Download Read. Praise for the Third Edition ". Basic definitions and notations are provided in addition to the related fundamental background for linear algebra, geometry, and calculus.
This new edition explores the essential topics of unconstrained optimization problems, linear programming problems, and nonlinear constrained optimization. The authors also present an optimization perspective on global search methods and include discussions on genetic algorithms, particle swarm optimization, and the simulated annealing algorithm. Featuring an elementary introduction to artificial neural networks, convex optimization, and multi-objective optimization, the Fourth Edition also offers: A new chapter on integer programming Expanded coverage of one-dimensional methods Updated and expanded sections on linear matrix inequalities Numerous new exercises at the end of each chapter MATLAB exercises and drill problems to reinforce the discussed theory and algorithms Numerous diagrams and figures that complement the written presentation of key concepts MATLAB M-files for implementation of the discussed theory and algorithms available via the book's website Introduction to Optimization, Fourth Edition is an ideal textbook for courses on optimization theory and methods.
In addition, the book is a useful reference for professionals in mathematics, operations research, electrical engineering, economics, statistics, and business. A modern, up-to-date introduction to optimization theory andmethods This authoritative book serves as an introductory text tooptimization at the senior undergraduate and beginning graduatelevels.
With consistently accessible and elementary treatment ofall topics, An Introduction to Optimization, Second Edition helpsstudents build a solid working knowledge of the field, includingunconstrained optimization, linear programming, and constrainedoptimization.
It is also a useful book for researchers andprofessionals in mathematics, electrical engineering, economics,statistics, and business. An Instructor's Manual presenting detailed solutions to all theproblems in the book is available from the Wiley editorialdepartment. The authors stress the relative simplicity, efficiency, flexibility of use, and suitability of various approaches used to solve difficult optimization problems. The authors are experienced, interdisciplinary lecturers and researchers and in their explanations they demonstrate many shared foundational concepts among the key methodologies.
This textbook is a suitable introduction for undergraduate and graduate students, researchers, and professionals in computer science, engineering, and logistics. This undergraduate textbook introduces students of science and engineering to the fascinating field of optimization.
It is a unique book that brings together the subfields of mathematical programming, variational calculus, and optimal control, thus giving students an overall view of all aspects of optimization in a single reference.
As a primer on optimization, its main goal is to provide a succinct and accessible introduction to linear programming, nonlinear programming, numerical optimization algorithms, variational problems, dynamic programming, and optimal control. Prerequisites have been kept to a minimum, although a basic knowledge of calculus, linear algebra, and differential equations is assumed.
A complete introduction to partial differential equations, this textbook provides a rigorous yet accessible guide to students in mathematics, physics and engineering. The presentation is lively and up to date, paying particular emphasis to developing an appreciation of underlying mathematical theory.
Beginning with basic definitions, properties and derivations of some basic equations of mathematical physics from basic principles, the book studies first order equations, classification of second order equations, and the one-dimensional wave equation.
Two chapters are devoted to the separation of variables, whilst others concentrate on a wide range of topics including elliptic theory, Green's functions, variational and numerical methods. A rich collection of worked examples and exercises accompany the text, along with a large number of illustrations and graphs to provide insight into the numerical examples.
This book presents the basics of linear and nonlinear optimization analysis for both single and multi-objective problems in hydrosystem engineering.
The book includes several examples with various levels of complexity in different fields of water resources engineering. The examples are solved step by step to assist the reader and to make it easier to understand the concepts. In addition, the latest tools and methods are presented to help students, researchers, engineers and water managers to properly conceptualize and formulate resource allocation problems, and to deal with the complexity of constraints in water demand and available supplies in an appropriate way.
Assuming only basic linear algebra, this textbook is the perfect starting point for undergraduate students from across the mathematical sciences. This brief provides a detailed introduction, discussion and bibliographic review of the nature1-inspired optimization algorithm called Harmony Search. It uses a large number of simulation results to demonstrate the advantages of Harmony Search and its variants and also their drawbacks. The authors show how weaknesses can be amended by hybridization with other optimization methods.
The Harmony Search Method with Applications will be of value to researchers in computational intelligence in demonstrating the state of the art of research on an algorithm of current interest.
It also helps researchers and practitioners of electrical and computer engineering more generally in acquainting themselves with this method of vector-based optimization. Multiobjective optimization deals with solving problems having not only one, but multiple, often conflicting, criteria. Based on the International Seminar on Practical Approaches to Multiobjective Optimization, held in Dagstuhl Castle, Germany, in December , this book gives an account of the status of research and applications in this field.
This book offers a basic introduction to genetic algorithms. It provides a detailed explanation of genetic algorithm concepts and examines numerous genetic algorithm optimization problems. It also includes application case studies on genetic algorithms in emerging fields.
Practical optimization problems are often hard to solve, in particular when they are black boxes and no further information about the problem is available except via function evaluations. This work introduces a collection of heuristics and algorithms for black box optimization with evolutionary algorithms in continuous solution spaces.
The book gives an introduction to evolution strategies and parameter control. Heuristic extensions are presented that allow optimization in constrained, multimodal and multi-objective solution spaces. An adaptive penalty function is introduced for constrained optimization. Meta-models reduce the number of fitness and constraint function calls in expensive optimization problems. The hybridization of evolution strategies with local search allows fast optimization in solution spaces with many local optima.
A selection operator based on reference lines in objective space is introduced to optimize multiple conflictive objectives. Evolutionary search is employed for learning kernel parameters of the Nadaraya-Watson estimator and a swarm-based iterative approach is presented for optimizing latent points in dimensionality reduction problems. Experiments on typical benchmark problems as well as numerous figures and diagrams illustrate the behavior of the introduced concepts and methods.
An extensively revised edition of a mathematically rigorous yet accessible introduction to algorithms. It gives an introduction to testing, describes the problems related to SOC testing, discusses the modeling granularity and the implementation into EDA electronic design automation tools.
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The first part covers an introduction into test problems including faults, fault types, design-flow, design-for-test techniques such as scan-testing and Boundary Scan.
The second part of the book discusses SOC related problems such as system modeling, test conflicts, power consumption, test access mechanism design, test scheduling and defect-oriented scheduling. Finally, the third part focuses on SOC applications, such as integrated test scheduling and TAM design, defect-oriented scheduling, and integrating test design with the core selection process. The book begins with an easy-to-read introduction to the concepts associated with the creation of optimization models for production planning.
From this foundation, fairly sophisticated models for supply chain management are developed.
Another unique feature is that models are developed with an eye toward implementation. In fact, there is a chapter that provides explicit examples of implementation of the basic models using a variety of popular, commercially available modeling languages.
It gives an introductory treatment of problems and methods of structural optimization. The three basic classes of geometrical - timization problems of mechanical structures, i. The focus is on concrete numerical solution methods for d- crete and? The style is explicit and practical: mathematical proofs are provided when arguments can be kept e- mentary but are otherwise only cited, while implementation details are frequently provided.
Moreover, since the text has an emphasis on geometrical design problems, where the design is represented by continuously varying—frequently very many— variables, so-called? These methods are based on sensitivity analysis, i. The classical?
It should be remarked that the classical and frequently used so-called op- mality criteria method is also of this kind. It may also be noted in this context that zero order methods such as response surface methods, surrogate models, neural n- works, genetic algorithms, etc. The efficiency and reliability of manufactured products depend on, among other things, geometrical aspects; it is therefore not surprising that optimal shape design problems have attracted the interest of applied mathematicians and engineers.
This self-contained, elementary introduction to the mathematical and computational aspects of sizing and shape optimization enables readers to gain a firm understanding of the theoretical and practical aspects so they may confidently enter this field.
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Introduction to Shape Optimization: Theory, Approximation, and Computation treats sizing and shape optimization comprehensively, covering everything from mathematical theory existence analysis, discretizations, and convergence analysis for discretized problems through computational aspects sensitivity analysis, numerical minimization methods to industrial applications.
Applications include contact stress minimization for elasto-plastic bodies, multidisciplinary optimization of an airfoil, and shape optimization of a dividing tube. By presenting sizing and shape optimization in an abstract way, the authors are able to use a unified approach in the mathematical analysis for a large class of optimization problems in various fields of physics.
Audience: the book is written primarily for students of applied mathematics, scientific computing, and mechanics. Most of the material is directed toward graduate students, although a portion of it is suitable for senior undergraduate students. This text presents a multi-disciplined view of optimization, providing students and researchers with a thorough examination of algorithms, methods, and tools from diverse areas of optimization without introducing excessive theoretical detail.
This second edition includes additional topics, including global optimization and a real-world case study using important concepts from each chapter.
Introduction to Applied Optimization is intended for advanced undergraduate and graduate students and will benefit scientists from diverse areas, including engineers. In order to facilitate understanding, the presentation of the methods is supplemented by simple flow charts, followed by pseudocode implementations that reveal deeper insights into their mode of operation.
These discussions are further supported by examples taken from important applications in computer vision.
Topics and features: provides a comprehensive overview of computer vision-related optimization; covers a range of techniques from classical iterative multidimensional optimization to cutting-edge topics of graph cuts and GPU-suited total variation-based optimization; describes in detail the optimization methods employed in computer vision applications; illuminates key concepts with clearly written and step-by-step explanations; presents detailed information on implementation, including pseudocode for most methods.
This book serves as an introductory text to optimization theory in normed spaces and covers all areas of nonlinear optimization. It presents fundamentals with particular emphasis on the application to problems in the calculus of variations, approximation and optimal control theory.
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Home Search results for: an introduction to optimization solutions. Chong,Stanislaw H. Zak Summary Praise for the Third Edition ". Zak Summary A modern, up-to-date introduction to optimization theory andmethods This authoritative book serves as an introductory text tooptimization at the senior undergraduate and beginning graduatelevels. Download Read An Introduction to Metaheuristics for Optimization by Bastien Chopard,Marco Tomassini Summary The authors stress the relative simplicity, efficiency, flexibility of use, and suitability of various approaches used to solve difficult optimization problems.