This book on optimization includes forewords by Michael I. Jordan, Zongben Xu and Zhi-Quan Luo. Machine learning relies heavily on optimization to solve problems with its learning models, and first-order optimization algorithms are the mainstream approaches. The acceleration of first-order optimization algorithms is crucial for the efficiency of machine learning. Written by leading experts in the field, this book provides a comprehensive introduction to, and state-of-the-art review of accelerated first-order optimization algorithms for machine learning. It discusses a variety of methods, including deterministic and stochastic algorithms, where the algorithms can be synchronous or asynchronous, for unconstrained and constrained problems, which can be convex or non-convex. Offering a rich blend of ideas, theories and proofs, the book is up-to-date and self-contained. It is an excellent reference resource for users who are seeking faster optimization algorithms, as well as for graduate students and researchers wanting to grasp the frontiers of optimization in machine learning in a short time.

Solving nonsmooth optimization (NSO) problems is critical in many practical applications and real-world modeling systems. The aim of this book is to survey various numerical methods for solving NSO problems and to provide an overview of the latest developments in the field. Experts from around the world share their perspectives on specific aspects of numerical NSO. The book is divided into four parts, the first of which considers general methods including subgradient, bundle and gradient sampling methods. In turn, the second focuses on methods that exploit the problem's special structure, e.g. algorithms for nonsmooth DC programming, VU decomposition techniques, and algorithms for minimax and piecewise differentiable problems. The third part considers methods for special problems like multiobjective and mixed integer NSO, and problems involving inexact data, while the last part highlights the latest advancements in derivative-free NSO. Given its scope, the book is ideal for students attending courses on numerical nonsmooth optimization, for lecturers who teach optimization courses, and for practitioners who apply nonsmooth optimization methods in engineering, artificial intelligence, machine learning, and business. Furthermore, it can serve as a reference text for experts dealing with nonsmooth optimization.

Steadily growing applications of game theory in modern science (including psychology, biology and economics) require sources to provide rapid access in both classical tools and recent developments to readers with diverse backgrounds. This book on game theory, its applications and mathematical methods, is written with this objective in mind.The book gives a concise but wide-ranging introduction to games including older (pre-game theory) party games and more recent topics like elections and evolutionary games and is generously spiced with excursions into philosophy, history, literature and politics. A distinguished feature is the clear separation of the text into two parts: elementary and advanced, which makes the book ideal for study at various levels.Part I displays basic ideas using no more than four arithmetic operations and requiring from the reader only some inclination to logical thinking. It can be used in a university degree course without any (or minimal) prerequisite in mathematics (say, in economics, business, systems biology), as well as for self-study by school teachers, social and natural scientists, businessmen or laymen.Part II is a rapid introduction to the mathematical methods of game theory, suitable for a mathematics degree course of various levels. It includes an advanced material not yet reflected in standard textbooks, providing links with the exciting modern developments in financial mathematics (rainbow option pricing), tropical mathematics, statistical physics (interacting particles) and discusses structural stability, multi-criteria differential games and turnpikes.To stimulate the mathematical and scientific imagination, graphics by a world-renowned mathematician and mathematics imaging artist, A T Fomenko, are used. The carefully selected works of this artist fit remarkably into the many ideas expressed in the book.

This book offers a valuable reference guide for researchers in distributed optimization and for senior undergraduate and graduate students alike. Focusing on the natures and functions of agents, communication networks and algorithms in the context of distributed optimization for networked control systems, this book introduces readers to the background of distributed optimization; recent developments in distributed algorithms for various types of underlying communication networks; the implementation of computation-efficient and communication-efficient strategies in the execution of distributed algorithms; and the frameworks of convergence analysis and performance evaluation. On this basis, the book then thoroughly studies 1) distributed constrained optimization and the random sleep scheme, from an agent perspective; 2) asynchronous broadcast-based algorithms, event-triggered communication, quantized communication, unbalanced directed networks, and time-varying networks, from a communication network perspective; and 3) accelerated algorithms and stochastic gradient algorithms, from an algorithm perspective. Finally, the applications of distributed optimization in large-scale statistical learning, wireless sensor networks, and for optimal energy management in smart grids are discussed.

Many important problems in global optimization, algebra, probability and statistics, applied mathematics, control theory, financial mathematics, inverse problems, etc. can be modeled as a particular instance of the Generalized Moment Problem (GMP). This book introduces, in a unified manual, a new general methodology to solve the GMP when its data are polynomials and basic semi-algebraic sets. This methodology combines semidefinite programming with recent results from real algebraic geometry to provide a hierarchy of semidefinite relaxations converging to the desired optimal value. Applied on appropriate cones, standard duality in convex optimization nicely expresses the duality between moments and positive polynomials. In the second part of this invaluable volume, the methodology is particularized and described in detail for various applications, including global optimization, probability, optimal context, mathematical finance, multivariate integration, etc., and examples are provided for each particular application.

Many important applications in global optimization, algebra, probability and statistics, applied mathematics, control theory, financial mathematics, inverse problems, etc. can be modeled as a particular instance of the Generalized Moment Problem (GMP).This book introduces a new general methodology to solve the GMP when its data are polynomials and basic semi-algebraic sets. This methodology combines semidefinite programming with recent results from real algebraic geometry to provide a hierarchy of semidefinite relaxations converging to the desired optimal value. Applied on appropriate cones, standard duality in convex optimization nicely expresses the duality between moments and positive polynomials.In the second part, the methodology is particularized and described in detail for various applications, including global optimization, probability, optimal control, mathematical finance, multivariate integration, etc., and examples are provided for each particular application.

This book is a collection of original papers presented at the International Conference on Computational Mathematics in Nanoelectronics and Astrophysics (CMNA 2018) held at the Indian Institute of Technology Indore, India, from 1 to 3 November 2018. It aims at presenting recent developments of computational mathematics in nanoelectronics, astrophysics and related areas of space sciences and engineering. These proceedings discuss the most advanced innovations, trends and real-world challenges encountered and their solutions with the application of computational mathematics in nanoelectronics, astrophysics and space sciences. From focusing on nano-enhanced smart technological developments to the research contributions of premier institutes in India and abroad on ISRO's future space explorations-this book includes topics from highly interdisciplinary areas of research. The book is of interest to researchers, students and practising engineers working in diverse areas of science and engineering, ranging from applied and computational mathematics to nanoelectronics, nanofabrications and astrophysics.

Andreu Mas-Colell revolutionized our understanding of competitive markets, price formation, and the behavior of market participants. General Equilibrium and Game Theory offers readers a compendium of his most important scholarly contributions, gathering in a single volume the groundbreaking papers that have solidified his standing as one of the preeminent economic theorists of our time. Built upon the foundations of neoclassical economics, Mas-Colell's work is distinguished by a mathematical and analytical elegance that brings theory closer to real-world situations. He overturns the standard assumption of general equilibrium theory-that markets are perfectly competitive and their participants are perfectly rational-and concludes that neither the law of supply and demand nor the existence of equilibrium prices depends on the rationality of agents. Similarly, Mas-Colell (working with Sergiu Hart) challenges classical game theory's reliance on rational behavior, demonstrating that adaptation and learning shape the dynamics of repeated games. Addressing central questions of finance, trade, industrial organization, and welfare economics, Mas-Colell shows the surprising power and versatility of differentiability and linear-space mathematical techniques, and he emphasizes the fruitfulness of cooperative game-theory approaches, such as Shapley value theory and the Bargaining Set, for understanding competition and distribution. General Equilibrium and Game Theory is a signal contribution to economic theory and an invaluable resource for anyone wishing to study the craft of a master of economic modeling.

Many of the complex problems faced by decision makers involve uncertainty as well as multiple conflicting objectives. This book provides a complete understanding of the types of objective functions that should be used in multiattribute decision making. By using tools such as preference, value, and utility functions, readers will learn state-of-the-art methods to analyze prospects to guide decision making and will develop a process that guarantees a defensible analysis to rationalize choices. Summarizing and distilling classical techniques and providing extensive coverage of recent advances in the field, the author offers practical guidance on how to make good decisions in the face of uncertainty. This text will appeal to graduate students and practitioners alike in systems engineering, operations research, business, management, government, climate change, energy, and healthcare.

To make the best decisions, you need the best information. However, because most issues in game theory are grey, nearly all recent research has been carried out using a simplified method that considers grey systems as white ones. This often results in a forecasting function that is far from satisfactory when applied to many real situations. Grey Game Theory and Its Applications in Economic Decision Making introduces classic game theory into the realm of grey system theory with limited knowledge. The book resolves three theoretical issues:

• A game equilibrium of grey game
• A reasonable explanation for the equilibrium of a grey matrix of static nonmatrix game issues based on incomplete information
• The Centipede Game paradox, which has puzzled theory circles for a long time and greatly enriched and developed the core methods of subgame Nash perfect equilibrium analysis as a result

The book establishes a grey matrix game model based on pure and mixed strategies. The author proposes the concepts of grey saddle points, grey mixed strategy solutions, and their corresponding structures and also puts forward the models and methods of risk measurement and evaluation of optimal grey strategies. He raises and solves the problems of grey matrix games. The book includes definitions of the test rules of information distortion experienced during calculation, the design of tokens based on new interval grey numbers, and new arithmetic laws to manipulate grey numbers. These features combine to provide a practical and efficient tool for forecasting real-life economic problems.

The First Comprehensive Book on the Subject Focusing on the underlying structure of a system, Optimal Design of Queueing Systems explores how to set the parameters of a queueing system, such as arrival and service rates, before putting it into operation. It considers various objectives, comparing individually optimal (Nash equilibrium), socially optimal, class optimal, and facility optimal flow allocations. After an introduction to basic design models, the book covers the optimal arrival rate model for a single-facility, single-class queue as well as dynamic algorithms for finding individually or socially optimal arrival rates and prices. It then examines several special cases of multiclass queues, presents models in which the service rate is a decision variable, and extends models and techniques to multifacility queueing systems. Focusing on networks of queues, the final chapters emphasize the qualitative properties of optimal solutions. Written by a long-time, recognized researcher on models for the optimal design and control of queues and networks of queues, this book frames the issues in the general setting of a queueing system. It shows how design models can control flow to achieve a variety of objectives.

Game theory is the study of strategic behavior in situations in which the decision makers are aware of the interdependence of their actions. This innovative textbook introduces students to the most basic principles of game theory - move and countermove - with an emphasis on real-world business and economic applications. Students with a background in principles of economics and business mathematics can readily understand most of the material.Demonstration problems in each chapter are designed to enhance the student's understanding of the concepts presented in the text. Many chapters include non-technical applications designed to further the student's intuitive understanding of strategic behavior. Case studies help underscore the usefulness of game theory for analyzing real-world situations. Each chapter concludes with a review and questions and exercises. An online Instructor's Manual with test bank is available to professors who adopt the text.

This book presents the best papers from the 1st International Conference on Mathematical Research for Blockchain Economy (MARBLE) 2019, held in Santorini, Greece. While most blockchain conferences and forums are dedicated to business applications, product development or Initial Coin Offering (ICO) launches, this conference focused on the mathematics behind blockchain to bridge the gap between practice and theory. Every year, thousands of blockchain projects are launched and circulated in the market, and there is a tremendous wealth of blockchain applications, from finance to healthcare, education, media, logistics and more. However, due to theoretical and technical barriers, most of these applications are impractical for use in a real-world business context. The papers in this book reveal the challenges and limitations, such as scalability, latency, privacy and security, and showcase solutions and developments to overcome them.

Optimization: Structure and Applications presents selected contributions from renowned researchers in the fields of operations research and industrial engineering. The book is divided into two parts; the first focuses on mathematical structure, and the second, on real-world applications. The work includes recent developments in several optimization-related topics such as decision theory, linear programming, turnpike theory, duality theory, convex analysis, and queuing theory. The applications presented include, but are not limited to, data imaging, network capacity allocation, water system management, and materials design.

The 21 self-contained chapters in this volume are devoted to the examination of modern trends and open problems in the field of optimization. This book will be a valuable tool not only to specialists interested in the technical detail and various applications presented, but also to researchers interested in building upon the book s theoretical results."

This book explores applications of computational intelligence in key and emerging fields of engineering, especially with regard to condition monitoring and fault diagnosis, inverse problems, decision support systems and optimization. These applications can be beneficial in a broad range of contexts, including: water distribution networks, manufacturing systems, production and storage of electrical energy, heat transfer, acoustic levitation, uncertainty and robustness of infinite-dimensional objects, fatigue failure prediction, autonomous navigation, nanotechnology, and the analysis of technological development indexes. All applications, mathematical and computational tools, and original results are presented using rigorous mathematical procedures. Further, the book gathers contributions by respected experts from 22 different research centers and eight countries: Brazil, Cuba, France, Hungary, India, Japan, Romania and Spain. The book is intended for use in graduate courses on applied computation, applied mathematics, and engineering, where tools like computational intelligence and numerical methods are applied to the solution of real-world problems in emerging areas of engineering.

This book introduces linear transformation and its key results, which have applications in engineering, physics, and various branches of mathematics. Linear transformation is a difficult subject for students. This concise text provides an in-depth overview of linear trans-formation. It provides multiple-choice questions, covers enough examples for the reader to gain a clear understanding, and includes exact methods with specific shortcuts to reach solutions for particular problems. Research scholars and students working in the fields of engineering, physics, and different branches of mathematics need to learn the concepts of linear transformation to solve their problems. This book will serve their need instead of having to use the more complex texts that contain more concepts then needed. The chapters mainly discuss the definition of linear transformation, properties of linear transformation, linear operators, composition of two or more linear transformations, kernels and range of linear transformation, inverse transformation, one-to-one and onto transformation, isomorphism, matrix linear transformation, and similarity of two matrices.

Analysis and Design of Discrete Part Production Lines provides a complete overview of production systems, investigating several production line problems, and describing the best approaches to the analysis of production line performance. Written by experts in the field of production and manufacturing research, this book also presents numerous techniques that can be used to describe and model various types of production lines.

Special Features:

* Includes access to a supplementary web-based software package, providing algorithms and examples, developed by distinguished experts of the field.

* Describes new results for evaluative techniques and design algorithms as well as several open problems in production line optimization.

* Presents in detail the theory and techniques that underlie production system management, design, and analysis, allowing the book to serve as an excellent introduction to newcomers in the field.

* Has potential for use in a graduate level course in industrial or manufacturing engineering, or in a business course with a manufacturing focus.

* Contains appendices providing an overview of several mathematical techniques employed to design and evaluate production line models.

Hex Strategy is the first book to offer a comprehensive look at the game of Hex, from its history and mathematical underpinnings to discussions of advanced playing techniques. This is first and foremost a book on strategy aimed at providing sufficient knowledge to play the game at any level desired. Numerous examples illustrate an algorithmic approach to the game. Hex Strategy is a book for board game enthusiasts, recreational mathematicians and programmers, or simply those who enjoy games and puzzles.

This book is a collection of certain lectures given at the Economics Department at Stanford University on the game theory. It contains material on this theory of rational behavior of people with nonidentical interests whose area of application includes economics, politics, and war.

As optimization techniques have developed, a gap has arisen between the people devising the methods and the people who actually need to use them. Research into methods is necessarily long-term and located usually in academic establishments; whereas the application of an optimization technique, normally in an industrial environment, has to be justified financially in the short term. The gap is probably inevitable; but there is no need for textbooks to reflect it. Teaching of optimization techniques separately from their connection with applications is pointless. This book gives a detailed exposition of the techniques.

In this first volume, T. A. J. Nicholson demonstrates the full range of techniques available to the practitioner for the solution of varying problems. For each technique, the background reasoning behind its development is explained in simple terms; where helpful it is supported by a geometrical argument; and the iterative algorithm for finding the optimum is defined clearly. These steps enable the reader not only to see plainly what is happening in the method but also to reach a level of understanding necessary to write computer programs for optimization techniques.

Problems are tackled in the same way--by searching a feasible region for an optimum. This approach helps the reader to develop the most essential of all skills--selecting appropriate techniques for different circumstances. The numerous worked examples in the text, supported by worked solutions, and the exercises at the end of the chapters are important aids to learning and to teachers. This book serves as an introduction to optimization techniques for students as well as a reference work for the practitioner in business and industry.

"T. A. J. Nicholson" is Senior Lecturer at the London Business School with research and consulting interests in industrial control systems.

This original, quantitatively oriented analysis applies the theory of the core to define competition in order to describe and deduce the consequences of competitive and non-competitive behavior. Written by one of the world's leading mathematical economists, the book is mathematically rigorous. No other book is currently available giving a game theoretic analysis of competition with basic mathematical tools.

Economic theorists have been working on a new and fundamental approach to the theory of competition and market structure, an approach inspired by appreciation of the earlier work of Edgeworth and Bohm-Bawerk and making use of the new tools of the theory of games as developed by von Neumann and Morgenstern. This new approach bases itself on the analysis of competitive behavior and its implications for the characteristics of market equilibrium rather than on assumptions about the characteristics of competitive and monopolistic markets. Its central concept is "the theory of the core of the market," and it is concerned, with the conditions under which markets will or will not achieve the characteristics of uniform prices and welfare optimality.

Telser provides a number of insights into the symptoms of competition, when and how competition is bought into play, the mechanisms of competition and collusion, the results of competition and collusion, and the results of competition and collusion for the economy and for the general public. Many misconceptions about the nature of a competitive equilibrium are dispelled. The book is not only a mathematical analysis of core price theory but also contains extensive empirical research in private industry. These empirical findings, from research pursued over several years, enhance understanding of how competition works and of the determinants of the returns to manufacturing industries.

"Lester G. Telser" is professor emeritus of economics at the University of Chicago. He is one of the world's leading mathematical economists; he has been a Visiting Research Fellow, Cowles Foundation for Research in Economics, Yale University; Ford Foundation Faculty Research Fellow; and assistant professor of economics, Iowa State University. In 2005 he received the St. Clair Drake award from Roosevelt University.

Computational optimization methods have matured over the last few years due to extensive research by applied mathematicians and engineers. These methods have been applied to many practical applications. Several general-purpose optimization programs and programs for specific engineering applications have become available to solve particular optimization problems.Written by leading researchers in the field of optimization, this highly readable book covers state-of-the-art computational algorithms as well as applications of optimization to structural and mechanical systems. Formulations of the problems and numerical solutions are presented, and topics requiring further research are also suggested.

The Distinguished Dissertation series is published on behalf of the Conference of Professors and Heads of Computing and The British Computer Society, who annually select the best British PhD dissertations in computer science for publication. The dissertations are selected on behalf of the CPHC by a panel of eight academics. Each dissertation chosen makes a noteworthy contribution to the subject and reaches a high standard of exposition, placing all results clearly in the context of computer science as a whole. In this way computer scientists with significantly different interests are able to grasp the essentials - or even find a means of entry - to an unfamiliar research topic. This book develops a theory of game semantics, a recently discovered setting for modelling and reasoning about sequential programming languages, suitable for interpreting higher-order functional languages with rich type structure, and applies it to constr uct a fully abstract model of the metalanguage FPC.

This book is devoted to the study of a class of optimal control problems arising in mathematical economics, related to the Robinson-Solow-Srinivasan (RSS) model. It will be useful for researches interested in the turnpike theory, infinite horizon optimal control and their applications, and mathematical economists. The RSS is a well-known model of economic dynamics that was introduced in the 1960s and as many other models of economic dynamics, the RSS model is determined by an objective function (a utility function) and a set-valued mapping (a technology map). The set-valued map generates a dynamical system whose trajectories are under consideration and the objective function determines an optimality criterion. The goal is to find optimal trajectories of the dynamical system, using the optimality criterion. Chapter 1 discusses turnpike properties for some classes of discrete time optimal control problems. Chapter 2 present the description of the RSS model and discuss its basic properties. Infinite horizon optimal control problems, related to the RSS model are studied in Chapter 3. Turnpike properties for the RSS model are analyzed in Chapter 4. Chapter 5 studies infinite horizon optimal control problems related to the RSS model with a nonconcave utility function. Chapter 6 focuses on infinite horizon optimal control problems with nonautonomous optimality criterions. Chapter 7 contains turnpike results for a class of discrete-time optimal control problems. Chapter 8 discusses the RSS model and compares different optimality criterions. Chapter 9 is devoted to the study of the turnpike properties for the RSS model. In Chapter 10 the one-dimensional autonomous RSS model is considered and the continuous time RSS model is studied in Chapter 11.

This established textbook is noted for its coverage of optimization methods that are of practical importance. It provides a thorough treatment of standard methods such as linear and quadratic programming, Newton-like methods and the conjugate gradient method. The theoretical aspects of the subject include an extended treatment of optimality conditions and the significance of Lagrange multipliers. The relevance of convexity theory to optimization is also not neglected. A significant proportion of the book is devoted to the solution of nonlinear problems, with an authoritative treatment of current methodology. Thus state of the art techniques such as the BFGS method, trust region methods and the SQP method are described and analysed. Other features are an extensive treatment of nonsmooth optimization and the L₁ penalty function. Contents Part 1 Unconstrained Optimization Part 2 Constrained Optimization

1. Introduction
2. Structure of Methods
3. Newton-like Methods
4. Conjugate Direction Methods
5. Restricted Step Methods
6. Sums of Squares and Nonlinear Equations
7. Introduction
8. Linear Programming
9. The Theory of Constrained Optimization
10. Quadratic Programming
11. General Linearly Constrained Optimization
12. Nonlinear Programming
13. Other Optimization Problems