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"Logic-Based Methods for Optimization develops for the first time a comprehensive conceptual framework for integrating optimization and constraint satisfaction, then goes a step further and shows how extending logical inference to optimization allows for more powerful as well as flexible modeling and solution techniques. Designed to be easily accessible to industry professionals and academics in both operations research and artificial intelligence, the book provides a wealth of examples as well as elegant techniques and modeling frameworks ready for implementation."--BOOK JACKET.
Merging logic and mathematics in deductive inference-an innovative, cutting-edge approach. Optimization methods for logical inference? Absolutely, say Vijay Chandru and John Hooker, two major contributors to this rapidly expanding field. And even though "solving logical inference problems with optimization methods may seem a bit like eating sauerkraut with chopsticks. . . it is the mathematical structure of a problem that determines whether an optimization model can help solve it, not the context in which the problem occurs." Presenting powerful, proven optimization techniques for logic inference problems, Chandru and Hooker show how optimization models can be used not only to solve problems in artificial intelligence and mathematical programming, but also have tremendous application in complex systems in general. They survey most of the recent research from the past decade in logic/optimization interfaces, incorporate some of their own results, and emphasize the types of logic most receptive to optimization methods-propositional logic, first order predicate logic, probabilistic and related logics, logics that combine evidence such as Dempster-Shafer theory, rule systems with confidence factors, and constraint logic programming systems. Requiring no background in logic and clearly explaining all topics from the ground up, Optimization Methods for Logical Inference is an invaluable guide for scientists and students in diverse fields, including operations research, computer science, artificial intelligence, decision support systems, and engineering.
Fuzzy logic models can be used to demonstrate human decision making in complex situations, and can therefore be an important tool in examining natural complexity. Moreover, fuzzy logic can be exploited to predict chaotic behaviors. But why is fuzzy logic so valuable? The idea of fuzzy logic has been around since 1965, and since its introduction thousands of applications of fuzzy logic have been implemented in industry, medicine, and even economic applications and patents. How did this invaluable theory achieve such great success? This book aims to compare well-known and well-used membership functions to demonstrate how to select the best membership functions and show when and why to utilize them. This book also demonstrates how different fields of studies utilize fuzzy logic showing its wide reach and relevance.
Logic Synthesis and Optimization presents up-to-date research information in a pedagogical form. The authors are recognized as the leading experts on the subject. The focus of the book is on logic minimization and includes such topics as two-level minimization, multi-level minimization, application of binary decision diagrams, delay optimization, asynchronous circuits, spectral method for logic design, field programmable gate array (FPGA) design, EXOR logic synthesis and technology mapping. Examples and illustrations are included so that each contribution can be read independently. Logic Synthesis and Optimization is an indispensable reference for academic researchers as well as professional CAD engineers.
A pioneering look at the fundamental role of logic in optimizationand constraint satisfaction While recent efforts to combine optimization and constraintsatisfaction have received considerable attention, little has beensaid about using logic in optimization as the key to unifying thetwo fields. Logic-Based Methods for Optimization develops for thefirst time a comprehensive conceptual framework for integratingoptimization and constraint satisfaction, then goes a step furtherand shows how extending logical inference to optimization allowsfor more powerful as well as flexible modeling and solutiontechniques. Designed to be easily accessible to industryprofessionals and academics in both operations research andartificial intelligence, the book provides a wealth of examples aswell as elegant techniques and modeling frameworks ready forimplementation. Timely, original, and thought-provoking,Logic-Based Methods for Optimization: * Demonstrates the advantages of combining the techniques inproblem solving * Offers tutorials in constraint satisfaction/constraintprogramming and logical inference * Clearly explains such concepts as relaxation, cutting planes,nonserial dynamic programming, and Bender's decomposition * Reviews the necessary technologies for software developersseeking to combine the two techniques * Features extensive references to important computationalstudies * And much more
Research and development of logic synthesis and verification have matured considerably over the past two decades. Many commercial products are available, and they have been critical in harnessing advances in fabrication technology to produce today's plethora of electronic components. While this maturity is assuring, the advances in fabrication continue to seemingly present unwieldy challenges. Logic Synthesis and Verification provides a state-of-the-art view of logic synthesis and verification. It consists of fifteen chapters, each focusing on a distinct aspect. Each chapter presents key developments, outlines future challenges, and lists essential references. Two unique features of this book are technical strength and comprehensiveness. The book chapters are written by twenty-eight recognized leaders in the field and reviewed by equally qualified experts. The topics collectively span the field. Logic Synthesis and Verification fills a current gap in the existing CAD literature. Each chapter contains essential information to study a topic at a great depth, and to understand further developments in the field. The book is intended for seniors, graduate students, researchers, and developers of related Computer-Aided Design (CAD) tools. From the foreword: "The commercial success of logic synthesis and verification is due in large part to the ideas of many of the authors of this book. Their innovative work contributed to design automation tools that permanently changed the course of electronic design." by Aart J. de Geus, Chairman and CEO, Synopsys, Inc.
This book/software package uniquely integrates logic and operations research. Its broad coverage provides concepts, templates, and the tools for the task of attacking difficult problems which are repeatedly encountered in decision making. The first part deals with linear programming and the second with search techniques for combinatorially hard problems. The applications discussed include product mix problems, pattern recognition, classical and probabilistic logic, financial planning, and expert systems.
This accessible textbook demonstrates how to recognize, simplify, model and solve optimization problems - and apply these principles to new projects.
A comprehensive introduction to optimization with a focus on practical algorithms for the design of engineering systems. This book offers a comprehensive introduction to optimization with a focus on practical algorithms. The book approaches optimization from an engineering perspective, where the objective is to design a system that optimizes a set of metrics subject to constraints. Readers will learn about computational approaches for a range of challenges, including searching high-dimensional spaces, handling problems where there are multiple competing objectives, and accommodating uncertainty in the metrics. Figures, examples, and exercises convey the intuition behind the mathematical approaches. The text provides concrete implementations in the Julia programming language. Topics covered include derivatives and their generalization to multiple dimensions; local descent and first- and second-order methods that inform local descent; stochastic methods, which introduce randomness into the optimization process; linear constrained optimization, when both the objective function and the constraints are linear; surrogate models, probabilistic surrogate models, and using probabilistic surrogate models to guide optimization; optimization under uncertainty; uncertainty propagation; expression optimization; and multidisciplinary design optimization. Appendixes offer an introduction to the Julia language, test functions for evaluating algorithm performance, and mathematical concepts used in the derivation and analysis of the optimization methods discussed in the text. The book can be used by advanced undergraduates and graduate students in mathematics, statistics, computer science, any engineering field, (including electrical engineering and aerospace engineering), and operations research, and as a reference for professionals.