This book, suitable for both course work and self-study, brings together for the first time, in an informal, tutorial fashion, the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields: programmers, scientists, engineers, mathematicians, statisticians and management scientists will all find interesting possibilities here. Major concepts are illustrated with running examples, and major algorithms are illustrated by Pascal computer programs. Chapter concludes with exercises and computer assignments. No prior knowledge of Gas or genetics is assumed.

This book combines the analytic principles of digital business and data science with business practice and big data. The interdisciplinary, contributed volume provides an interface between the main disciplines of engineering and technology and business administration. Written for managers, engineers and researchers who want to understand big data and develop new skills that are necessary in the digital business, it not only discusses the latest research, but also presents case studies demonstrating the successful application of data in the digital business.

This two volume classic comprises two titles: "Patterns of Plausible Inference" and "Induction and Analogy in Mathematics". This is a guide to the practical art of plausible reasoning, particularly in mathematics, but also in every field of human activity. Using mathematics as the example par excellence, Polya shows how even the most rigorous deductive discipline is heavily dependent on techniques of guessing, inductive reasoning, and reasoning by analogy. In solving a problem, the answer must be guessed at before a proof can be given, and guesses are usually made from a knowledge of facts, experience, and hunches. The truly creative mathematician must be a good guesser first and a good prover afterward; many important theorems have been guessed but no proved until much later. In the same way, solutions to problems can be guessed, and a god guesser is much more likely to find a correct solution. This work might have been called "How to Become a Good Guesser."-From the Dust Jacket.

This book focuses on the analytic principles of business practice and big data. Specifically, it provides an interface between the main disciplines of engineering/technology and the organizational and administrative aspects of management, serving as a complement to books in other disciplines such as economics, finance, marketing and risk analysis. The contributors present their areas of expertise, together with essential case studies that illustrate the successful application of engineering management theories in real-life examples.

The Handbook of Mathematical Economics aims to provide a definitive source, reference, and teaching supplement for the field of mathematical economics. It surveys, as of the late 1970's the state of the art of mathematical economics. This is a constantly developing field and all authors were invited to review and to appraise the current status and recent developments in their presentations. In addition to its use as a reference, it is intended that this Handbook will assist researchers and students working in one branch of mathematical economics to become acquainted with other branches of this field. Volume 2 elaborates on Mathematical Approaches to Microeconomic Theory , including consumer, producer, oligopoly, and duality theory, as well as Mathematical Approaches to Competitive Equilibrium including such aspects of competitive equilibrium as existence, stability, uncertainty, the computation of equilibrium prices, and the core of an economy. For more information on the Handbooks in Economics series, please see our home page on http://www.elsevier.nl/locate/hes

This manual follows the sequence of topics in the text, and contains clear, step-by-step instructions on how to use Microsoft(R) Excel(R) and popular Texas Instruments graphing calculators.

This book provides relevant theoretical frameworks and the latest empirical research findings of Operations Research/Management Science applied to Internet of Things. This book identifies and describes ways in which OR and MS have been applied and influenced the development of IoT. Examples are from smart industry; city; transportation; home and smart devices. It discusses future applications, trends, and potential benefits of this new discipline. It is written for professionals who want to improve their understanding of the strategic role of IoT at various levels of the organization, that is, IoT at the global economy level, at networks and organizations level, at teams and work groups, at information systems and, finally, IoT at the level of individuals, as players in the networked environments.

In these essays Heinz von Foerster discusses some of the fundamental principles that govern how we know the world and how we process the information from which we derive that knowledge. Included are path- breaking articles concerning the principles of computation in neural nets (1967), the definition of self-organizing systems (1960), the nature of cognition (1970), as well as recent expansions on these themes (e.g. "How recursive is communication," 1993). Working with Norbert Wiener, Warren McCullough, and others in the 1960s and 1970s, von Foerster was one of the founders of the science of cybernetics, which has had profound effects both on modern systems theory and on the philosophy of cognition. At the Biological Computer Laboratory at the University of Illinois he produced the first parallel computers and contributed to many other developments in the theory of computation and cognition.

Quantum computers will break today's most popular public-key cryptographic systems, including RSA, DSA, and ECDSA. This book introduces the reader to the next generation of cryptographic algorithms, the systems that resist quantum-computer attacks: in particular, post-quantum public-key encryption systems and post-quantum public-key signature systems. Leading experts have joined forces for the first time to explain the state of the art in quantum computing, hash-based cryptography, code-based cryptography, lattice-based cryptography, and multivariate cryptography. Mathematical foundations and implementation issues are included. This book is an essential resource for students and researchers who want to contribute to the field of post-quantum cryptography.

Preface Introduction Chapter 1 Meaning and Mentalism Fodor and Chomsky Three Reasons Why Mentalism Can't Be Right 1 Meaning Is Holistic 2 Meaning Is in Part a Normative Notion 3 Our Concepts Depend on Our Physical and Social Environment in a Way That Evolution (Which Was Com ... Connections between 1, 2, and 3 Chapter 2 Meaning, Other People, and the World The Division of Linguistic Labor Elms, Beeches, and Searle The Contribution of the Environment An Indexical Component Other Natural Kinds Reference and Theory Change Meaning and "Mental Representation" Chapter 3 Fodor and Block on "Narrow Content" Narrow Content as a "Function of Observable Properties" "Narrow Content" and "Conceptual Role" Concluding Remarks Chapter 4 Are There Such Things as Reference and Truth? Why "Folk Psychology" and Not "Folk Logic"? Disquotation, Anyone? The "Semantical Conception" of Truth Disquotation as Disappearance Chapter 5 Why Functionalism Didn't Work Sociofunctionalism What "In Principle" Means Here The Single-Computational-State Version of Functionalism Equivalence Surveying Rationality Chapter 6 Other Forms of Functionalism David Lewis and I Lewis's Theory Further Examined Conclusion Chapter 7 A Sketch of an Alternative Picture Objectivity and Conceptual Relativity Internal Realism as an Alternative Picture My Present Diagnosis of the "Functionalism" Issue Appendix Notes Author Index

Scientific Computing and Differential Equations: An Introduction to Numerical Methods, is an excellent complement to Introduction to Numerical Methods by Ortega and Poole. The book emphasizes the importance of solving differential equations on a computer, which comprises a large part of what has come to be called scientific computing. It reviews modern scientific computing, outlines its applications, and places the subject in a larger context. This book is appropriate for upper undergraduate courses in mathematics, electrical engineering, and computer science; it is also well-suited to serve as a textbook for numerical differential equations courses at the graduate level. * An introductory chapter gives an overview of scientific computing, indicating its important role in solving differential equations, and placing the subject in the larger environment * Contains an introduction to numerical methods for both ordinary and partial differential equations * Concentrates on ordinary differential equations, especially boundary-value problems * Contains most of the main topics for a first course in numerical methods, and can serve as a text for this course * Uses material for junior/senior level undergraduate courses in math and computer science plus material for numerical differential equations courses for engineering/science students at the graduate level

Computers And Thought by Edward A. Feigenbaum is a comprehensive book that explores the relationship between computers and human thought. The book delves into the history of computer science and artificial intelligence, and how these fields have impacted our understanding of human cognition. It also examines the ways in which computers have been used to model human thought processes, and how these models have been applied to various fields such as psychology, linguistics, and philosophy.Feigenbaum discusses the different approaches to artificial intelligence, including rule-based systems, expert systems, and neural networks. He also explores the limitations of these approaches and the challenges of creating truly intelligent machines. The book includes case studies of successful applications of artificial intelligence, such as the development of chess-playing programs and natural language processing systems.The author also addresses ethical and philosophical questions related to artificial intelligence, such as the possibility of machines surpassing human intelligence and the implications of this for society. He argues that while computers can simulate human thought processes, they cannot replicate the full range of human experience and consciousness.Overall, Computers And Thought is a thought-provoking and informative book that provides a comprehensive overview of the relationship between computers and human thought. It is a valuable resource for anyone interested in the history and future of artificial intelligence and its impact on society.This scarce antiquarian book is a facsimile reprint of the old original and may contain some imperfections such as library marks and notations. Because we believe this work is culturally important, we have made it available as part of our commitment for protecting, preserving, and promoting the world's literature in affordable, high quality, modern editions, that are true to their original work.

Abstract In this chapter we provide an overview of probabilistic logic networks (PLN), including our motivations for developing PLN and the guiding principles underlying PLN. We discuss foundational choices we made, introduce PLN knowledge representation, and briefly introduce inference rules and truth-values. We also place PLN in context with other approaches to uncertain inference. 1.1 Motivations This book presents Probabilistic Logic Networks (PLN), a systematic and pragmatic framework for computationally carrying out uncertain reasoning – r- soning about uncertain data, and/or reasoning involving uncertain conclusions. We begin with a few comments about why we believe this is such an interesting and important domain of investigation. First of all, we hold to a philosophical perspective in which “reasoning” – properly understood – plays a central role in cognitive activity. We realize that other perspectives exist; in particular, logical reasoning is sometimes construed as a special kind of cognition that humans carry out only occasionally, as a deviation from their usual (intuitive, emotional, pragmatic, sensorimotor, etc.) modes of thought. However, we consider this alternative view to be valid only according to a very limited definition of “logic.” Construed properly, we suggest, logical reasoning may be understood as the basic framework underlying all forms of cognition, including those conventionally thought of as illogical and irrational.

In 1942, Lt. Herman H. Goldstine, a former mathematics professor, was stationed at the Moore School of Electrical Engineering at the University of Pennsylvania. It was there that he assisted in the creation of the ENIAC, the first electronic digital computer. The ENIAC was operational in 1945, but plans for a new computer were already underway. The principal source of ideas for the new computer was John von Neumann, who became Goldstine's chief collaborator. Together they developed EDVAC, successor to ENIAC. After World War II, at the Institute for Advanced Study, they built what was to become the prototype of the present-day computer. Herman Goldstine writes as both historian and scientist in this first examination of the development of computing machinery, from the seventeenth century through the early 1950s. His personal involvement lends a special authenticity to his narrative, as he sprinkles anecdotes and stories liberally through his text.

The starting point of this volume was a conference entitled "Progress in Mathematical Programming," held at the Asilomar Conference Center in Pacific Grove, California, March 1-4, 1987. The main topic of the conference was developments in the theory and practice of linear programming since Karmarkar's algorithm. There were thirty presentations and approximately fifty people attended. Presentations included new algorithms, new analyses of algorithms, reports on computational experience, and some other topics related to the practice of mathematical programming. Interestingly, most of the progress reported at the conference was on the theoretical side. Several new polynomial algorithms for linear program- ming were presented (Barnes-Chopra-Jensen, Goldfarb-Mehrotra, Gonzaga, Kojima-Mizuno-Yoshise, Renegar, Todd, Vaidya, and Ye). Other algorithms presented were by Betke-Gritzmann, Blum, Gill-Murray-Saunders-Wright, Nazareth, Vial, and Zikan-Cottle. Efforts in the theoretical analysis of algo- rithms were also reported (Anstreicher, Bayer-Lagarias, Imai, Lagarias, Megiddo-Shub, Lagarias, Smale, and Vanderbei). Computational experiences were reported by Lustig, Tomlin, Todd, Tone, Ye, and Zikan-Cottle. Of special interest, although not in the main direction discussed at the conference, was the report by Rinaldi on the practical solution of some large traveling salesman problems. At the time of the conference, it was still not clear whether the new algorithms developed since Karmarkar's algorithm would replace the simplex method in practice. Alan Hoffman presented results on conditions under which linear programming problems can be solved by greedy algorithms.

In this monograph, the authors propose a systematic and rigorous treatment of electromagnetic field representations in complex structures. The architecture suggested in this book accommodates use of different numerical methods as well as alternative Green's function representations in each of the subdomains resulting from a partitioning of the overall problem. The subdomains are regions of space where electromagnetic energy is stored and are described in terms of equivalent circuit representations based either on lumped element circuits or on transmission lines. Connection networks connect the subcircuits representing the subdomains. The connection networks are lossless, don't store energy and represent the overall problem topology. This is similar to what is done in circuit theory and permits a phrasing of the solution of EM field problems in complex structures by Network-oriented methods.

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This book is about computational methods based on operator splitting. It consists of twenty-three chapters written by recognized splitting method contributors and practitioners, and covers a vast spectrum of topics and application areas, including computational mechanics, computational physics, image processing, wireless communication, nonlinear optics, and finance. Therefore, the book presents very versatile aspects of splitting methods and their applications, motivating the cross-fertilization of ideas.