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.

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.

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.

In Mathematical Analysis and Optimization for Economists, the author aims to introduce students of economics to the power and versatility of traditional as well as contemporary methodologies in mathematics and optimization theory; and, illustrates how these techniques can be applied in solving microeconomic problems. This book combines the areas of intermediate to advanced mathematics, optimization, and microeconomic decision making, and is suitable for advanced undergraduates and first-year graduate students. This text is highly readable, with all concepts fully defined, and contains numerous detailed example problems in both mathematics and microeconomic applications. Each section contains some standard, as well as more thoughtful and challenging, exercises. Solutions can be downloaded from the CRC Press website. All solutions are detailed and complete. Features Contains a whole spectrum of modern applicable mathematical techniques, many of which are not found in other books of this type. Comprehensive and contains numerous and detailed example problems in both mathematics and economic analysis. Suitable for economists and economics students with only a minimal mathematical background. Classroom-tested over the years when the author was actively teaching at the University of Hartford. Serves as a beginner text in optimization for applied mathematics students. Accompanied by several electronic chapters on linear algebra and matrix theory, nonsmooth optimization, economic efficiency, and distance functions available for free on www.routledge.com/9780367759018.

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.

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.

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."

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.

A 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. Because of its success in solving difficult problems in areas from telecommunications to VLSI, from product distribution to airline crew scheduling, the field has seen a ground swell of activity over the past decade. Combinatorial Optimization is an ideal introduction to this mathematical discipline for advanced undergraduates and graduate students of discrete mathematics, computer science, and operations research. Written by a team of recognized experts, the text offers a thorough, highly accessible treatment of both classical concepts and recent results. The topics include:

• Network flow problems
• Optimal matching
• Integrality of polyhedra
• Matroids
• NP-completeness

While mathematically sophisticated methods can be used to better understand and improve processes, the nonlinear nature of food processing models can make their dynamic optimization a daunting task. With contributions from a virtual who s who in the food processing industry, Optimization in Food Engineering evaluates the potential uses and limitations of optimization techniques for food processing, including classical methods, artificial intelligence-genetic algorithms, multi-objective optimization procedures, and computational fluid dynamics.

The book begins by delineating the fundamentals and methods for analytical and numerical procedures. It then covers optimization techniques and how they specifically apply to food processing. The final section digs deep into fundamental food processes and provides detailed explanation and examples from the most experienced and published authors in the field. This includes a range of processes from optimization strategies for improving the performance of batch reactors to the optimization of conventional thermal processing, microwave heating, freeze drying, spray drying, and refrigeration systems, to structural optimization techniques for developing beverage containers, optimization approaches for impingement processing, and optimal operational planning methodologies. Each chapter presents the required parameters for the given process with the optimization procedure to apply.

An increasing part of the food processor s job is to optimize systems to squeeze more dollars out of overhead to offset rising utility and transportation costs. Logically combining optimization techniques from many sources into a single volume focused on food production processes, this book provides real solutions to increases in energy, healthcare, and product liability costs that impact the bottom line in food production.

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.

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.

Features Provides a uniquely historical perspective on the mathematical underpinnings of a comprehensive list of games Suitable for a broad audience of differing mathematical levels. Anyone with a passion for games, game theory, and mathematics will enjoy this book, whether they be students, academics, or game enthusiasts Covers a wide selection of topics at a level that can be appreciated on a historical, recreational, and mathematical level.

This book uses asymptotic methods to obtain simple approximate analytic solutions to various problems within mechanics, notably wave processes in heterogeneous materials. Presenting original solutions to common issues within mechanics, this book builds upon years of research to demonstrate the benefits of implementing asymptotic techniques within mechanical engineering and material science. Focusing on linear and nonlinear wave phenomena in complex micro-structured solids, the book determines their global characteristics through analysis of their internal structure, using homogenization and asymptotic procedures, in line with the latest thinking within the field. The book's cutting-edge methodology can be applied to optimal design, non-destructive control and in deep seismic sounding, providing a valuable alternative to widely used numerical methods. Using case studies, the book covers topics such as elastic waves in nonhomogeneous materials, regular and chaotic dynamics based on continualisation and discretization and vibration localization in 1D Linear and Nonlinear lattices. The book will be of interest to students, research engineers, and professionals specialising in mathematics and physics as well as mechanical and civil engineering.

Seeking sparse solutions of underdetermined linear systems is required in many areas of engineering and science such as signal and image processing. The efficient sparse representation becomes central in various big or high-dimensional data processing, yielding fruitful theoretical and realistic results in these fields. The mathematical optimization plays a fundamentally important role in the development of these results and acts as the mainstream numerical algorithms for the sparsity-seeking problems arising from big-data processing, compressed sensing, statistical learning, computer vision, and so on. This has attracted the interest of many researchers at the interface of engineering, mathematics and computer science. Sparse Optimization Theory and Methods presents the state of the art in theory and algorithms for signal recovery under the sparsity assumption. The up-to-date uniqueness conditions for the sparsest solution of underdertemined linear systems are described. The results for sparse signal recovery under the matrix property called range space property (RSP) are introduced, which is a deep and mild condition for the sparse signal to be recovered by convex optimization methods. This framework is generalized to 1-bit compressed sensing, leading to a novel sign recovery theory in this area. Two efficient sparsity-seeking algorithms, reweighted l1-minimization in primal space and the algorithm based on complementary slackness property, are presented. The theoretical efficiency of these algorithms is rigorously analysed in this book. Under the RSP assumption, the author also provides a novel and unified stability analysis for several popular optimization methods for sparse signal recovery, including l1-mininization, Dantzig selector and LASSO. This book incorporates recent development and the author's latest research in the field that have not appeared in other books.

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.

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 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.

An Application-Oriented Introduction to Essential Optimization Concepts and Best Practices Optimization is an inherent human tendency that gained new life after the advent of calculus; now, as the world grows increasingly reliant on complex systems, optimization has become both more important and more challenging than ever before. Engineering Optimization provides a practically-focused introduction to modern engineering optimization best practices, covering fundamental analytical and numerical techniques throughout each stage of the optimization process. Although essential algorithms are explained in detail, the focus lies more in the human function: how to create an appropriate objective function, choose decision variables, identify and incorporate constraints, define convergence, and other critical issues that define the success or failure of an optimization project. Examples, exercises, and homework throughout reinforce the author's "do, not study" approach to learning, underscoring the application-oriented discussion that provides a deep, generic understanding of the optimization process that can be applied to any field. Providing excellent reference for students or professionals, Engineering Optimization Describes and develops a variety of algorithms, including gradient based (such as Newton's, and Levenberg-Marquardt), direct search (such as Hooke-Jeeves, Leapfrogging, and Particle Swarm), along with surrogate functions for surface characterization Provides guidance on optimizer choice by application, and explains how to determine appropriate optimizer parameter values Details current best practices for critical stages of specifying an optimization procedure, including decision variables, defining constraints, and relationship modeling Provides access to software and Visual Basic macros for Excel on the companion website, along with solutions to examples presented in the book Clear explanations, explicit equation derivations, and practical examples make this book ideal for use as part of a class or self-study, assuming a basic understanding of statistics, calculus, computer programming, and engineering models. Anyone seeking best practices for "making the best choices" will find value in this introductory resource.

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.

Real-life decisions are usually made in the state of uncertainty such as randomness and fuzziness. How do we model optimization problems in uncertain environments? How do we solve these models? In order to answer these questions, this book provides a self-contained, comprehensive and up-to-date presentation of uncertain programming theory, including numerous modeling ideas, hybrid intelligent algorithms, and applications in system reliability design, project scheduling problem, vehicle routing problem, facility location problem, and machine scheduling problem. Researchers, practitioners and students in operations research, management science, information science, system science, and engineering will find this work a stimulating and useful reference.