Introduction To Optimization Methods

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Introduction to Optimization Methods

Author : P. Adby
Publisher : Springer Science & Business Media
Page : 214 pages
File Size : 42,5 Mb
Release : 2013-03-09
Category : Science
ISBN : 9789400957053

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Introduction to Optimization Methods by P. Adby Pdf

During the last decade the techniques of non-linear optim ization have emerged as an important subject for study and research. The increasingly widespread application of optim ization has been stimulated by the availability of digital computers, and the necessity of using them in the investigation of large systems. This book is an introduction to non-linear methods of optimization and is suitable for undergraduate and post graduate courses in mathematics, the physical and social sciences, and engineering. The first half of the book covers the basic optimization techniques including linear search methods, steepest descent, least squares, and the Newton-Raphson method. These are described in detail, with worked numerical examples, since they form the basis from which advanced methods are derived. Since 1965 advanced methods of unconstrained and constrained optimization have been developed to utilise the computational power of the digital computer. The second half of the book describes fully important algorithms in current use such as variable metric methods for unconstrained problems and penalty function methods for constrained problems. Recent work, much of which has not yet been widely applied, is reviewed and compared with currently popular techniques under a few generic main headings. vi PREFACE Chapter I describes the optimization problem in mathemat ical form and defines the terminology used in the remainder of the book. Chapter 2 is concerned with single variable optimization. The main algorithms of both search and approximation methods are developed in detail since they are an essential part of many multi-variable methods.

Introduction to Optimization Methods and their Application in Statistics

Author : B. Everitt
Publisher : Springer Science & Business Media
Page : 88 pages
File Size : 46,9 Mb
Release : 2012-12-06
Category : Science
ISBN : 9789400931534

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Introduction to Optimization Methods and their Application in Statistics by B. Everitt Pdf

Optimization techniques are used to find the values of a set of parameters which maximize or minimize some objective function of interest. Such methods have become of great importance in statistics for estimation, model fitting, etc. This text attempts to give a brief introduction to optimization methods and their use in several important areas of statistics. It does not pretend to provide either a complete treatment of optimization techniques or a comprehensive review of their application in statistics; such a review would, of course, require a volume several orders of magnitude larger than this since almost every issue of every statistics journal contains one or other paper which involves the application of an optimization method. It is hoped that the text will be useful to students on applied statistics courses and to researchers needing to use optimization techniques in a statistical context. Lastly, my thanks are due to Bertha Lakey for typing the manuscript.

An Introduction to Optimization Techniques

Author : Vikrant Sharma,Vinod Kumar Jain,Atul Kumar
Publisher : CRC Press
Page : 432 pages
File Size : 54,5 Mb
Release : 2021-04-19
Category : Mathematics
ISBN : 9781000338232

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An Introduction to Optimization Techniques by Vikrant Sharma,Vinod Kumar Jain,Atul Kumar Pdf

An Introduction to Optimization Techniques introduces the basic ideas and techniques of optimization. Optimization is a precise procedure using design constraints and criteria to enable the planner to find the optimal solution. Optimization techniques have been applied in numerous fields to deal with different practical problems. This book is designed to give the reader a sense of the challenge of analyzing a given situation and formulating a model for it while explaining the assumptions and inner structure of the methods discussed as fully as possible. It includes real-world examples and applications making the book accessible to a broader readership. Features Each chapter begins with the Learning Outcomes (LO) section, which highlights the critical points of that chapter. All learning outcomes, solved examples and questions are mapped to six Bloom Taxonomy levels (BT Level). Book offers fundamental concepts of optimization without becoming too complicated. A wide range of solved examples are presented in each section after the theoretical discussion to clarify the concept of that section. A separate chapter on the application of spreadsheets to solve different optimization techniques. At the end of each chapter, a summary reinforces key ideas and helps readers recall the concepts discussed. The wide and emerging uses of optimization techniques make it essential for students and professionals. Optimization techniques have been applied in numerous fields to deal with different practical problems. This book serves as a textbook for UG and PG students of science, engineering, and management programs. It will be equally useful for Professionals, Consultants, and Managers.

An Introduction to Optimization

Author : Edwin K. P. Chong,Stanislaw H. Żak
Publisher : John Wiley & Sons
Page : 497 pages
File Size : 43,6 Mb
Release : 2004-04-05
Category : Mathematics
ISBN : 9780471654001

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An Introduction to Optimization by Edwin K. P. Chong,Stanislaw H. Żak Pdf

A modern, up-to-date introduction to optimization theory and methods This authoritative book serves as an introductory text to optimization at the senior undergraduate and beginning graduate levels. With consistently accessible and elementary treatment of all topics, An Introduction to Optimization, Second Edition helps students build a solid working knowledge of the field, including unconstrained optimization, linear programming, and constrained optimization. Supplemented with more than one hundred tables and illustrations, an extensive bibliography, and numerous worked examples to illustrate both theory and algorithms, this book also provides: * A review of the required mathematical background material * A mathematical discussion at a level accessible to MBA and business students * A treatment of both linear and nonlinear programming * An introduction to recent developments, including neural networks, genetic algorithms, and interior-point methods * A chapter on the use of descent algorithms for the training of feedforward neural networks * Exercise problems after every chapter, many new to this edition * MATLAB(r) exercises and examples * Accompanying Instructor's Solutions Manual available on request An Introduction to Optimization, Second Edition helps students prepare for the advanced topics and technological developments that lie ahead. It is also a useful book for researchers and professionals in mathematics, electrical engineering, economics, statistics, and business. An Instructor's Manual presenting detailed solutions to all the problems in the book is available from the Wiley editorial department.

Fundamentals of Optimization Techniques with Algorithms

Author : Sukanta Nayak
Publisher : Academic Press
Page : 323 pages
File Size : 45,9 Mb
Release : 2020-08-25
Category : Technology & Engineering
ISBN : 9780128224922

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Fundamentals of Optimization Techniques with Algorithms by Sukanta Nayak Pdf

Optimization is a key concept in mathematics, computer science, and operations research, and is essential to the modeling of any system, playing an integral role in computer-aided design. Fundamentals of Optimization Techniques with Algorithms presents a complete package of various traditional and advanced optimization techniques along with a variety of example problems, algorithms and MATLAB© code optimization techniques, for linear and nonlinear single variable and multivariable models, as well as multi-objective and advanced optimization techniques. It presents both theoretical and numerical perspectives in a clear and approachable way. In order to help the reader apply optimization techniques in practice, the book details program codes and computer-aided designs in relation to real-world problems. Ten chapters cover, an introduction to optimization; linear programming; single variable nonlinear optimization; multivariable unconstrained nonlinear optimization; multivariable constrained nonlinear optimization; geometric programming; dynamic programming; integer programming; multi-objective optimization; and nature-inspired optimization. This book provides accessible coverage of optimization techniques, and helps the reader to apply them in practice. Presents optimization techniques clearly, including worked-out examples, from traditional to advanced Maps out the relations between optimization and other mathematical topics and disciplines Provides systematic coverage of algorithms to facilitate computer coding Gives MATLAB© codes in relation to optimization techniques and their use in computer-aided design Presents nature-inspired optimization techniques including genetic algorithms and artificial neural networks

A Gentle Introduction to Optimization

Author : B. Guenin,J. Könemann,L. Tunçel
Publisher : Cambridge University Press
Page : 283 pages
File Size : 53,6 Mb
Release : 2014-07-31
Category : Business & Economics
ISBN : 9781107053441

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A Gentle Introduction to Optimization by B. Guenin,J. Könemann,L. Tunçel Pdf

Assuming only basic linear algebra, this textbook is the perfect starting point for undergraduate students from across the mathematical sciences.

Algorithms for Optimization

Author : Mykel J. Kochenderfer,Tim A. Wheeler
Publisher : MIT Press
Page : 521 pages
File Size : 40,6 Mb
Release : 2019-03-12
Category : Computers
ISBN : 9780262039420

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Algorithms for Optimization by Mykel J. Kochenderfer,Tim A. Wheeler Pdf

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.

An Introduction to Optimization

Author : Edwin K. P. Chong,Stanislaw H. Żak
Publisher : John Wiley & Sons
Page : 646 pages
File Size : 45,8 Mb
Release : 2013-02-05
Category : Mathematics
ISBN : 9781118515150

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An Introduction to Optimization by Edwin K. P. Chong,Stanislaw H. Żak Pdf

Praise for the Third Edition ". . . guides and leads the reader through the learning path . . . [e]xamples are stated very clearly and the results are presented with attention to detail." —MAA Reviews Fully updated to reflect new developments in the field, the Fourth Edition of Introduction to Optimization fills the need for accessible treatment of optimization theory and methods with an emphasis on engineering design. 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.

Introduction to optimization methods

Author : Paul R. Adby,Michael A. H. Dempster
Publisher : Unknown
Page : 204 pages
File Size : 47,7 Mb
Release : 1978
Category : Electronic
ISBN : OCLC:630989652

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Introduction to optimization methods by Paul R. Adby,Michael A. H. Dempster Pdf

Biologically Inspired Optimization Methods

Author : Mattias Wahde
Publisher : WIT Press
Page : 241 pages
File Size : 53,5 Mb
Release : 2008-08-14
Category : Mathematics
ISBN : 9781845641481

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Biologically Inspired Optimization Methods by Mattias Wahde Pdf

Biologically inspired optimization methods constitute a rapidly expanding field of research, with new applications appearing on an almost daily basis, as optimization problems of ever-increasing complexity appear in science and technology. This book provides a general introduction to such optimization methods, along with descriptions of the biological systems upon which the methods are based. The book also covers classical optimization methods, making it possible for the reader to determine whether a classical optimization method or a biologically inspired one is most suitable for a given problem.

Introduction to Optimization Methods

Author : P Adby
Publisher : Springer
Page : 216 pages
File Size : 43,6 Mb
Release : 2014-01-15
Category : Electronic
ISBN : 9400957068

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Introduction to Optimization Methods by P Adby Pdf

Numerical Methods and Optimization

Author : Sergiy Butenko,Panos M. Pardalos
Publisher : CRC Press
Page : 408 pages
File Size : 43,6 Mb
Release : 2014-03-11
Category : Business & Economics
ISBN : 9781466577787

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Numerical Methods and Optimization by Sergiy Butenko,Panos M. Pardalos Pdf

For students in industrial and systems engineering (ISE) and operations research (OR) to understand optimization at an advanced level, they must first grasp the analysis of algorithms, computational complexity, and other concepts and modern developments in numerical methods. Satisfying this prerequisite, Numerical Methods and Optimization: An Intro

Optimization Models

Author : Giuseppe C. Calafiore,Laurent El Ghaoui
Publisher : Cambridge University Press
Page : 651 pages
File Size : 46,9 Mb
Release : 2014-10-31
Category : Business & Economics
ISBN : 9781107050877

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Optimization Models by Giuseppe C. Calafiore,Laurent El Ghaoui Pdf

This accessible textbook demonstrates how to recognize, simplify, model and solve optimization problems - and apply these principles to new projects.

Introduction to Applied Optimization

Author : Urmila Diwekar
Publisher : Springer Science & Business Media
Page : 342 pages
File Size : 50,8 Mb
Release : 2013-03-09
Category : Mathematics
ISBN : 9781475737455

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Introduction to Applied Optimization by Urmila Diwekar Pdf

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.

First-Order Methods in Optimization

Author : Amir Beck
Publisher : SIAM
Page : 476 pages
File Size : 45,5 Mb
Release : 2017-10-02
Category : Mathematics
ISBN : 9781611974980

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First-Order Methods in Optimization by Amir Beck Pdf

The primary goal of this book is to provide a self-contained, comprehensive study of the main ?rst-order methods that are frequently used in solving large-scale problems. First-order methods exploit information on values and gradients/subgradients (but not Hessians) of the functions composing the model under consideration. With the increase in the number of applications that can be modeled as large or even huge-scale optimization problems, there has been a revived interest in using simple methods that require low iteration cost as well as low memory storage. The author has gathered, reorganized, and synthesized (in a unified manner) many results that are currently scattered throughout the literature, many of which cannot be typically found in optimization books. First-Order Methods in Optimization offers comprehensive study of first-order methods with the theoretical foundations; provides plentiful examples and illustrations; emphasizes rates of convergence and complexity analysis of the main first-order methods used to solve large-scale problems; and covers both variables and functional decomposition methods.