Many decision problems in Operations Research are defined on temporal networks, that is, workflows of time-consuming tasks whose processing order is constrained by precedence relations. For example, temporal networks are used to model projects, computer applications, digital circuits and production processes. Optimization problems arise in temporal networks when a decision maker wishes to determine a temporal arrangement of the tasks and/or a resource assignment that optimizes some network characteristic (e.g. the time required to complete all tasks). The parameters of these optimization problems (e.g. the task durations) are typically unknown at the time the decision problem arises. This monograph investigates solution techniques for optimization problems in temporal networks that explicitly account for this parameter uncertainty. We study several formulations, each of which requires different information about the uncertain problem parameters.

Invitation to Linear Operators is an essential guide to linear operators. Linear operator theory is a natural extension of matrix theory. Most of the books on linear operators are not easy to read for students and researchers who have insufficient knowledge of mathematics. This book explains, in easy to follow steps, the newest essential and fundamental results on linear operators based on matrix theory. The topics covered include: Hilbert spaces, fundamental properties of bounded linear operators, and further developments of bounded linear operators. This book will undoubtedly be of interest to undergraduate and graduate students as well as researchers who have an interest in linear operators and their various applications. It also serves as a reference book for advanced readers.

Proportional Optimization and Fairness is a long-needed attempt to reconcile optimization with apportionment in just-in-time (JIT) sequences and find the common ground in solving problems ranging from sequencing mixed-model just-in-time assembly lines through just-in-time batch production, balancing workloads in event graphs to bandwidth allocation internet gateways and resource allocation in computer operating systems. The book argues that apportionment theory and optimization based on deviation functions provide natural benchmarks for a process, and then looks at the recent research and developments in the field.

Individual chapters look at the theory of apportionment and just-in-time sequences; minimization of just-in-time sequence deviation; optimality of cyclic sequences and the oneness; bottleneck minimization; competition-free instances, Fraenkel s Conjecture, and optimal admission sequences; response time variability; applications to the Liu-Layland Problem and pinwheel scheduling; temporal capacity constraints and supply chain balancing; fair queuing and stride scheduling; and smoothing and batching.

Optimal Control and Optimization of Stochastic Supply Chain Systems examines its subject the context of the presence of a variety of uncertainties. Numerous examples with intuitive illustrations and tables are provided, to demonstrate the structural characteristics of the optimal control policies in various stochastic supply chains and to show how to make use of these characteristics to construct easy-to-operate sub-optimal policies. In Part I, a general introduction to stochastic supply chain systems is provided. Analytical models for various stochastic supply chain systems are formulated and analysed in Part II. In Part III the structural knowledge of the optimal control policies obtained in Part II is utilized to construct easy-to-operate sub-optimal control policies for various stochastic supply chain systems accordingly. Finally, Part IV discusses the optimisation of threshold-type control policies and their robustness. A key feature of the book is its tying together of the complex analytical models produced by the requirements of operational practice, and the simple solutions needed for implementation. The analytical models and theoretical analysis propounded in this monograph will be of benefit to academic researchers and graduate students looking at logistics and supply chain management from standpoints in operations research or industrial, manufacturing, or control engineering. The practical tools and solutions and the qualitative insights into the ideas underlying functional supply chain systems will be of similar use to readers from more industrially-based backgrounds.

Agent systems are being used to model complex systems like societies, markets and biological systems. In this book we investigate issues of agent systems related to convergence and interactivity using techniques from agent based modelling to simulate complex systems, and demonstrate that interactivity/exchange and convergence in multi-agent systems are issues that are significantly interrelated.

Topic and features: - Introduces the state of the art in multi-agent systems, with an emphasis on agent-based computational economics. - Sheds light on the fundamental concepts behind the stability of multi-agent systems. - Investigates knowledge exchange among agents, the rationale behind it and its effects on the ecosystem. - Explores how information provided through interaction with the system can be used to optimise its performance. - Describes a pricing strategy for a realistic large-scale distributed system.

This book supplies a comprehensive resource and will be invaluable reading for researchers and postgraduates studying this topic.

Joel Watson has refined his successful text to make it even more student-friendly. A number of sections have been added, and numerous chapters have been substantially revised. Dozens of new exercises have been added, along with solutions to selected exercises. Chapters are short and focused, with just the right amount of mathematical content and end-of-chapter exercises. New passages walk students through tricky topics.

The book presents a set of novel, efficient and systematic concurrent multiscale optimization methods by considering the distribution of the material in macro-scale and the unit-cell configuration design in micro-scale simultaneously. Different from the traditional optimization method that is performed in a single scale, the proposed methods could generate a great deal of improvements in structural performance through the multiscale structure-material concurrent optimum design.The proposed theory and methods are related to statics, dynamics, thermoelastics and the coupling of different physical fields. Therefore, it provides a comprehensive designing scheme when multiple factors are taken into account. For example, the designing scheme can have a great significance on enhancing the structural performances under coupled multi-physical fields, such as load bearing capacity, vibration resistance ability, and safety under thermal stress and so on.Several numerical examples are highlighted in this unique volume based on practical engineering applications. The examples collectively demonstrate drastically improved designs featuring excellent unit-cell configuration and highly regular macroscale material distribution in a variety of industrial applications.

The aim of this book is to incorporate Marshallian ideas such as external increasing returns and monopolistic competitions into the general equilibrium framework of Walrasian tradition. New chapters and sections have been added to this revised and expanded edition of General Equilibrium Analysis of Production and Increasing Returns (World Scientific, 2009).The new material includes a presentation of equilibrium existence and core equivalence theorems for an infinite horizon economy with a measure space of consumers. These results are currently the focus of extensive studies by mathematical theorists, and are obtained by an application of an advanced mathematical concept called saturated (super-atomless) measure space.The second major change is the inclusion of a simple toy model of a liberal society which implements the difference principle proposed by J Rawls as a principle of distributive justice. This new section opens up a possibility to connect theoretical economics and political philosophy.Thirdly, the author presents the marginal cost pricing equilibrium and discusses welfare properties of the external increasing returns, which also belong to Marshall/ Pigou tradition of the Cambridge school.Finally, a new mathematical appendix treats basics of singular homology theory. Although the fixed point theorem is originally a theorem of algebraic topology, most economic students know its proof only in the context of the differentiable manifold theory presented by J Milnor. Considering the significance of the fixed point theorem and its playing a key role in general equilibrium theory, the purpose of this new appendix is to provide readers with the idea of a proof of Brower's fixed point theorem from the 'right place'.This volume will be helpful for graduate students and researchers of mathematical economics, game theory, and microeconomics.

Stochastic games have an element of chance: the state of the next round is determined probabilistically depending upon players' actions and the current state. Successful players need to balance the need for short-term payoffs while ensuring future opportunities remain high. The various techniques needed to analyze these often highly non-trivial games are a showcase of attractive mathematics, including methods from probability, differential equations, algebra, and combinatorics. This book presents a course on the theory of stochastic games going from the basics through to topics of modern research, focusing on conceptual clarity over complete generality. Each of its chapters introduces a new mathematical tool - including contracting mappings, semi-algebraic sets, infinite orbits, and Ramsey's theorem, among others - before discussing the game-theoretic results they can be used to obtain. The author assumes no more than a basic undergraduate curriculum and illustrates the theory with numerous examples and exercises, with solutions available online.

Stochastic games have an element of chance: the state of the next round is determined probabilistically depending upon players' actions and the current state. Successful players need to balance the need for short-term payoffs while ensuring future opportunities remain high. The various techniques needed to analyze these often highly non-trivial games are a showcase of attractive mathematics, including methods from probability, differential equations, algebra, and combinatorics. This book presents a course on the theory of stochastic games going from the basics through to topics of modern research, focusing on conceptual clarity over complete generality. Each of its chapters introduces a new mathematical tool - including contracting mappings, semi-algebraic sets, infinite orbits, and Ramsey's theorem, among others - before discussing the game-theoretic results they can be used to obtain. The author assumes no more than a basic undergraduate curriculum and illustrates the theory with numerous examples and exercises, with solutions available online.

This volume brings together the collected contributions of Dirk Bergemann and Juuso Valimaki, on the theme of learning, experimentation and intertemporal incentives, spanning over two decades of thought (1996 to 2019).The collection starts with a comprehensive introduction to the recent developments of dynamic mechanism design with a primary focus on the quasilinear case. The authors describe socially optimal and revenue optimal dynamic mechanism. They cover models of sequential screening and revenue maximizing auctions with dynamically changing bidder types, and also discuss models of information management where the mechanism designer can control (at least partially) the stochastic process governing the agent's types.Consolidating the research agenda on learning, experimentation, and dynamic mechanism design, which has been prominent in the area of economic theory, the authors present for the first time the main results of the research agenda in this volume.

This text provides deep and comprehensive coverage of the mathematical background for data science, including machine learning, optimal recovery, compressed sensing, optimization, and neural networks. In the past few decades, heuristic methods adopted by big tech companies have complemented existing scientific disciplines to form the new field of Data Science. This text embarks the readers on an engaging itinerary through the theory supporting the field. Altogether, twenty-seven lecture-length chapters with exercises provide all the details necessary for a solid understanding of key topics in data science. While the book covers standard material on machine learning and optimization, it also includes distinctive presentations of topics such as reproducing kernel Hilbert spaces, spectral clustering, optimal recovery, compressed sensing, group testing, and applications of semidefinite programming. Students and data scientists with less mathematical background will appreciate the appendices that provide more background on some of the more abstract concepts.

A comprehensive introduction to convexity and optimization in Rn

This book presents the mathematics of finite dimensional constrained optimization problems. It provides a basis for the further mathematical study of convexity, of more general optimization problems, and of numerical algorithms for the solution of finite dimensional optimization problems. For readers who do not have the requisite background in real analysis, the author provides a chapter covering this material. The text features abundant exercises and problems designed to lead the reader to a fundamental understanding of the material.

Convexity and Optimization in Rn provides detailed discussion of:

• Requisite topics in real analysis
• Convex sets
• Convex functions
• Optimization problems
• Convex programming and duality
• The simplex method

A detailed bibliography is included for further study and an index offers quick reference. Suitable as a text for both graduate and undergraduate students in mathematics and engineering, this accessible text is written from extensively class-tested notes.

The Christoffel-Darboux kernel, a central object in approximation theory, is shown to have many potential uses in modern data analysis, including applications in machine learning. This is the first book to offer a rapid introduction to the subject, illustrating the surprising effectiveness of a simple tool. Bridging the gap between classical mathematics and current evolving research, the authors present the topic in detail and follow a heuristic, example-based approach, assuming only a basic background in functional analysis, probability and some elementary notions of algebraic geometry. They cover new results in both pure and applied mathematics and introduce techniques that have a wide range of potential impacts on modern quantitative and qualitative science. Comprehensive notes provide historical background, discuss advanced concepts and give detailed bibliographical references. Researchers and graduate students in mathematics, statistics, engineering or economics will find new perspectives on traditional themes, along with challenging open problems.

A host of digital affordances, including reduced cost production tools, open distribution platforms, and ubiquitous connectivity, have engendered the growth of indie games among makers and users, forcing critics to reconsider the question of who makes games and why. Taking seriously this new mode of cultural produciton compells analysts to reconsider the blurred boundaries and relations of makers, users and texts as well as their respective relationship to cultural power and hierarchy. The contributions to Indie Games in the Digital Age consider these questions and examine a series of firms, makers, games and scenes, ranging from giants like Nintendo and Microsoft to grassroots games like Cards Against Humanity and Stardew Valley, to chart more precisely the productive and instructive disruption that this new site of cultural production offers.

In this book, different quantitative approaches to the study of electoral systems have been developed: game-theoretic, decision-theoretic, statistical, probabilistic, combinatorial, geometric, and optimization ones. All the authors are prominent scholars from these disciplines. Quantitative approaches offer a powerful tool to detect inconsistencies or poor performance in actual systems. Applications to concrete settings such as EU, American Congress, regional, and committee voting are discussed.

This book develops a mathematical framework for modeling and optimizing interference-coupled multiuser systems. At the core of this framework is the concept of general interference functions, which provides a simple means of characterizing interdependencies between users. The entire analysis builds on the two core axioms scale-invariance and monotonicity.
The proposed network calculus has its roots in power control theory and wireless communications. It adds theoretical tools for analyzing the typical behavior of interference-coupled networks. In this way it complements existing game-theoretic approaches.
The framework should also be viewed in conjunction with optimization theory. There is a fruitful interplay between the theory of interference functions and convex optimization theory. By jointly exploiting the properties of interference functions, it is possible to design algorithms that outperform general-purpose techniques that only exploit convexity.
The title "network calculus" refers to the fact that the theory of interference functions constitutes a generic theoretical framework for the analysis of interference coupled systems. Certain operations within the framework are "closed," that is, combinations of interference functions are interference functions again. Also, certain properties are preserved under such operations. This, provides a methodology for analyzing different multiuser performance measures that can be expressed as interference functions or combinations of interference functions.

The Gradient Test: Another Likelihood-Based Test presents the latest on the gradient test, a large-sample test that was introduced in statistics literature by George R. Terrell in 2002. The test has been studied by several authors, is simply computed, and can be an interesting alternative to the classical large-sample tests, namely, the likelihood ratio (LR), Wald (W), and Rao score (S) tests. Due to the large literature about the LR, W and S tests, the gradient test is not frequently used to test hypothesis. The book covers topics on the local power of the gradient test, the Bartlett-corrected gradient statistic, the gradient statistic under model misspecification, and the robust gradient-type bounded-influence test.

Mathematics in Games, Sports, and Gambling: The Games People Play, Second Edition demonstrates how discrete probability, statistics, and elementary discrete mathematics are used in games, sports, and gambling situations. With emphasis on mathematical thinking and problem solving, the text draws on numerous examples, questions, and problems to explain the application of mathematical theory to various real-life games. This updated edition of a widely adopted textbook considers a number of popular games and diversions that are mathematically based or can be studied from a mathematical perspective. Requiring only high school algebra, the book is suitable for use as a textbook in seminars, general education courses, or as a supplement in introductory probability courses. New in this Edition: Many new exercises, including basic skills exercises More answers in the back of the book Expanded summary exercises, including writing exercises More detailed examples, especially in the early chapters An expansion of the discrete adjustment technique for binomial approximation problems New sections on chessboard puzzles that encourage students to develop graph theory ideas New review material on relations and functions Exercises are included in each section to help students understand the various concepts. The text covers permutations in the two-deck matching game so derangements can be counted. It introduces graphs to find matches when looking at extensions of the five-card trick and studies lexicographic orderings and ideas of encoding for card tricks. The text also explores linear and weighted equations in the section on the NFL passer rating formula and presents graphing to show how data can be compared or displayed. For each topic, the author includes exercises based on real games and actual sports data.

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.

In the last few years, Algorithms for Convex Optimization have revolutionized algorithm design, both for discrete and continuous optimization problems. For problems like maximum flow, maximum matching, and submodular function minimization, the fastest algorithms involve essential methods such as gradient descent, mirror descent, interior point methods, and ellipsoid methods. The goal of this self-contained book is to enable researchers and professionals in computer science, data science, and machine learning to gain an in-depth understanding of these algorithms. The text emphasizes how to derive key algorithms for convex optimization from first principles and how to establish precise running time bounds. This modern text explains the success of these algorithms in problems of discrete optimization, as well as how these methods have significantly pushed the state of the art of convex optimization itself.

Reactive Search and Intelligent Optimization is an excellent introduction to the main principles of reactive search, as well as an attempt to develop some fresh intuition for the approaches. The book looks at different optimization possibilities with an emphasis on opportunities for learning and self-tuning strategies. While focusing more on methods than on problems, problems are introduced wherever they help make the discussion more concrete, or when a specific problem has been widely studied by reactive search and intelligent optimization heuristics. Individual chapters cover reacting on the neighborhood; reacting on the annealing schedule; reactive prohibitions; model-based search; reacting on the objective function; relationships between reactive search and reinforcement learning; and much more. Each chapter is structured to show basic issues and algorithms; the parameters critical for the success of the different methods discussed; and opportunities for the automated tuning of these parameters.

Written by an international group of active researchers in the field, this volume presents innovative formulations and applied procedures for sensitivity analysis and structural design optimization. Eight chapters discuss subjects ranging from recent developments in the determination and application of topological gradients, to the use of evolutionary algorithms and meta-models to solve practical engineering problems. With such a comprehensive set of contributions, the book is a valuable source of information for graduate students and researchers entering or working in the matter.

The starting point for this monograph is the previously unknown connection between the Continuum Hypothesis and the saturation of the non-stationary ideal on 1; and the principle result of this monograph is the identification of a canonical model in which the Continuum Hypothesis is false. This is the first example of such a model and moreover the model can be characterized in terms of maximality principles concerning the universal-existential theory of all sets of countable ordinals. This model is arguably the long sought goal of the study of forcing axioms and iterated forcing but is obtained by completely different methods, for example no theory of iterated forcing whatsoever is required. The construction of the model reveals a powerful technique for obtaining independence results regarding the combinatorics of the continuum, yielding a number of results which have yet to be obtained by any other method. This monograph is directed to researchers and advanced graduate students in Set Theory. The second edition is updated to take into account some of the developments in the decade since the first edition appeared, this includes a revised discussion of -logic and related matters.