"Addresses the key topic in combinatorial synthesis--how to optimize the quality of a combinatorial library--by determining the usefulness of synthesized compunds, the reliability of biological assay results, and analyzing acadmic and industrial applications, real-world examples, and case studies of successful and unsuccessful technologies."

This book includes papers presented at the ISDG12-GTM2019 International Meeting on Game Theory, as a joint meeting of the 12th International ISDG Workshop and the 13th "International Conference on Game Theory and Management", held in St. Petersburg in July 2019. The topics cover a wide range of game-theoretic models and include both theory and applications, including applications to management.

This book is about algebraic and differential methods, as well as fractional calculus, applied to diagnose and reject faults in nonlinear systems, which are of integer or fractional order. This represents an extension of a very important and widely studied problem in control theory, namely fault diagnosis and rejection (using differential algebraic approaches), to systems presenting fractional dynamics, i.e. systems whose dynamics are represented by derivatives and integrals of non-integer order. The authors offer a thorough overview devoted to fault diagnosis and fault-tolerant control applied to fractional-order and integer-order dynamical systems, and they introduce new methodologies for control and observation described by fractional and integer models, together with successful simulations and real-time applications. The basic concepts and tools of mathematics required to understand the methodologies proposed are all clearly introduced and explained. Consequently, the book is useful as supplementary reading in courses of applied mathematics and nonlinear control theory. This book is meant for engineers, mathematicians, physicists and, in general, to researchers and postgraduate students in diverse areas who have a minimum knowledge of calculus. It also contains advanced topics for researchers and professionals interested in the area of states and faults estimation.

This book systematically discusses nonlinear interval optimization design theory and methods. Firstly, adopting a mathematical programming theory perspective, it develops an innovative mathematical transformation model to deal with general nonlinear interval uncertain optimization problems, which is able to equivalently convert complex interval uncertain optimization problems to simple deterministic optimization problems. This model is then used as the basis for various interval uncertain optimization algorithms for engineering applications, which address the low efficiency caused by double-layer nested optimization. Further, the book extends the nonlinear interval optimization theory to design problems associated with multiple optimization objectives, multiple disciplines, and parameter dependence, and establishes the corresponding interval optimization models and solution algorithms. Lastly, it uses the proposed interval uncertain optimization models and methods to deal with practical problems in mechanical engineering and related fields, demonstrating the effectiveness of the models and methods.

This book includes up-to-date contributions in the broadly defined area of probabilistic analysis of voting rules and decision mechanisms. Featuring papers from all fields of social choice and game theory, it presents probability arguments to allow readers to gain a better understanding of the properties of decision rules and of the functioning of modern democracies. In particular, it focuses on the legacy of William Gehrlein and Dominique Lepelley, two prominent scholars who have made important contributions to this field over the last fifty years. It covers a range of topics, including (but not limited to) computational and technical aspects of probability approaches, evaluation of the likelihood of voting paradoxes, power indices, empirical evaluations of voting rules, models of voters' behavior, and strategic voting. The book gathers articles written in honor of Gehrlein and Lepelley along with original works written by the two scholars themselves.

John von Neumann invented the digital computer, played a key role in the development of the atom bomb, constructed a branch of mathematics known as game theory, and became a defender of a movement to bomb the Russians before they could bomb us. Now comes a biography of this controversial genius and an exploration of his greatest idea--one that nearly triggered a nuclear war in 1950. Photographs.

Economic archaeology and ancient economic history have boomed the past decades. The former thanks to greatly enhanced techniques to identify, collect, and interpret material remains as proxies for economic interactions and performance; the latter by embracing the frameworks of new institutional economics. Both disciplines, however, still have great difficulty talking with each other. There is no reliable method to convert ancient proxy-data into the economic indicators used in economic history. In turn, the shared cultural belief-systems underlying institutions and the symbolic ways in which these are reproduced remain invisible in the material record. This book explores ways to bring both disciplines closer together by building a theoretical and methodological framework to evaluate and integrate archaeological proxy-data in economic history research. Rather than the linear interpretations offered by neoclassical or neomalthusian models, we argue that complexity economics, based on system theory, offers a promising way forward.

This book includes selected papers presented at the Indo-French Seminar on Optimization, Variational Analysis and Applications (IFSOVAA-2020), held at the Department of Mathematics, Institute of Science, Banaras Hindu University, Varanasi, India, from 2-4 February 2020. The book discusses current optimization problems and their solutions by using the powerful tool of variational analysis. Topics covered in this volume include set optimization, multiobjective optimization, mathematical programs with complementary, equilibrium, vanishing and switching constraints, copositive optimization, interval-valued optimization, sequential quadratic programming, bound-constrained optimization, variational inequalities, and more. Several applications in different branches of applied mathematics, engineering, economics, finance, and medical sciences have been included. Each chapter not only provides a detailed survey of the topic but also builds systematic theories and suitable algorithms to deduce the most recent findings in literature. This volume appeals to graduate students as well as researchers and practitioners in pure and applied mathematics and related fields that make use of variational analysis in solving optimization problems.

Gaming the Market: Applying Game Theory to Create Winning Trading Strategies is the first book to show investors how game theory is applicable to decisions about buying and selling stocks, bonds, mutual funds, futures, and options. As a practical trading guide, Gaming the Market will help investors master this revolutionary approach, and employ it to their advantage.

Although game theory has been studied since the 1940s, it has only recently been applied to the world of finance. Game theory champions garnered the 1994 Nobel Prize in Economics, and, today, this theory is used to analyze everything from the baseball strike to FCC auctions. Increasingly, game theory is making its mark as a potent tool for traders. In Gaming the Market, economist Ronald B. Shelton provides a model that enables traders to predict profitability and, as a result, make effective buy and sell decisions.

Stated simply, game theory is the study of conflict based on a formal approach to decision making that views decisions as choices made in a game. Whether playing individually or in a group, each player in a conflict has more than one course of action available to him, and the outcome of the "game" depends on the interaction of the strategies pursued by each. Shelton offers real-world examples that reveal how the principles of game theory drive financial markets --and how these same principles can be used to develop winning investment strategies. Through Shelton's organized and precise explanations--he uses familiar games such as chess and checkers to illustrate his points --readers gain a solid understanding of the key principles of game theory before applying them to actual financial market situations.

Gaming the Market examines the interaction between price fluctuations and risk acceptance levels and gradually constructs a game theory model which proves that there are probability-based formulas for determining the profitability of any given trade.

With appendixes on T-Bond futures, mathematical representations of the model, and QuickBasic code for calculating relative frequencies, Gaming the Market provides a thorough overview of the rules and strategies of game theory. This indispensable reference will prove invaluable to novice and seasoned players alike.

Are the markets a game? What are the rules? Who are the players?

How can you, as a player, come up with a winning strategy?

Now, acclaimed economist Ronald B. Shelton shows you how to master the power of game theory in the first trader's guide to this revolutionary approach to investment decisions!

"It's not often that a refreshingly new idea appears in the field of trading strategies or risk management, but Ronald B. Shelton has taken pieces from game theory and betting strategies and transformed them into a new, visual way to make trading decisions. . . . He has been able to put a value on trading situations which can increase your ability to manage risk as well as clarify expectations --both essential ingredients for success." --from the Foreword by Perry Kaufman author of The New Commodity Trading Systems and Methods.

"Gaming the Market is a very welcome and most useful new guide to playing profitably in the biggest and most complex game ever devised -- speculating in the financial markets. Investors and traders who study this book will gain valuable insights into the real nature of the markets and willlearn how to play the game to win." --Thomas A. Bierovic, President, Synergy Futures.

"Ronald B. Shelton has extended the field of excursion analysis with an innovative and provocative book that is sure to be widely read--and controversial. By examining the actual distributions of price excursion, he shows a technique to estimate your odds going in on a new position, and within the context of game theory, how to evaluate those chances. All traders and analysts seeking objective bases for trading will want to read this book." --John Sweeney, Technical Editor, Technical Analysis of Stocks and Commodities magazine.

This contributed volume presents some of the latest research related to model order reduction of complex dynamical systems with a focus on time-dependent problems. Chapters are written by leading researchers and users of model order reduction techniques and are based on presentations given at the 2019 edition of the workshop series Model Reduction of Complex Dynamical Systems - MODRED, held at the University of Graz in Austria. The topics considered can be divided into five categories: system-theoretic methods, such as balanced truncation, Hankel norm approximation, and reduced-basis methods; data-driven methods, including Loewner matrix and pencil-based approaches, dynamic mode decomposition, and kernel-based methods; surrogate modeling for design and optimization, with special emphasis on control and data assimilation; model reduction methods in applications, such as control and network systems, computational electromagnetics, structural mechanics, and fluid dynamics; and model order reduction software packages and benchmarks. This volume will be an ideal resource for graduate students and researchers in all areas of model reduction, as well as those working in applied mathematics and theoretical informatics.

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.

Handbook of Optimization in Medicine is devoted to examining the dramatic increase in the application of effective optimization techniques to the delivery of health care. The articles, written by experts, focus on models and algorithms that have led to more efficient and sophisticated treatments of patients.

Topics covered include: optimization in medical imaging, classification and data mining with medical applications, treatment of epilepsy and other brain disorders, treatment of head-and-neck, prostate, and other cancers using conventional conformal and intensity-modulated radiation therapy as well as proton therapy, treatment selection for breast cancer based on new classification schemes, optimization for the genome project, optimal timing of organ transplants.

This book presents the best papers from the 2nd International Conference on Mathematical Research for Blockchain Economy (MARBLE) 2020, held in Vilamoura, Portugal. 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. Blockchain Technology has been considered as the most fundamental and revolutionising invention since the Internet. 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.

This book presents simple design paradigms related to lightweight design, that are derived from an in-depth and theoretically sound analysis based on Pareto theory. It uses numerous examples, including torsion and inflated tubes, to fully explain the theories discussed. Lightweight Construction Principles begins by defining terms in relation to engineering design and optimal design of complex mechanical systems. It then discusses the analytical derivation of the Pareto-optimal set, before applying analytical formulae to optimal design of bent beams. The book moves through numerous case studies of different beam and tube construction including beams subject to bending, thin walled tubes under torsion and truss structures. This book will be of interest to researchers and graduate students in the field of structural optimisation and multi-objective optimization, as well as to practitioners such as design engineers.

Matrix Analysis and Computations introduces the basics of matrix analysis and presents representative methods and their corresponding theories in matrix computations. In this textbook, readers will find: The matrix theory necessary for direct and iterative methods for solving systems of linear equations. Systematic methods and rigorous theory on matrix splitting iteration methods and Krylov subspace iteration methods. Current results on preconditioning and iterative methods for solving standard and generalized saddle-point linear systems. Exercises at the end of each chapter for applying learned methods. This book is intended for graduate students, researchers, and engineers interested in matrix analysis and matrix computations. It is appropriate for the following courses: Advanced Numerical Analysis, Special Topics on Numerical Analysis, Topics on Data Science, Topics on Numerical Optimization, and Topics on Approximation Theory.

This book investigates Reliability-based Multidisciplinary Design Optimization (RBMDO) theory and its application in the design of deep manned submersibles (DMSs). Multidisciplinary Design Optimization (MDO) is an effective design method for large engineering systems like aircraft, warships, and satellites, which require designers and engineers from various disciplines to cooperate with each other. MDO can be used to handle the conflicts that arise between these disciplines, and focuses on the optimal design of the system as a whole. However, it can also push designs to the brink of failure. In order to keep the system balanced, Reliability-based Design (RBD) must be incorporated into MDO. Consequently, new algorithms and methods have to be developed for RBMDO theory. This book provides an essential overview of MDO, RBD, and RBMDO and subsequently introduces key algorithms and methods by means of case analyses. In closing, it introduces readers to the design of DMSs and applies RBMDO methods to the design of the manned hull and the general concept design. The book is intended for all students and researchers who are interested in system design theory, and for engineers working on large, complex engineering systems.

This book addresses the principles and applications of metaheuristic approaches in engineering and related fields. The first part covers metaheuristics tools and techniques such as ant colony optimization and Tabu search, and their applications to several classes of optimization problems. In turn, the book's second part focuses on a wide variety of metaheuristics applications in engineering and/or the applied sciences, e.g. in smart grids and renewable energy. In addition, the simulation codes for the problems discussed are included in an appendix for ready reference. Intended for researchers aspiring to learn and apply metaheuristic techniques, and gathering contributions by prominent experts in the field, the book offers readers an essential introduction to metaheuristics, its theoretical aspects and applications.

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.

This English version of Ruslan L. Stratonovich's Theory of Information (1975) builds on theory and provides methods, techniques, and concepts toward utilizing critical applications. Unifying theories of information, optimization, and statistical physics, the value of information theory has gained recognition in data science, machine learning, and artificial intelligence. With the emergence of a data-driven economy, progress in machine learning, artificial intelligence algorithms, and increased computational resources, the need for comprehending information is essential. This book is even more relevant today than when it was first published in 1975. It extends the classic work of R.L. Stratonovich, one of the original developers of the symmetrized version of stochastic calculus and filtering theory, to name just two topics. Each chapter begins with basic, fundamental ideas, supported by clear examples; the material then advances to great detail and depth. The reader is not required to be familiar with the more difficult and specific material. Rather, the treasure trove of examples of stochastic processes and problems makes this book accessible to a wide readership of researchers, postgraduates, and undergraduate students in mathematics, engineering, physics and computer science who are specializing in information theory, data analysis, or machine learning.

The goal of this book is to elaborate on the main principles of the theory of the Berge equilibrium by answering the following two questions: What are the basic properties of the Berge equilibrium? Does the Berge equilibrium exist, and how can it be calculated? The Golden Rule of ethics, which appears in Christianity, Judaism, Islam, Buddhism, Confucianism and other world religions, states the following: "Behave towards others as you would like them to behave towards you." In any game, each party of conflict seeks to maximize some payoff. Therefore, for each player, the Golden Rule is implemented through the maximization of his/her payoff by all other players, which matches well with the concept of the Berge equilibrium. The approach presented here will be of particular interest to researchers (including undergraduates and graduates) and economists focused on decision-making under complex conflict conditions. The peaceful resolution of conflicts is the cornerstone of the approach: as a matter of fact, the Golden Rule precludes military clashes and violence. In turn, the new approach requires new methods; in particular, the existence problems are reduced to saddle point design for the Germeier convolution of payoff functions, with further transition to mixed strategies in accordance with the standard procedure employed by E. Borel, J. von Neumann, J. Nash, and their followers. Moreover, this new approach has proven to be efficient and fruitful with regard to a range of other important problems in mathematical game theory, which are considered in the Appendix.

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.

This book examines the mismatch between discrete programs, which lie at the center of modern applied mathematics, and the continuous space phenomena they simulate. The author considers whether we can imagine continuous spaces of programs, and asks what the structure of such spaces would be and how they would be constituted. He proposes a functional analysis of program spaces focused through the lens of iterative optimization. The author begins with the observation that optimization methods such as Genetic Algorithms, Evolution Strategies, and Particle Swarm Optimization can be analyzed as Estimation of Distributions Algorithms (EDAs) in that they can be formulated as conditional probability distributions. The probabilities themselves are mathematical objects that can be compared and operated on, and thus many methods in Evolutionary Computation can be placed in a shared vector space and analyzed using techniques of functional analysis. The core ideas of this book expand from that concept, eventually incorporating all iterative stochastic search methods, including gradient-based methods. Inspired by work on Randomized Search Heuristics, the author covers all iterative optimization methods and not just evolutionary methods. The No Free Lunch Theorem is viewed as a useful introduction to the broader field of analysis that comes from developing a shared mathematical space for optimization algorithms. The author brings in intuitions from several branches of mathematics such as topology, probability theory, and stochastic processes and provides substantial background material to make the work as self-contained as possible. The book will be valuable for researchers in the areas of global optimization, machine learning, evolutionary theory, and control theory.

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.