Introduction To Linear Optimization And Extensions With Matlab

Introduction to Linear Optimization and Extensions with MATLAB PDF
Author: Roy H. Kwon
Publisher: CRC Press
ISBN: 1482204347
Size: 59.91 MB
Format: PDF, ePub, Docs
Category : Business & Economics
Languages : en
Pages : 362
View: 5488

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Filling the need for an introductory book on linear programming that discusses the important ways to mitigate parameter uncertainty, Introduction to Linear Optimization and Extensions with MATLAB provides a concrete and intuitive yet rigorous introduction to modern linear optimization. In addition to fundamental topics, the book discusses current l

Introduction To Linear And Convex Programming

Introduction to Linear and Convex Programming PDF
Author: Neil Cameron
Publisher: CUP Archive
ISBN: 9780521312073
Size: 15.22 MB
Format: PDF, ePub, Mobi
Category : Mathematics
Languages : en
Pages : 149
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This introduction to optimization emphasizes the need for both a pure and an applied mathematical point of view. Beginning with a chapter on linear algebra and Euclidean geometry, the author then applies this theory with an introduction to linear programming. There follows a discussion of convex analysis, which finds application in non-linear programming. The book ends with an extensive commentary to the exercises that are given at the end of each chapter. The author's straightforward, geometrical approach makes this an attractive textbook for undergraduate students of mathematics, engineering, operations research and economics.

Introduction To Linear Programming With Matlab

Introduction to Linear Programming with MATLAB PDF
Author: Shashi Kant Mishra
Publisher: CRC Press
ISBN: 1351596799
Size: 54.45 MB
Format: PDF, ePub, Docs
Category : Mathematics
Languages : en
Pages : 313
View: 5507

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This book is based on the lecture notes of the author delivered to the students at the Institute of Science, Banaras Hindu University, India. It covers simplex, revised simplex, two-phase method, duality, dual simplex, complementary slackness, transportation and assignment problems with good number of examples, clear proofs, MATLAB codes and homework problems. The book will be useful for both students and practitioners.

An Introduction To Optimization

An Introduction to Optimization PDF
Author: Edwin K. P. Chong
Publisher: John Wiley & Sons
ISBN: 1118279018
Size: 49.16 MB
Format: PDF, Docs
Category : Mathematics
Languages : en
Pages : 640
View: 3159

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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

Introduction to Optimization PDF
Author: Pablo Pedregal
Publisher: Springer Science & Business Media
ISBN: 0387216804
Size: 12.80 MB
Format: PDF, Mobi
Category : Mathematics
Languages : en
Pages : 246
View: 5425

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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.

Introduction To Mathematical Optimization

Introduction to Mathematical Optimization PDF
Author: Xin-She Yang
Publisher: Cambridge International Science Pub
Size: 49.77 MB
Format: PDF, Docs
Category : Mathematics
Languages : en
Pages : 150
View: 5332

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This book strives to provide a balanced coverage of efficient algorithms commonly used in solving mathematical optimization problems. It covers both the convectional algorithms and modern heuristic and metaheuristic methods. Topics include gradient-based algorithms such as Newton-Raphson method, steepest descent method, Hooke-Jeeves pattern search, Lagrange multipliers, linear programming, particle swarm optimization (PSO), simulated annealing (SA), and Tabu search. Multiobjective optimization including important concepts such as Pareto optimality and utility method is also described. Three Matlab and Octave programs so as to demonstrate how PSO and SA work are provided. An example of demonstrating how to modify these programs to solve multiobjective optimization problems using recursive method is discussed.