStep-by-step OperationUse the Corpus ModuleVector Space ModelsBag of Words (BOW)Term Frequency–Inverse Document Frequency (TF-IDF)Latent Dirichlet Allocation (LDA)ModelsDirichlet DistributionGibbs SamplingTopic Modelling ExampleLatent Semantic Analysis (LSA)Search Relevant DocumentsEuclidean and Cosine SimilarityLinear SearchingSummaryReferences16. Dataframe for Tabular DataBasic ConceptsCreate FramesManipulate FramesQuery FramesIterate, Map, and FilterRead\/Write CSV FilesInfer Type and SeparatorSummary17. Symbolic RepresentationIntroductionDesignCore abstractionEnginesONNX EngineExample 1: Basic operationsExample 2: Variable InitialisationExample 3: Neural networkLaTeX EngineOwl EngineSummary18. Probabilistic ProgrammingGenerative Model vs Discriminative ModelBayesian NetworksSampling TechniquesInferencePART 2: SYSTEM ARCHITECTURE19. Architecture OverviewIntroductionArchitecture OverviewCore ImplementationN-dimensional ArrayInterfaced LibrariesAdvanced FunctionalityComputation GraphAlgorithmic DifferentiationRegressionNeural NetworkParallel ComputingActor EngineGPU ComputingOpenMPCommunity-Driven R\u0026amp;DSummary20. Core OptimisationBackgroundNumerical LibrariesOptimisation of Numerical ComputationInterfacing to C CodeNdarray OperationsFrom OCaml to COptimisation TechniquesMap OperationsConvolution OperationsReduction OperationsRepeat OperationsSummaryReferences21. Automatic Empirical TuningWhat is Parameter TuningWhy Parameter Tuning in OwlHow to Tune OpenMP ParametersMake a DifferenceSummary22. Computation GraphIntroductionWhat is a Computation Graph?From Dynamic to StaticSignificance in ComputingExamplesExample 01: Basic CGraphExample 02: CGraph with ADExample 03: CGraph with DNNDesign RationaleOptimisation of CGraphOptimising memory with pebblesAllocation AlgorithmAs Intermediate RepresentationsSummary23. Scripting and Zoo SystemIntroductionShare Script with ZooTypical ScenarioCreate a ScriptShare via GistImport in Another ScriptSelect a Specific VersionCommand Line ToolMore ExamplesSystem DesignServicesType CheckingBackendDomain Specific LanguageService DiscoveryUse CaseSummaryReferences24. Compiler BackendsBase LibraryBackend: JavaScriptUse Native OCamlUse Facebook ReasonBackend: MirageOSMirageOS and UnikernelExample: Gradient DescentExample: Neural NetworkEvaluationSummary25. Distributed ComputingActor SystemDesignActor EnginesMap-Reduce EngineParameter Server EnginePeer-to-Peer EngineClassic Synchronise ParallelBulk Synchronous ParallelAsynchronous ParallelStale Synchronous ParallelProbabilistic Synchronise ParallelBasic idea: samplingCompatibilityBarrier Trade-off DimensionsConvergenceA Distributed Training ExampleStep ProgressAccuracySummaryReferences26. Testing FrameworkUnit TestExampleWhat Could Go WrongCorner CasesTest CoverageUse FunctorSummary27. Constants and Metric SystemWhat Is a Metric SystemFour Metric SystemsSI PrefixExample: Physics and Math constantsInternational System of UnitsTimeLengthAreaVolumeSpeedMassForceEnergyPowerPressureViscosityLuminanceRadioactivity28. Internal Utility ModulesDataset ModuleMNISTCIFAR-10Graph ModuleStack and Heap ModulesCount-Min SketchSummaryPART 3: CASE STUDIES29. Case - Image RecognitionBackgroundLeNetAlexNetVGGResNetSqueezeNetCapsule NetworkBuilding InceptionV3 NetworkInceptionV1 and InceptionV2FactorisationGrid Size ReductionInceptionV3 ArchitecturePreparing WeightsProcessing ImageRunning InferenceApplicationsSummaryReferences30. Case - Instance SegmentationIntroductionMask R-CNN NetworkBuilding Mask R-CNNFeature ExtractorProposal GenerationClassificationRun the CodeSummaryReferences31. Case - Neural Style TransferContent and StyleContent ReconstructionStyle RecreationCombining Content and StyleRunning NSTExtending NSTFast Style TransferBuilding FST NetworkRunning FSTSummaryReferences32. Case - Recommender SystemIntroductionArchitectureBuild Topic ModelsIndex Text CorpusRandom ProjectionOptimising Vector StorageOptimise Data StructureOptimise Index AlgorithmSearch ArticlesCode ImplementationMake It LiveSummaryReferences33. Case - Applications in FinanceIntroductionBond PricingBlack-Scholes ModelMathematical ModelOption PricingPortfolio OptimisationMathematical ModelEfficient FrontierMaximise Sharpe Ratio\u003cbr\u003e","brand":"APress","offers":[{"title":"Default Title","offer_id":48885829501271,"sku":"9781484288528","price":33.74,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781484288528.jpg?v=1722537848"},{"product_id":"options-and-derivatives-programming-in-c23-9781484298268","title":"Options and Derivatives Programming in C23","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eThis book is a hands-on guide for programmers who want to learn how C++ is used to develop solutions for options and derivatives trading in the financial industry. It explores the main algorithms and programming techniques used in implementing systems and solutions for trading options and derivatives. This updated edition will bring forward new advances in C++ software language and libraries, with a particular focus on the new C++23 standard.   The book starts by covering C++ language features that are frequently used to write financial software for options and derivatives. These features include the STL (standard template library), generic templates, functional programming, and support for numerical code. Examples include additional support for lambda functions with simplified syntax, improvements in automatic type detection for templates, custom literals, modules, constant expressions, and improved initialization strategies for C++ objects. This book also provides how-to examples tha\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e","brand":"APress","offers":[{"title":"Default Title","offer_id":48885835006295,"sku":"9781484298268","price":37.49,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781484298268.jpg?v=1722537867"},{"product_id":"monte-carlo-simulation-methods-assessment-applications-9781536119893","title":"Monte Carlo Simulation: Methods, Assessment \u0026","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eChapter One presents a study on application of Monte Carlo simulation in reliability assessment of composite electric power systems. Chapter Two develops a PK\/PD model to evaluate, by Monte Carlo simulation as a data maximisation strategy, the antiviral activity of two stavudine formulations: conventional stavudine and stavudine-gold nanoparticles (stavudine-AuNPs). In Chapter Three, the magnetic properties of the kagomé lattice is studied with RudermanKittelKasuyaYosida (RKKY) exchange interactions in a spin-7\/2 and alternate mixed spin-5\/2 and spin-2 Ising model on the Bethe lattice by using the Monte Carlo simulations.","brand":"Nova Science Publishers Inc","offers":[{"title":"Default Title","offer_id":48886062940503,"sku":"9781536119893","price":999.99,"currency_code":"GBP","in_stock":false}]},{"product_id":"horizons-in-computer-science-research-volume-15-9781536127577","title":"Horizons in Computer Science Research: Volume 15","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e","brand":"Nova Science Publishers Inc","offers":[{"title":"Default Title","offer_id":48886074736983,"sku":"9781536127577","price":205.59,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781536127577.jpg?v=1722538727"},{"product_id":"scientific-computing-applications-9781590330272","title":"Scientific Computing \u0026 Applications","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eScientific Computing \u0026amp; Applications","brand":"Nova Science Publishers Inc","offers":[{"title":"Default Title","offer_id":48886463594839,"sku":"9781590330272","price":85.59,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781590330272.jpg?v=1722540170"},{"product_id":"higher-education-computer-science-a-manual-of-practical-approaches-9783031293856","title":"Higher Education Computer Science: A Manual of","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eThe march towards on-line and blended teaching—present before the Covid-19 pandemic—has been accelerated by it, and there is no going back. Students and staff may object, but the economic drive towards “greater productivity” will inevitably result in less face-to-face (f2f) instruction. Therefore, it is incumbent for those delivering this precious, in-person resource to make maximum use of time…which raises the question, “how”?\u003c\/p\u003e\u003cp\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003eThe \u003ci\u003esecond edition\u003c\/i\u003e of \u003cb\u003e\u003ci\u003eHigher Education Computer Science\u003c\/i\u003e\u003c\/b\u003e\u003ci\u003e \u003c\/i\u003eoffers some potential answers. It also addresses other questions, such as “why have f2f teaching at all?” “what is the purpose of f2f?” and “what is the appropriate balance between the two?” The first edition began to offer suggestions for optimising limited opportunities to get together with students. \u003c\/p\u003e\u003cp\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003eAligned with that, this unique new volume examines how to use the technology available to maximum advantage: For example, resources such as Moocs and other on-line instructional materials can provide invaluable pedagogic support. In addition, the book addresses ‘problem-based learning,’ using robotics in the teaching of programming, and a multidisciplinary approach to data science. Although it includes a chapter on distance learning, there is greater emphasis placed on the soft, transferable skills and employability skills that are best delivered in person. Further, the work provides several examples of putting theory into practice when teaching computer science at both undergraduate and postgraduate levels.\u003c\/p\u003e\u003cp\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003e Written by experienced practitioners, each chapter tackles a particular teaching activity or topic within computing, presented in such a way that other practitioners can use. As such, this new volume will be an invaluable resource to those who want to protect and optimise in-person teaching.\u003cbr\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003ePart 1: Approaches to Learning.- Changing minds: Multitasking in lectures.- Active learning in large lectures.- The flipped classroom.- Applying cognitive theory to the teaching of programming: Metaphors, robots and problem-based learning.- Distance learning: Lessons learned from a UK masters programme.- Academic integrity for computer science instructors.- Contextualisation in data science.- Part 2: Teaching examples and practice.- Using graphics to inspire failing students.- Best practices for teaching information systems modelling.- Promoting design thinking through knowledge maps: A case study in computer games design and development education.- Teaching fuzzy logic utilising innovative approaches.- Semi-automating the marking of a Java programming portfolio assessment: A case study from a UK undergraduate programme.- Part 3: Employability and group work.- The enterprise showcase experience.- Task versus process: A taxonomy for group projects.- Realising the threshold of employability in higher education.- Exploring the landscape of HE industrial placements within engineering and technology subjects. Observations of recruitment, graduate attributes and student experience.- Baseline skills – Scaffolding soft skills development within the curriculum.- Professionalism and online presence.- Postscript.\u003cbr\u003e\u003c\/p\u003e","brand":"Springer International Publishing AG","offers":[{"title":"Default Title","offer_id":48889016648023,"sku":"9783031293856","price":37.49,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9783031293856.jpg?v=1722552280"},{"product_id":"programming-challenges-the-programming-contest-training-manual-texts-in-computer-science-9780387001630","title":"Programming Challenges The Programming Contest","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eThe challenges of problems from international programming competitions are an effective way to improve your algorithmic and coding skills and understanding. This volume uses international programming competition-type problems to motivate the study of algorithms, programming, and other topics in computer science.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e\"Skiena and Revilla's new book  'Programming Challenges: The Programming Contest Training Manual'  is just the ticket for those interested in a jumpstart to the world of contest programming.  With special emphasis on the international ACM collegiate contests, the book's best feature is each chapter's pithy introduction that demystifies a particular scheme or algorithmic approach.  The ensemble of these explications coupled with the contest strategy guidelines in the appendix can enable a novice to enhance contest results dramatically in a short time simply by solving the suggested exercises in each chapter.  Even contest veterans are likely to be able to find a nugget or two in the explanations and strategies. \"Presented in a logical order (contest programming has over a dozen different primary attacks), the book guides readers not only through the techniques and algorithms required but also through a huge set of problems that can be used for training.  Solutions can be submitted to Valladolid University's online trainer for quick feedback and reinforcement. \"If you're the sort who likes to have a single volume that covers the vast majority of a field, you'll love Skiena and Revilla's new tome.\" --Rob Kolstad, Ph.D., Head Coach, USA Computing Olympiad\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e* Getting started * Data structures * Strings * Sorting * Arithmetic and algebra * Combinatorics * Number theory * Backtracking * Graph traversal * Graph algorithms * Dynamic programming * Grids * Geometry * Computational geometry * Appendix * Index","brand":"Springer-Verlag New York Inc.","offers":[{"title":"Default Title","offer_id":49083495022935,"sku":"9780387001630","price":56.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780387001630.jpg?v=1725549127"},{"product_id":"mathematics-and-computation-9780691189130","title":"Mathematics and Computation","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e\"Avi Wigderson, Co-Winner of the Abel Prize, Norwegian Academy of Science and Letters\"\u003cbr\u003e\"Avi Wigderson's new 440-page book, \u003ci\u003eMathematics and Computation: A Theory Revolutionizing Technology and Science\u003c\/i\u003e (Princeton University Press, October 2019), lays out a commanding overview of the theory of computing and argues for its central role in human thought.\"\u003cb\u003e---Allyn Jackson, \u003ci\u003eCommunications of the ACM\u003c\/i\u003e\u003c\/b\u003e\u003cbr\u003e\"This must-read book provides a high-level, enjoyable overview of numerous parts of mathematics that are related to computation in general, and computational complexity in particular.\" * Choice *","brand":"Princeton University Press","offers":[{"title":"Default Title","offer_id":49083560919383,"sku":"9780691189130","price":40.5,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780691189130.jpg?v=1725549328"},{"product_id":"deep-learning-architectures-a-mathematical-approach-9783030367206","title":"Deep Learning Architectures: A Mathematical","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eThis book describes how neural networks operate from the mathematical point of view. As a result, neural networks can be interpreted both as function universal approximators and information processors. The book bridges the gap between ideas and concepts of neural networks, which are used nowadays at an intuitive level, and the precise modern mathematical language, presenting the best practices of the former and enjoying the robustness and elegance of the latter.\u003c\/p\u003e\u003cp\u003eThis book can be used in a graduate course in deep learning, with the first few parts being accessible to senior undergraduates.  In addition, the book will be of wide interest to machine learning researchers who are interested in a theoretical understanding of the subject.\u003c\/p\u003e\u003cp\u003e \u003c\/p\u003e\u003cp\u003e \u003c\/p\u003e\u003cp\u003e \u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e“This book is useful to students who have already had an introductory course in machine learning and are further interested to deepen their understanding of the machine learning material from the mathematical point of view.” (T. C. Mohan, zbMATH 1441.68001, 2020)\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003eIntroductory Problems.- Activation Functions.- Cost Functions.- Finding Minima Algorithms.- Abstract Neurons.- Neural Networks.- Approximation Theorems.- Learning with One-dimensional Inputs.- Universal Approximators.- Exact Learning.- Information Representation.- Information Capacity Assessment.- Output Manifolds.- Neuromanifolds.- Pooling.- Convolutional Networks.- Recurrent Neural Networks.- Classification.- Generative Models.- Stochastic Networks.- Hints and Solutions. ","brand":"Springer Nature Switzerland AG","offers":[{"title":"Default Title","offer_id":49084750233943,"sku":"9783030367206","price":75.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9783030367206.jpg?v=1725553222"},{"product_id":"introduction-to-computation-haskell-logic-and-automata-9783030769079","title":"Introduction to Computation: Haskell, Logic and","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eComputation, itself a form of calculation, incorporates steps that include arithmetical and non-arithmetical (logical) steps following a specific set of rules (an algorithm).  This uniquely accessible textbook introduces students using a very distinctive approach, quite rapidly leading them into essential topics with sufficient depth, yet in a highly intuitive manner.  From core elements like sets, types, Venn diagrams and logic, to patterns of reasoning, calculus, recursion and expression trees, the book spans the breadth of key concepts and methods that will enable students to readily progress with their studies in Computer Science.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e“This book is intended as a textbook for an introductory course in computation for students beginning in informatics. No prerequisites are needed, all concepts, even elementary ones ... . it is also very suited for self-study, even if a reader is interested in Haskell or symbolic logic alone. ... Comprehension is supported by exercises for each chapter ... .” (Dieter Riebesehl, zbMATH 1497.68005, 2022)\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e1 Sets 132 Types 193 Simple Computations 274 Venn Diagrams and Logical Connectives 355 Lists and Comprehensions 456 Features and Predicates 557 Testing Your Programs 638 Patterns of Reasoning 739 More Patterns of Reasoning 8110 Lists and Recursion 9111 More Fun with Recursion 10112 Higher-Order Functions 11113 Higher and Higher 12314 Sequent Calculus 13115 Algebraic Data Types 14316 Expression Trees 15717 Karnaugh Maps 17518 Relations and Quantifiers 18319 Checking Satisfiability 19120 Data Representation 20321 Data Abstraction 22122 Efficient CNF Conversion 23723 Counting Satisfying Valuations 24924 Type Classes 26325 Search in Trees 27526 Combinatorial Algorithms 28527 Finite Automata 29928 Deterministic Finite Automata 31129 Non-Deterministic Finite Automata 32130 Input\/Output and Monads 34131 Regular Expressions 35932 Non-Regular Languages 369Index 377","brand":"Springer Nature Switzerland AG","offers":[{"title":"Default Title","offer_id":49084751577431,"sku":"9783030769079","price":28.49,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9783030769079.jpg?v=1725553223"},{"product_id":"computational-geometry-algorithms-and-applications-9783540779735","title":"Computational Geometry: Algorithms and","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eThis introduction to computational geometry focuses on algorithms. Motivation is provided from the application areas as all techniques are related to particular applications in robotics, graphics, CAD\/CAM, and geographic information systems. Modern insights in computational geometry are used to provide solutions that are both efficient and easy to understand and implement.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e\u003cem\u003e\"An excellent introduction to the field is given here, including a general motivation and usage cases beyond simple graphics rendering or interaction.\"\u003c\/em\u003e from the ACM Reviews by William Fahle, University of Texas at Dallas, USA\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eComputational Geometry: Introduction.- Line Segment Intersection: Thematic Map Overlay.- Polygon Triangulation: Guarding an Art Gallery.- Linear Programming: Manufacturing with Molds.- Orthogonal Range Searching: Querying a Database.- Point Location: Knowing Where You Are.- Voronoi Diagrams: The Post Office Problem.- Arrangements and Duality: Supersampling in Ray Tracing.- Delaunay Triangulations: Height Interpolation.- More Geometric Data Structures: Windowing.- Convex Hulls: Mixing Things.- Binary Space Partitions: The Painter's Algorithm.- Robot Motion Planning: Getting Where You Want to Be.- Quadtrees: Non-Uniform Mesh Generation.- Visibility Graphs: Finding the Shortest Route.- Simplex Range Searching: Windowing Revisited.- Bibliography.- Index.\u003c\/p\u003e","brand":"Springer-Verlag Berlin and Heidelberg GmbH \u0026 Co. KG","offers":[{"title":"Default Title","offer_id":49084775465303,"sku":"9783540779735","price":42.74,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9783540779735.jpg?v=1725553300"},{"product_id":"programming-languages-principles-and-paradigms-9783031341434","title":"Programming Languages: Principles and Paradigms","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eThis textbook is a thorough, up-to-date introduction to the principles and techniques that guide the design and implementation of modern programming languages. \u003c\/p\u003e\u003cp\u003eThe goal of the book is to provide the basis for a critical understanding of most modern programming languages. Thus, rather than focusing on a specific language, the book identifies the most important principles shared by large classes of languages. The notion of ‘abstract machine’ is a unifying concept that helps to maintain an accurate and elementary treatment. The book introduces, analyses in depth, and compares the imperative, object-oriented, functional, logic, concurrent, constraint-based, and service-oriented programming paradigms. All material coming from the first English edition has been updated and extended, clarifying some tricky points, and discussing newer programming languages. This second edition contains new chapters dedicated to constraint, concurrent, and service-oriented programming.\u003c\/p\u003e\u003cp\u003e\u003cb\u003eTopics and features:\u003c\/b\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eRequires familiarity with one programming language is a prerequisite\u003c\/li\u003e\n\u003cli\u003eProvides a chapter on history offering context for most of the constructs in use today\u003c\/li\u003e\n\u003cli\u003ePresents an elementary account of semantical approaches and of computability\u003c\/li\u003e\n\u003cli\u003eIntroduces new examples in modern programming languages like Python or Scala\u003c\/li\u003e\n\u003cli\u003eOffers a chapter that opens a perspective on applications in artificial intelligence\u003c\/li\u003e\n\u003c\/ul\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003eConceived as a university textbook, this unique volume will also be suitable for IT specialists who want to deepen their knowledge of the mechanisms behind the languages they use. The choice of themes and the presentation style are largely influenced by the experience of teaching the content as part of a bachelor's degree in computer science.\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e1. Abstract Machines.- 2. How to Describe a Programming Language.- 3. Foundations.- 4. Names and the Environment.- 5. Memory Management.- 6. Control Structure.- 7. Control Abstraction.- Structuring Data.- 8. Data Abstraction.- 9. The Object-Oriented Paradigm.- 10. The Functional Paradigm.- 11. The Logic Programming Paradigm.- 12. A Short Historical Perspective.\u003cbr\u003e\u003c\/p\u003e","brand":"Springer International Publishing AG","offers":[{"title":"Default Title","offer_id":49396273185111,"sku":"9783031341434","price":44.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9783031341434.jpg?v=1730415325"},{"product_id":"metaheuristics-9780470278581","title":"Metaheuristics","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eA unified view of metaheuristics  \u003cp\u003eThis book provides a complete background on metaheuristics and shows readers how to design and implement efficient algorithms to solve complex optimization problems across a diverse range of applications, from networking and bioinformatics to engineering design, routing, and scheduling. It presents the main design questions for all families of metaheuristics and clearly illustrates how to implement the algorithms under a software framework to reuse both the design and code.\u003c\/p\u003e \u003cp\u003eThroughout the book, the key search components of metaheuristics are considered as a toolbox for:\u003c\/p\u003e \u003cul\u003e \u003cli\u003eDesigning efficient metaheuristics (e.g. local search, tabu search, simulated annealing, evolutionary algorithms, particle swarm optimization, scatter search, ant colonies, bee colonies, artificial immune systems) for optimization problems\u003c\/li\u003e \u003cli\u003e \u003cp\u003eDesigning efficient metaheuristics for multi-objective optimization problems\u003c\/p\u003e \u003c\/li\u003e \u003cli\u003e \u003cp\u003eDesigning hybrid, \u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e\u003c\/p\u003e\n\u003cp\u003e“In conclusion, I found reading Metaheuristics: From Design to Implementation to be pleasant and enjoyable. I particularly recommend it as a reference for researchers and students of computer science or operations research who want a global outlook of metaheuristics methods. It would also be extremely useful for introducing graduate and PhD students who are new to the field of heuristics and metaheuristics to the amazing world of the designing of these procedures.”  (\u003ci\u003eInforms\u003c\/i\u003e, 1 July 2012)\u003c\/p\u003e \u003cp\u003e\"It will be an indispensable text for advanced undergraduate and graduate students in computer science, operations research, applied mathematics, control, business and management and engineering.\" (\u003ci\u003eZentralblatt MATH,\u003c\/i\u003e 2010)\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e\n\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cb\u003ePreface.\u003c\/b\u003e  \u003cp\u003e\u003cb\u003eAcknowledgments.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eGlossary.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1 Common Concepts for Metaheuristics.\u003c\/p\u003e \u003cp\u003e1.1 Optimization Models.\u003c\/p\u003e \u003cp\u003e1.2 Other Models for Optimization.\u003c\/p\u003e \u003cp\u003e1.3 Optimization Methods.\u003c\/p\u003e \u003cp\u003e1.4 Main Common Concepts for Metaheuristics.\u003c\/p\u003e \u003cp\u003e1.5 Constraint Handling.\u003c\/p\u003e \u003cp\u003e1.6 Parameter Tuning.\u003c\/p\u003e \u003cp\u003e1.7 Performance Analysis of Metaheuristics.\u003c\/p\u003e \u003cp\u003e1.8 Software Frameworks for Metaheuristics.\u003c\/p\u003e \u003cp\u003e1.9 Conclusions.\u003c\/p\u003e \u003cp\u003e1.10 Exercises.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Single-Solution Based Metaheuristics.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 Common Concepts for Single-Solution Based Metaheuristics.\u003c\/p\u003e \u003cp\u003e2.2 Fitness Landscape Analysis.\u003c\/p\u003e \u003cp\u003e2.3 Local Search.\u003c\/p\u003e \u003cp\u003e2.4 Simulated Annealing.\u003c\/p\u003e \u003cp\u003e2.5 Tabu Search.\u003c\/p\u003e \u003cp\u003e2.6 Iterated Local Search.\u003c\/p\u003e \u003cp\u003e2.7 Variable Neighborhood Search.\u003c\/p\u003e \u003cp\u003e2.8 Guided Local Search.\u003c\/p\u003e \u003cp\u003e2.9 Other Single-Solution Based Metaheuristics.\u003c\/p\u003e \u003cp\u003e2.10 S-Metaheuristic Implementation Under ParadisEO.\u003c\/p\u003e \u003cp\u003e2.11 Conclusions.\u003c\/p\u003e \u003cp\u003e2.12 Exercises.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Population-Based Metaheuristics.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Common Concepts for Population-Based Metaheuristics.\u003c\/p\u003e \u003cp\u003e3.2 Evolutionary Algorithms.\u003c\/p\u003e \u003cp\u003e3.3 Common Concepts for Evolutionary Algorithms.\u003c\/p\u003e \u003cp\u003e3.4 Other Evolutionary Algorithms.\u003c\/p\u003e \u003cp\u003e3.5 Scatter Search.\u003c\/p\u003e \u003cp\u003e3.6 Swarm Intelligence.\u003c\/p\u003e \u003cp\u003e3.7 Other Population-Based Methods.\u003c\/p\u003e \u003cp\u003e3.8 P-metaheuristics Implementation Under ParadisEO.\u003c\/p\u003e \u003cp\u003e3.9 Conclusions.\u003c\/p\u003e \u003cp\u003e3.10 Exercises.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Metaheuristics for Multiobjective Optimization.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Multiobjective Optimization Concepts.\u003c\/p\u003e \u003cp\u003e4.2 Multiobjective Optimization Problems.\u003c\/p\u003e \u003cp\u003e4.3 Main Design Issues of Multiobjective Metaheuristics.\u003c\/p\u003e \u003cp\u003e4.4 Fitness Assignment Strategies.\u003c\/p\u003e \u003cp\u003e4.5 Diversity Preservation.\u003c\/p\u003e \u003cp\u003e4.6 Elitism.\u003c\/p\u003e \u003cp\u003e4.7 Performance Evaluation and Pareto Front Structure.\u003c\/p\u003e \u003cp\u003e4.8 Multiobjective Metaheuristics Under ParadisEO.\u003c\/p\u003e \u003cp\u003e4.9 Conclusions and Perspectives.\u003c\/p\u003e \u003cp\u003e4.10 Exercises.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Hybrid Metaheuristics.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Hybrid Metaheuristics.\u003c\/p\u003e \u003cp\u003e5.2 Combining Metaheuristics with Mathematical Programming.\u003c\/p\u003e \u003cp\u003e5.3 Combining Metaheuristics with Constraint Programming.\u003c\/p\u003e \u003cp\u003e5.4 Hybrid Metaheuristics with Machine Learning and Data Mining.\u003c\/p\u003e \u003cp\u003e5.5 Hybrid Metaheuristics for Multiobjective Optimization.\u003c\/p\u003e \u003cp\u003e5.6 Hybrid Metaheuristics Under ParadisEO.\u003c\/p\u003e \u003cp\u003e5.7 Conclusions and Perspectives.\u003c\/p\u003e \u003cp\u003e5.8 Exercises.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Parallel Metaheuristics.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Parallel Design of Metaheuristics.\u003c\/p\u003e \u003cp\u003e6.2 Parallel Implementation of Metaheuristics.\u003c\/p\u003e \u003cp\u003e6.3 Parallel Metaheuristics for Multiobjective Optimization.\u003c\/p\u003e \u003cp\u003e6.4 Parallel Metaheuristics Under ParadisEO.\u003c\/p\u003e \u003cp\u003e6.5 Conclusions and Perspectives.\u003c\/p\u003e \u003cp\u003e6.6 Exercises.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAppendix: UML and C++.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eA.1 A Brief Overview of UML Notations.\u003c\/p\u003e \u003cp\u003eA.2 A Brief Overview of the C++ Template Concept.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eReferences.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eIndex.\u003c\/b\u003e\u003c\/p\u003e\n\u003c\/li\u003e\n\u003c\/ul\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":49402312229207,"sku":"9780470278581","price":113.36,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780470278581.jpg?v=1730480031"},{"product_id":"computational-statistics-9780470533314","title":"Computational Statistics","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eThis new edition continues to serve as a comprehensive guide to modern and classical methods of statistical computing. The book is comprised of four main parts spanning the field:\u003c\/p\u003e \u003cul\u003e \u003cli\u003eOptimization\u003c\/li\u003e \u003cli\u003eIntegration and Simulation\u003c\/li\u003e \u003cli\u003eBootstrapping\u003c\/li\u003e \u003cli\u003eDensity Estimation and Smoothing\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eWithin these sections,each chapter includes a comprehensive introduction and step-by-step implementation summaries to accompany the explanations of key methods. The new edition includes updated coverage and existing topics as well as new topics such as adaptive MCMC and bootstrapping for correlated data. The book website now includes comprehensive R code for the entire book. There are extensive exercises, real examples, and helpful insights about how to use the methods in practice.\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003ePREFACE xv\u003c\/p\u003e \u003cp\u003eACKNOWLEDGMENTS xvii\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1\u003c\/b\u003e \u003cb\u003eREVIEW\u003c\/b\u003e \u003cb\u003e1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 Mathematical Notation 1\u003c\/p\u003e \u003cp\u003e1.2 Taylor’s Theorem and Mathematical Limit Theory 2\u003c\/p\u003e \u003cp\u003e1.3 Statistical Notation and Probability Distributions 4\u003c\/p\u003e \u003cp\u003e1.4 Likelihood Inference 9\u003c\/p\u003e \u003cp\u003e1.5 Bayesian Inference 11\u003c\/p\u003e \u003cp\u003e1.6 Statistical Limit Theory 13\u003c\/p\u003e \u003cp\u003e1.7 Markov Chains 14\u003c\/p\u003e \u003cp\u003e1.8 Computing 17\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePART I\u003c\/b\u003e \u003cb\u003eOPTIMIZATION\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2\u003c\/b\u003e \u003cb\u003eOPTIMIZATION AND SOLVING NONLINEAR EQUATIONS\u003c\/b\u003e \u003cb\u003e21\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 Univariate Problems 22\u003c\/p\u003e \u003cp\u003e2.2 Multivariate Problems 34\u003c\/p\u003e \u003cp\u003eProblems 54\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3\u003c\/b\u003e \u003cb\u003eCOMBINATORIAL OPTIMIZATION\u003c\/b\u003e \u003cb\u003e59\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Hard Problems and NP-Completeness 59\u003c\/p\u003e \u003cp\u003e3.2 Local Search 65\u003c\/p\u003e \u003cp\u003e3.3 Simulated Annealing 68\u003c\/p\u003e \u003cp\u003e3.4 Genetic Algorithms 75\u003c\/p\u003e \u003cp\u003e3.5 Tabu Algorithms 85\u003c\/p\u003e \u003cp\u003eProblems 92\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4\u003c\/b\u003e \u003cb\u003eEM OPTIMIZATION METHODS\u003c\/b\u003e \u003cb\u003e97\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Missing Data, Marginalization, and Notation 97\u003c\/p\u003e \u003cp\u003e4.2 The EM Algorithm 98\u003c\/p\u003e \u003cp\u003e4.3 EM Variants 111\u003c\/p\u003e \u003cp\u003eProblems 121\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePART II\u003c\/b\u003e \u003cb\u003eINTEGRATION AND SIMULATION\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5\u003c\/b\u003e \u003cb\u003eNUMERICAL INTEGRATION\u003c\/b\u003e \u003cb\u003e129\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Newton–Côtes Quadrature 129\u003c\/p\u003e \u003cp\u003e5.2 Romberg Integration 139\u003c\/p\u003e \u003cp\u003e5.3 Gaussian Quadrature 142\u003c\/p\u003e \u003cp\u003e5.4 Frequently Encountered Problems 146\u003c\/p\u003e \u003cp\u003eProblems 148\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6\u003c\/b\u003e \u003cb\u003eSIMULATION AND MONTE CARLO INTEGRATION\u003c\/b\u003e \u003cb\u003e151\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction to the Monte Carlo Method 151\u003c\/p\u003e \u003cp\u003e6.2 Exact Simulation 152\u003c\/p\u003e \u003cp\u003e6.3 Approximate Simulation 163\u003c\/p\u003e \u003cp\u003e6.4 Variance Reduction Techniques 180\u003c\/p\u003e \u003cp\u003eProblems 195\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7\u003c\/b\u003e \u003cb\u003eMARKOV CHAIN MONTE CARLO\u003c\/b\u003e \u003cb\u003e201\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Metropolis–Hastings Algorithm 202\u003c\/p\u003e \u003cp\u003e7.2 Gibbs Sampling 209\u003c\/p\u003e \u003cp\u003e7.3 Implementation 218\u003c\/p\u003e \u003cp\u003eProblems 230\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8\u003c\/b\u003e \u003cb\u003eADVANCED TOPICS IN MCMC\u003c\/b\u003e \u003cb\u003e237\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 Adaptive MCMC 237\u003c\/p\u003e \u003cp\u003e8.2 Reversible Jump MCMC 250\u003c\/p\u003e \u003cp\u003e8.3 Auxiliary Variable Methods 256\u003c\/p\u003e \u003cp\u003e8.4 Other Metropolis–Hastings Algorithms 260\u003c\/p\u003e \u003cp\u003e8.5 Perfect Sampling 264\u003c\/p\u003e \u003cp\u003e8.6 Markov Chain Maximum Likelihood 268\u003c\/p\u003e \u003cp\u003e8.7 Example: MCMC for Markov Random Fields 269\u003c\/p\u003e \u003cp\u003eProblems 279\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePART III\u003c\/b\u003e \u003cb\u003eBOOTSTRAPPING\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9\u003c\/b\u003e \u003cb\u003eBOOTSTRAPPING\u003c\/b\u003e \u003cb\u003e287\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e9.1 The Bootstrap Principle 287\u003c\/p\u003e \u003cp\u003e9.2 Basic Methods 288\u003c\/p\u003e \u003cp\u003e9.3 Bootstrap Inference 292\u003c\/p\u003e \u003cp\u003e9.4 Reducing Monte Carlo Error 302\u003c\/p\u003e \u003cp\u003e9.5 Bootstrapping Dependent Data 303\u003c\/p\u003e \u003cp\u003e9.6 Bootstrap Performance 315\u003c\/p\u003e \u003cp\u003e9.7 Other Uses of the Bootstrap 316\u003c\/p\u003e \u003cp\u003e9.8 Permutation Tests 317\u003c\/p\u003e \u003cp\u003eProblems 319\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePART IV\u003c\/b\u003e \u003cb\u003eDENSITY ESTIMATION AND SMOOTHING\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10\u003c\/b\u003e \u003cb\u003eNONPARAMETRIC DENSITY ESTIMATION\u003c\/b\u003e \u003cb\u003e325\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e10.1 Measures of Performance 326\u003c\/p\u003e \u003cp\u003e10.2 Kernel Density Estimation 327\u003c\/p\u003e \u003cp\u003e10.3 Nonkernel Methods 341\u003c\/p\u003e \u003cp\u003e10.4 Multivariate Methods 345\u003c\/p\u003e \u003cp\u003eProblems 359\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11\u003c\/b\u003e \u003cb\u003eBIVARIATE SMOOTHING\u003c\/b\u003e \u003cb\u003e363\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e11.1 Predictor–Response Data 363\u003c\/p\u003e \u003cp\u003e11.2 Linear Smoothers 365\u003c\/p\u003e \u003cp\u003e11.3 Comparison of Linear Smoothers 377\u003c\/p\u003e \u003cp\u003e11.4 Nonlinear Smoothers 379\u003c\/p\u003e \u003cp\u003e11.5 Confidence Bands 384\u003c\/p\u003e \u003cp\u003e11.6 General Bivariate Data 388\u003c\/p\u003e \u003cp\u003eProblems 389\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12\u003c\/b\u003e \u003cb\u003eMULTIVARIATE SMOOTHING\u003c\/b\u003e \u003cb\u003e393\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e12.1 Predictor–Response Data 393\u003c\/p\u003e \u003cp\u003e12.2 General Multivariate Data 413\u003c\/p\u003e \u003cp\u003eProblems 416\u003c\/p\u003e \u003cp\u003e\u003cb\u003eDATA ACKNOWLEDGMENTS\u003c\/b\u003e \u003cb\u003e421\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eREFERENCES\u003c\/b\u003e \u003cb\u003e423\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eINDEX\u003c\/b\u003e \u003cb\u003e457\u003c\/b\u003e\u003c\/p\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":49402357252439,"sku":"9780470533314","price":99.86,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780470533314.jpg?v=1730480164"},{"product_id":"chaos-and-order-in-the-capital-markets-9780471139386","title":"Chaos and Order in the Capital Markets","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eThis edition includes developments in chaos theory, with new chapters that tie in with innovations such as fuzzy logic, neural nets and artificial intelligence as they relate to finance.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003eTHE NEW PARADIGM.\u003cbr\u003e \u003cbr\u003e Introduction: Life Can Be So Complicated.\u003cbr\u003e \u003cbr\u003e Random Walks and Efficient Markets.\u003cbr\u003e \u003cbr\u003e The Failure of the Linear Paradigm.\u003cbr\u003e \u003cbr\u003e Markets and Chaos: Chance and Necessity.\u003cbr\u003e \u003cbr\u003e FRACTAL STRUCTURE IN THE CAPITAL MARKETS.\u003cbr\u003e \u003cbr\u003e Introduction to Fractals.\u003cbr\u003e \u003cbr\u003e The Fractal Dimension.\u003cbr\u003e \u003cbr\u003e Fractal Time Series-Biased Random Walks.\u003cbr\u003e \u003cbr\u003e R\/S Analysis of the Capital Markets.\u003cbr\u003e \u003cbr\u003e Fractal Statistics.\u003cbr\u003e \u003cbr\u003e Fractals and Chaos.\u003cbr\u003e \u003cbr\u003e NONLINEAR DYNAMICS.\u003cbr\u003e \u003cbr\u003e Introduction to Nonlinear Dynamic Systems.\u003cbr\u003e \u003cbr\u003e Dynamic Analysis of Time Series.\u003cbr\u003e \u003cbr\u003e Dynamic Analysis of the Capital Markets.\u003cbr\u003e \u003cbr\u003e LIVING WITH COMPLEXITY.\u003cbr\u003e \u003cbr\u003e The Coherent Market Hypothesis.\u003cbr\u003e \u003cbr\u003e Fractional Truth: Fuzzy Logic and Behavioral Finance.\u003cbr\u003e \u003cbr\u003e Applying Chaos and Nonlinear Methods.\u003cbr\u003e \u003cbr\u003e What Lies Ahead: Toward a More General Approach.\u003cbr\u003e \u003cbr\u003e About the Diskette.\u003cbr\u003e \u003cbr\u003e Appendices.\u003cbr\u003e \u003cbr\u003e Bibliography.\u003cbr\u003e \u003cbr\u003e Glossary.\u003cbr\u003e \u003cbr\u003e Index.","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":49402496876887,"sku":"9780471139386","price":52.5,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780471139386.jpg?v=1730480588"},{"product_id":"simulation-9780471251842","title":"Simulation","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eA unique, integrated treatment of computer modeling and simulation The future of science belongs to those willing to make the shift to simulation-based modeling, predicts Rice Professor James Thompson, a leading modeler and computational statistician widely known for his original ideas and engaging style. He discusses methods, available to anyone with a fast desktop computer, for integrating simulation into the modeling process in order to create meaningful models of real phenomena. Drawing from a wealth of experience, he gives examples from trading markets, oncology, epidemiology, statistical process control, physics, public policy, combat, real-world optimization, Bayesian analyses, and population dynamics. Dr. Thompson believes that, so far from liberating us from the necessity of modeling, the fast computer enables us to engage in realistic models of processes in , for example, economics, which have not been possible earlier because simple stochastic models in the forward temporal \u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003eWith the advent of faster computers with comparatively large storage facilities, simulation-based modeling is rapidly being adopted as an alternative approach to conventional top-down, assumptions-based, continuous, stochastic and discrete differential equation modeling. Thompson offers an interesting exposition to the art of simulation, and views \"simulation approach\" to modeling as a paradigm for realistic evolutionary modeling. The book is written in a very casual style, and background knowledge in statistics is all that is required to grasp the material contained therein. Thompson begins with an exposition of the generation of randomnumbers and then delves into a variety of special topics, including models for stocks and derivatives, optimization and estimation in a noisy world, Monte-Carlo solutions to differential equations, simulation assessment of multivariate and robust procedures in statistical process control, resampling-based tests, and some exposition to modeling the AIDS epidemic. Several useful algorithms, problem sets, and references to standard simulation packages are provided. Short chapter bibliographies; wide range of examples. The book could be used as a resource for beginning graduate students and professionals in applied statistics, computer science, economics and finance, engineering, and the natural sciences. Highly recommended. Graduate students; faculty; professionals. (CHOICE, April 2001, Vol. 38, No. 8)\u003cbr\u003e \"...an eclectic survey of computing methods...lively and interesting...the wide variety of example certainly helps bring the material to life.\" (Journal of the American Statistical Association, Vol. 97, No. 457, March 2002)\u003cbr\u003e \"...a very useful and entertaining book...a great reference book...contains some valuable material and philosophy that is unavailable anywhere else.\" (IIE Transactions)\u003cbr\u003e \"...often entertaining...the level of detain and relevance is appropriate...a worthwhile read for model builders comfortable with both mathematics and simulation.\" (Complexity, Vol. 7, No. 2, 2002)\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003eThe Generation of \"Random\" Numbers.\u003cbr\u003e \u003cbr\u003e Random Quadrature.\u003cbr\u003e \u003cbr\u003e Monte Carlo Solutions of Differential Equations.\u003cbr\u003e \u003cbr\u003e Markov Chains, Poisson Processes and Linear Equations.\u003cbr\u003e \u003cbr\u003e SIMEST, SIMDAT, and Pseudoreality.\u003cbr\u003e \u003cbr\u003e Models for Stocks and Derivatives.\u003cbr\u003e \u003cbr\u003e Simulation Assessment of Multivariate and Robust Procedures in Statistical Process Control.\u003cbr\u003e \u003cbr\u003e Noise and Chaos.\u003cbr\u003e \u003cbr\u003e Bayesian Approaches.\u003cbr\u003e \u003cbr\u003e Resampling Based Tests.\u003cbr\u003e \u003cbr\u003e Optimization and Estimation in a Noisy World.\u003cbr\u003e \u003cbr\u003e Modeling the USA AIDS Epidemic: Exploration, Simulation and Conjecture.\u003cbr\u003e \u003cbr\u003e Appendices.\u003cbr\u003e \u003cbr\u003e Index.","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":49402542391639,"sku":"9780471251842","price":140.35,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780471251842.jpg?v=1730480712"},{"product_id":"linear-algebra-9780471308973","title":"Linear Algebra","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eReinforcing concepts with frequent exercises and extended applications, this introduction to the theoretical and computational aspects of linear algebra uses the MATLAB software to master the basic algorithms and to increase the speed and efficiency of computations.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003eMatrix Algebra.\u003cbr\u003e \u003cbr\u003e Vector Spaces and Linear Transformations.\u003cbr\u003e \u003cbr\u003e Orthogonality and Projections.\u003cbr\u003e \u003cbr\u003e Eigenvalues and Eigenvectors.\u003cbr\u003e \u003cbr\u003e The Spectral Theorem and Applications.\u003cbr\u003e \u003cbr\u003e Normal Forms.\u003cbr\u003e \u003cbr\u003e Appendices.\u003cbr\u003e \u003cbr\u003e Bibliography.\u003cbr\u003e \u003cbr\u003e Index.","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":49402561036631,"sku":"9780471308973","price":222.26,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780471308973.jpg?v=1730480756"},{"product_id":"data-engineering-fuzzy-mathematics-in-systems-theory-and-data-analysis-9780471416562","title":"Data Engineering Fuzzy Mathematics in Systems","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eThere are many situations in science and engineering where complex output data from a given system is used to formulate a model of how that system operates, or to simulate its response to different inputs. Applications include control, decision theory, and the emerging fields of bioinformatics.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e\"To cope with real world uncertainties and provide a philosophical and practical guide...several methodologies are presented...\" (SciTech Book News, Vol. 25, No. 4, December 2001)\u003cbr\u003e \u003cbr\u003e \"...certainly a book that should be in the library of any institution where research and advanced study in fuzzy systems are carried out.\" (Choice, Vol. 39, No. 7, March 2002)\u003cbr\u003e \u003cbr\u003e \"...well organized, easy to read, and self-contained.... I would recommend it to anyone interested in self-study of the basic ideas of fuzzy systems...\" (International Journal of General Systems, Vol. 31, No. 6, 2002)\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003ePreface.\u003cbr\u003e \u003cbr\u003e Acknowledgments.\u003cbr\u003e \u003cbr\u003e Introduction.\u003cbr\u003e \u003cbr\u003e System Analysis.\u003cbr\u003e \u003cbr\u003e Uncertainty Techniques.\u003cbr\u003e \u003cbr\u003e Learning from Data: System Identification.\u003cbr\u003e \u003cbr\u003e Propositions as Subsets of the Data Space.\u003cbr\u003e \u003cbr\u003e Fuzzy Systems and Identification.\u003cbr\u003e \u003cbr\u003e Random-Set Modelling and Identification.\u003cbr\u003e \u003cbr\u003e Certain Uncertainty.\u003cbr\u003e \u003cbr\u003e Fuzzy Inference Engines.\u003cbr\u003e \u003cbr\u003e Fuzzy Classification.\u003cbr\u003e \u003cbr\u003e Fuzzy Control.\u003cbr\u003e \u003cbr\u003e Fuzzy Mathematics.\u003cbr\u003e \u003cbr\u003e Summary.\u003cbr\u003e \u003cbr\u003e Appendices.\u003cbr\u003e \u003cbr\u003e Index.","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":49402590331223,"sku":"9780471416562","price":131.35,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780471416562.jpg?v=1730480873"},{"product_id":"combinatorial-optimization-33-wiley-series-in-discrete-mathematics-and-optimization-9780471558941","title":"Combinatorial Optimization 33 Wiley Series in","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eA complete, highly accessible introduction to one of today's most exciting areas of applied mathematics  One of the youngest, most vital areas of applied mathematics, combinatorial optimization integrates techniques from combinatorics, linear programming, and the theory of algorithms.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003eProblems and Algorithms.\u003cbr\u003e \u003cbr\u003e Optimal Trees and Paths.\u003cbr\u003e \u003cbr\u003e Maximum Flow Problems.\u003cbr\u003e \u003cbr\u003e Minimum-Cost Flow Problems.\u003cbr\u003e \u003cbr\u003e Optimal Matchings.\u003cbr\u003e \u003cbr\u003e Integrality of Polyhedra.\u003cbr\u003e \u003cbr\u003e The Traveling Salesman Problem.\u003cbr\u003e \u003cbr\u003e Matroids.\u003cbr\u003e \u003cbr\u003e NP and NP-Completeness.\u003cbr\u003e \u003cbr\u003e Appendix.\u003cbr\u003e \u003cbr\u003e Bibliography.\u003cbr\u003e \u003cbr\u003e Index.","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":49402631684439,"sku":"9780471558941","price":148.45,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780471558941.jpg?v=1730481050"},{"product_id":"thinking-recursively-9780471816522","title":"Thinking Recursively","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eThe process of solving large problems by breaking them down into smaller, more simple problems that have identical forms. Thinking Recursively: A small text to solve large problems. Concentrating on the practical value of recursion. this text, the first of its kind, is essential to computer science students' education.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003eThe Idea of Recursion.\u003cbr\u003e \u003cbr\u003e Mathematical Preliminaries.\u003cbr\u003e \u003cbr\u003e Recursive Functions.\u003cbr\u003e \u003cbr\u003e The Procedural Approach.\u003cbr\u003e \u003cbr\u003e The Tower of Hanoi.\u003cbr\u003e \u003cbr\u003e Permutations.\u003cbr\u003e \u003cbr\u003e Sorting.\u003cbr\u003e \u003cbr\u003e Intelligent Algorithms.\u003cbr\u003e \u003cbr\u003e Graphical Applications.\u003cbr\u003e \u003cbr\u003e Recursive Data.\u003cbr\u003e \u003cbr\u003e Implementation of Recursion.","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":49402676609367,"sku":"9780471816522","price":97.8,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780471816522.jpg?v=1730481199"},{"product_id":"garbage-collection-algorithms-for-automatic-dynamic-memory-management-9780471941484","title":"Garbage Collection  Algorithms for Automatic","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eModern software places increasing reliance on dynamic memory allocation, but its direct management is not only notoriously error--prone. Garbage collection eliminates many of these bugs. This reference presents each of the most important algorithms in detail, often with illustrations of its characteristic features and animations of its use.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003eThe Classical Algorithms.\u003cbr\u003e \u003cbr\u003e Reference Counting.\u003cbr\u003e \u003cbr\u003e Mark-Sweep Garbage Collection.\u003cbr\u003e \u003cbr\u003e Mark-Compact Garbage Collection.\u003cbr\u003e \u003cbr\u003e Copying Garbage Collection.\u003cbr\u003e \u003cbr\u003e Generational Garbage Collection.\u003cbr\u003e \u003cbr\u003e Incremental and Concurrent Garbage Collection.\u003cbr\u003e \u003cbr\u003e Garbage Collection for C. Garbage Collection for C++.\u003cbr\u003e \u003cbr\u003e Cache-Conscious Garbage Collection.\u003cbr\u003e \u003cbr\u003e Distributed Garbage Collection.\u003cbr\u003e \u003cbr\u003e Glossary.\u003cbr\u003e \u003cbr\u003e Bibliography.\u003cbr\u003e \u003cbr\u003e Index.","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":49402686734679,"sku":"9780471941484","price":55.76,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780471941484.jpg?v=1730481233"},{"product_id":"algorithms-and-data-structures-in-c-9780471963554","title":"Algorithms and Data Structures in C","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eThe aim of this work is to give breadth and depth to the C++ programmer's existing experience of the language. It presents a large number of algorithms, each of them implemented as ready-to-run (and standalone) programs.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003eSome Aspects of Programming in C++.\u003cbr\u003e \u003cbr\u003e Arithmetic.\u003cbr\u003e \u003cbr\u003e Sorting Arrays and Files.\u003cbr\u003e \u003cbr\u003e Stacks, Queues and Lists.\u003cbr\u003e \u003cbr\u003e Searching and String Processing.\u003cbr\u003e \u003cbr\u003e Binary Trees.\u003cbr\u003e \u003cbr\u003e B-trees.\u003cbr\u003e \u003cbr\u003e Tries, Priority Queues and File Compression.\u003cbr\u003e \u003cbr\u003e Graphs.\u003cbr\u003e \u003cbr\u003e Some Combinatorial Algorithms.\u003cbr\u003e \u003cbr\u003e Fundamentals of Interpreters and Compilers.\u003cbr\u003e \u003cbr\u003e Appendix.\u003cbr\u003e \u003cbr\u003e Bibliography.\u003cbr\u003e \u003cbr\u003e Index.","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":49402696434007,"sku":"9780471963554","price":56.0,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780471963554.jpg?v=1730481266"},{"product_id":"introduction-to-scientific-computing-9780471972662","title":"Introduction to Scientific Computing","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eThis book presents the basic scientific computing methods for the solution of partial differential equations (PDEs) as they occur in engineering problems. Programming codes in Fortran and C are included for each problem.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003eSome Partial Differential Equations.\u003cbr\u003e \u003cbr\u003e PROGRAMMING THE MODEL PROBLEM BY A FINITE ELEMENT METHOD.\u003cbr\u003e \u003cbr\u003e Introduction to the Finite Element Method: Energy Minimisation.\u003cbr\u003e \u003cbr\u003e Finite Element Method: Variational Formulation and Direct Methods.\u003cbr\u003e \u003cbr\u003e Finite Element Method: Optimisation of the Method.\u003cbr\u003e \u003cbr\u003e GENERAL ELLIPTIC PROBLEMS AND EVOLUTION PROBLEMS.\u003cbr\u003e \u003cbr\u003e Finite Element Method for General Elliptic Problems.\u003cbr\u003e \u003cbr\u003e Non-symmetric or Non-linear Partial Differential Equations.\u003cbr\u003e \u003cbr\u003e Evolution Problems: Finite Differences in Time.\u003cbr\u003e \u003cbr\u003e COMPLEMENTS ON NUMERICAL METHODS.\u003cbr\u003e \u003cbr\u003e Integral Methods for the Laplacian.\u003cbr\u003e \u003cbr\u003e Some Algorithms for Parallel Computing.\u003cbr\u003e \u003cbr\u003e Bibliography.\u003cbr\u003e \u003cbr\u003e Index.","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":49402702233943,"sku":"9780471972662","price":80.96,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780471972662.jpg?v=1730481281"},{"product_id":"automata-and-algebras-in-categories-37-mathematics-and-its-applications-9780792300106","title":"Automata and Algebras in Categories 37","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e","brand":"Springer","offers":[{"title":"Default Title","offer_id":49405068312919,"sku":"9780792300106","price":71.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780792300106.jpg?v=1730488576"}],"url":"https:\/\/bookcurl.com\/collections\/mathematical-theory-of-computation.oembed?page=14","provider":"Book Curl","version":"1.0","type":"link"}