Learn how to analyse and manage evolutionary and sequential user behaviours in modern networks, and how to optimize network performance by using indirect reciprocity, evolutionary games, and sequential decision making. Understand the latest theory without the need to go through the details of traditional game theory. With practical management tools to regulate user behaviour, and simulations and experiments with real data sets, this is an ideal tool for graduate students and researchers working in networking, communications, and signal processing.

Mathematical optimization is used in nearly all computer graphics applications, from computer vision to animation. This book teaches readers the core set of techniques that every computer graphics professional should understand in order to envision and expand the boundaries of what is possible in their work.
Study of this authoritative reference will help readers develop a very powerful tool- the ability to create and decipher mathematical models that can better realize solutions to even the toughest problems confronting computer graphics community today.
The authors are renowned researchers from IMPA, the Brazilian National Institute for Pure and Applied Mathematics.
*Distils down a vast and complex world of info on optimization into one short, self-contained volume especially for computer graphics
*Helps CG professionals identify the best technique for solving particular problems quickly, by categorizing the most effective algorithms by application
*Keeps readers current by supplementing the focus on key, classic methods with special end-of-chapter sections on cutting-edge developments

Lectures on Stochastic Programming: Modeling and Theory, Third Edition covers optimization problems involving uncertain parameters for which stochastic models are available. These problems occur in almost all areas of science and engineering. This substantial revision of the previous edition presents a modern theory of stochastic programming, including expanded coverage of sample complexity, risk measures, and distributionally robust optimization: Chapter 6 is updated and the interchangeability principle for risk measures is discussed in detail. Two new chapters, 'Distributionally Robust Stochastic Programming' (DRSP) and 'Computational Methods' provide readers with a solid understanding of emerging topics. Chapter 8 presents new material on formulation and numerical approaches to solving periodical multistage stochastic programs. This book is written for researchers and graduate students working on theory and applications of optimization.

This book introduces readers to benchmarking techniques in the stochastic environment, primarily stochastic data envelopment analysis (DEA), and provides stochastic models in DEA for the possibility of variations in inputs and outputs. It focuses on the application of theories and interpretations of the mathematical programs, which are combined with economic and organizational thinking. The book's main purpose is to shed light on the advantages of the different methods in deterministic and stochastic environments and thoroughly prepare readers to properly use these methods in various cases. Simple examples, along with graphical illustrations and real-world applications in industry, are provided for a better understanding. The models introduced here can be easily used in both theoretical and real-world evaluations. This book is intended for graduate and PhD students, advanced consultants, and practitioners with an interest in quantitative performance evaluation.

Highly entertaining text essential for anyone interested in Game Theory. Only basic understanding of arithmetic needed to grasp necessary aspects of two-, three-, four- and larger strategy games with two or more sets of inimical interests and a limitless array of zero-sum payoffs.

Optimal Design for Nonlinear Response Models discusses the theory and applications of model-based experimental design with a strong emphasis on biopharmaceutical studies. The book draws on the authors' many years of experience in academia and the pharmaceutical industry. While the focus is on nonlinear models, the book begins with an explanation of the key ideas, using linear models as examples. Applying the linearization in the parameter space, it then covers nonlinear models and locally optimal designs as well as minimax, optimal on average, and Bayesian designs. The authors also discuss adaptive designs, focusing on procedures with non-informative stopping. The common goals of experimental design-such as reducing costs, supporting efficient decision making, and gaining maximum information under various constraints-are often the same across diverse applied areas. Ethical and regulatory aspects play a much more prominent role in biological, medical, and pharmaceutical research. The authors address all of these issues through many examples in the book.

"The Journal of the Musical Arts in Africa" (JMAA) is a new journal that aims to combine ethnomusicological, musicological, music educational and performance-based research in a unique way to promote musical arts on the African continent. This internationally refereed journal incorporates the following: research in the musical arts (music, dance, theatre) and related disciplines; research based practical suggestions for the music teacher and general arts and culture teacher; methods of instrumental and vocal teaching (African, Western and Non-Western); and music technology.

Volume 2 applies the linear algebra concepts presented in Volume 1 to optimization problems which frequently occur throughout machine learning. This book blends theory with practice by not only carefully discussing the mathematical under pinnings of each optimization technique but by applying these techniques to linear programming, support vector machines (SVM), principal component analysis (PCA), and ridge regression. Volume 2 begins by discussing preliminary concepts of optimization theory such as metric spaces, derivatives, and the Lagrange multiplier technique for finding extrema of real valued functions. The focus then shifts to the special case of optimizing a linear function over a region determined by affine constraints, namely linear programming. Highlights include careful derivations and applications of the simplex algorithm, the dual-simplex algorithm, and the primal-dual algorithm. The theoretical heart of this book is the mathematically rigorous presentation of various nonlinear optimization methods, including but not limited to gradient decent, the Karush-Kuhn-Tucker (KKT) conditions, Lagrangian duality, alternating direction method of multipliers (ADMM), and the kernel method. These methods are carefully applied to hard margin SVM, soft margin SVM, kernel PCA, ridge regression, lasso regression, and elastic-net regression. Matlab programs implementing these methods are included.

Nonnegative matrix factorization (NMF) in its modern form has become a standard tool in the analysis of high-dimensional data sets. This book provides a comprehensive and up-to-date account of the most important aspects of the NMF problem and is the first to detail its theoretical aspects, including geometric interpretation, nonnegative rank, complexity, and uniqueness. It explains why understanding these theoretical insights is key to using this computational tool effectively and meaningfully. Nonnegative Matrix Factorization is accessible to a wide audience and is ideal for anyone interested in the workings of NMF. It discusses some new results on the nonnegative rank and the identifiability of NMF and makes available MATLAB codes for readers to run the numerical examples presented in the book. Graduate students starting to work on NMF and researchers interested in better understanding the NMF problem and how they can use it will find this book useful. It can be used in advanced undergraduate and graduate-level courses on numerical linear algebra and on advanced topics in numerical linear algebra and requires only a basic knowledge of linear algebra and optimization.

This book is addressed to researchers in operations research, data science and artificial intelligence. It collects selected contributions from the first hybrid "Optimization and Decision Science - ODS2021" international conference on the theme Optimization and Artificial Intelligence and Data Sciences, which was held in Rome 14-17 September 2021 and organized by AIRO, the Italian Operations Research Society and the Department of Statistical Sciences of Sapienza University of Rome. The book offers new and original contributions on different methodological optimization topics, from Support Vector Machines to Game Theory Network Models, from Mathematical Programming to Heuristic Algorithms, and Optimization Methods for a number of emerging problems from Truck and Drone delivery to Risk Assessment, from Power Networks Design to Portfolio Optimization. The articles in the book can give a significant edge to the general themes of sustainability and pollution reduction, distributive logistics, healthcare management in pandemic scenarios and clinical trials, distributed computing, scheduling, and many others. For these reasons, the book is aimed not only at researchers in the Operations Research community but also for practitioners facing decision-making problems in these areas and to students and researchers from other disciplines, including Artificial Intelligence, Computer Sciences, Finance, Mathematics, and Engineering.

Targeted at graduate students, researchers and practitioners in the field of science and engineering, this book gives a self-contained introduction to a measure-theoretic framework in laying out the definitions and basic concepts of random variables and stochastic diffusion processes. It then continues to weave into a framework of several practical tools and applications involving stochastic dynamical systems. These include tools for the numerical integration of such dynamical systems, nonlinear stochastic filtering and generalized Bayesian update theories for solving inverse problems and a new stochastic search technique for treating a broad class of non-convex optimization problems. MATLAB (R) codes for all the applications are uploaded on the companion website.

Features recent advances and new applications in graph edge coloring

Reviewing recent advances in the Edge Coloring Problem, "Graph Edge Coloring: Vizing's Theorem and Goldberg's Conjecture" provides an overview of the current state of the science, explaining the interconnections among the results obtained from important graph theory studies. The authors introduce many new improved proofs of known results to identify and point to possible solutions for open problems in edge coloring.

The book begins with an introduction to graph theory and the concept of edge coloring. Subsequent chapters explore important topics such as:

Use of Tashkinov trees to obtain an asymptotic positive solution to Goldberg's conjecture

Application of Vizing fans to obtain both known and new results

Kierstead paths as an alternative to Vizing fans

Classification problem of simple graphs

Generalized edge coloring in which a color may appear more than once at a vertex

This book also features first-time English translations of two groundbreaking papers written by Vadim Vizing on an estimate of the chromatic class of a p-graph and the critical graphs within a given chromatic class.

Written by leading experts who have reinvigorated research in the field, "Graph Edge Coloring" is an excellent book for mathematics, optimization, and computer science courses at the graduate level. The book also serves as a valuable reference for researchers interested in discrete mathematics, graph theory, operations research, theoretical computer science, and combinatorial optimization.

Algorithms are a dominant force in modern culture, and every indication is that they will become more pervasive, not less. The best algorithms are undergirded by beautiful mathematics. This text cuts across discipline boundaries to highlight some of the most famous and successful algorithms. Readers are exposed to the principles behind these examples and guided in assembling complex algorithms from simpler building blocks. Algorithms from THE BOOK: Incorporates Julia code for easy experimentation. Is written in clear, concise prose consistent with mathematical rigour. Includes a large number of classroom-tested exercises at the end of each chapter. Covers background material, often omitted from undergraduate courses, in the appendices. This textbook is aimed at first-year graduate and advanced undergraduate students. It will also serve as a convenient reference for professionals throughout the mathematical sciences, physical sciences, engineering, and the quantitative sectors of the biological and social sciences.

This book brings together papers of well-known specialists in game theory and adjacent problems. It presents the basic results in dynamic games, stochastic games, applications of game theoretical methods in ecology and economics and methodological aspects of game theory.

Game Theory & Applications Volume 4

This concise text contains the most commonly-encountered examination problems in the topic of Optimization Models and Methods, an important module in engineering and other disciplines where there exists an increasing need to operate optimally and sustainably under constraints, such as tighter resource availability, environmental consideration, and cost pressures. This book is comprehensive in coverage as it includes a diverse spectrum of problems from numerical open-ended questions that probe creative thinking to the relation of concepts to realistic settings. The book adopts many examples of design scenarios as context for curating sample problems. This will help students relate desktop problem-solving to tackling real-world problems. Succinct yet rigorous, with over a 100 pages of problems and corresponding worked solutions presented in detail, the book is ideal for students of engineering, applied science, and market analysis.

The game of Dots-and-Boxes, the popular game in which two players take turns connecting an array of dots to form squares, or "boxes" has long been considered merely a child's game. In this book, however, the author reveals the surprising complexity of the game, along with advanced strategies that will allow the reader to win at any level of gameplay desired. This book is an essential guide to the game of Dots-and-Boxes and its mathematical underpinnings. Chapters of strategy are interspersed with dozens of sample problems and their solutions. Furthermore, the strategies can be applied to several other games, such as Strings-and-Coins and Nimstring.

What if life is a game? Are you winning? Have you even decided what 'winning' is? Game design could be defined in many ways, but here the term is used to denote the practice of creating choices. Designing a game, in this sense, involves crafting limits, rewards, incentives, and risks in such a way that the person who interacts with the game - the player - makes choices that have consequences. Edward Castronova urges readers to think about the fundamentals of the human condition and compare them to different games that we all know. In some ways, life is like an idle game: providing unchallenging distractions that fit easily into a person's daily routine. In other ways, life is like the game Minesweeper: You poke in different places to learn about what you don't know, taking care to avoid big explosions. Or, life is like a role-playing game: You adopt a persona and speak your part, always seeking adventure. Bringing together questions relating to diverse fields - such as politics, economics, sociology and philosophy - Castronova persuades readers to broaden the scope of game design to answer questions about life's everyday obstacles. The object of this book is to take seriously the idea that life is a game. The goal is not to make readers wealthier or healthier. Its goal is to go on a journey into the human condition, with game design as a guide.

Game-theoretic probability and finance come of age Glenn Shafer and Vladimir Vovk's Probability and Finance, published in 2001, showed that perfect-information games can be used to define mathematical probability. Based on fifteen years of further research, Game-Theoretic Foundations for Probability and Finance presents a mature view of the foundational role game theory can play. Its account of probability theory opens the way to new methods of prediction and testing and makes many statistical methods more transparent and widely usable. Its contributions to finance theory include purely game-theoretic accounts of Ito's stochastic calculus, the capital asset pricing model, the equity premium, and portfolio theory. Game-Theoretic Foundations for Probability and Finance is a book of research. It is also a teaching resource. Each chapter is supplemented with carefully designed exercises and notes relating the new theory to its historical context. Praise from early readers "Ever since Kolmogorov's Grundbegriffe, the standard mathematical treatment of probability theory has been measure-theoretic. In this ground-breaking work, Shafer and Vovk give a game-theoretic foundation instead. While being just as rigorous, the game-theoretic approach allows for vast and useful generalizations of classical measure-theoretic results, while also giving rise to new, radical ideas for prediction, statistics and mathematical finance without stochastic assumptions. The authors set out their theory in great detail, resulting in what is definitely one of the most important books on the foundations of probability to have appeared in the last few decades." - Peter Grunwald, CWI and University of Leiden "Shafer and Vovk have thoroughly re-written their 2001 book on the game-theoretic foundations for probability and for finance. They have included an account of the tremendous growth that has occurred since, in the game-theoretic and pathwise approaches to stochastic analysis and in their applications to continuous-time finance. This new book will undoubtedly spur a better understanding of the foundations of these very important fields, and we should all be grateful to its authors." - Ioannis Karatzas, Columbia University

This book contains the contributions resulting from the 6th Italian-Japanese workshop on Geometric Properties for Parabolic and Elliptic PDEs, which was held in Cortona (Italy) during the week of May 20-24, 2019. This book will be of great interest for the mathematical community and in particular for researchers studying parabolic and elliptic PDEs. It covers many different fields of current research as follows: convexity of solutions to PDEs, qualitative properties of solutions to parabolic equations, overdetermined problems, inverse problems, Brunn-Minkowski inequalities, Sobolev inequalities, and isoperimetric inequalities.

Nonlinear Parameter Optimization Using RJohn C. Nash, Telfer School of Management, University of Ottawa, Canada

A systematic and comprehensive treatment of optimization software using RIn recent decades, optimization techniques have been streamlined by computational and artificial intelligence methods to analyze more variables, especially under non-linear, multivariable conditions, more quickly than ever before.Optimization is an important tool for decision science and for the analysis of physical systems used in engineering. Nonlinear Parameter Optimization with R explores the principal tools available in R for function minimization, optimization, and nonlinear parameter determination and features numerous examples throughout.

Nonlinear Parameter Optimization with R: - Provides a comprehensive treatment of optimization techniques- Examines optimization problems that arise in statistics and how to solve them using R- Enables researchers and practitioners to solve parameter determination problems- Presents traditional methods as well as recent developments in R- Is supported by an accompanying website featuring R code, examples and datasets

Researchers and practitioners who have to solve parameter determination problems who are users of R but are novices in the field optimization or function minimization will benefit from this book. It will also be useful for scientists building and estimating nonlinear models in various fields such as hydrology, sports forecasting, ecology, chemical engineering, pharmaco-kinetics, agriculture, economics and statistics.

This is the first book devoted entirely to Particle Swarm Optimization (PSO), which is a non-specific algorithm, similar to evolutionary algorithms, such as taboo search and ant colonies.

Since its original development in 1995, PSO has mainly been applied to continuous-discrete heterogeneous strongly non-linear numerical optimization and it is thus used almost everywhere in the world. Its convergence rate also makes it a preferred tool in dynamic optimization.

The series is designed to bring together those mathematicians who are seriously interested in getting new challenging stimuli from economic theories with those economists who are seeking effective mathematical tools for their research. A lot of economic problems can be formulated as constrained optimizations and equilibration of their solutions. Various mathematical theories have been supplying economists with indispensable machineries for these problems arising in economic theory. Conversely, mathematicians have been stimulated by various mathematical difficulties raised by economic theories.

Game Theory and Exercises introduces the main concepts of game theory, along with interactive exercises to aid readers' learning and understanding. Game theory is used to help players understand decision-making, risk-taking and strategy and the impact that the choices they make have on other players; and how the choices of those players, in turn, influence their own behaviour. So, it is not surprising that game theory is used in politics, economics, law and management. This book covers classic topics of game theory including dominance, Nash equilibrium, backward induction, repeated games, perturbed strategie s, beliefs, perfect equilibrium, Perfect Bayesian equilibrium and replicator dynamics. It also covers recent topics in game theory such as level-k reasoning, best reply matching, regret minimization and quantal responses. This textbook provides many economic applications, namely on auctions and negotiations. It studies original games that are not usually found in other textbooks, including Nim games and traveller's dilemma. The many exercises and the inserts for students throughout the chapters aid the reader's understanding of the concepts. With more than 20 years' teaching experience, Umbhauer's expertise and classroom experience helps students understand what game theory is and how it can be applied to real life examples. This textbook is suitable for both undergraduate and postgraduate students who study game theory, behavioural economics and microeconomics.