An Introduction to Optimization, 4 edition  PDF & ePUB Download
EPUB
 eBook:An Introduction to Optimization, 4 edition
 Author:Edwin K. P. Chong, Stanislaw H. Zak
 Edition:4 edition
 Categories:
 Data:20130114
 ISBN:1118279018
 ISBN13:9781118279014
 Language:English
 Pages:640
 Format:EPUB
Description of An Introduction to Optimization, 4 edition ebook
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 multiobjective optimization, the Fourth Edition also offers:
 A new chapter on integer programming
 Expanded coverage of onedimensional 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 Mfiles for implementation of the discussed theory and algorithms (available via the book's website)

Content
Chapter 1: Methods of Proof and Some Notation
Chapter 2: Vector Spaces and Matrices
Chapter 3: Transformations
Chapter 4: Concepts from Geometry
Chapter 5: Elements of Calculus
Part II: Unconstrained Optimization
Chapter 6: Basics of SetConstrained and Unconstrained Optimization
Chapter 7: OneDimensional Search Methods
Chapter 8: Gradient Methods
Chapter 9: Newton’s Method
Chapter 10: Conjugate Direction Methods
Chapter 11: QuasiNewton Methods
Chapter 12: Solving Linear Equations
Chapter 13: Unconstrained Optimization and Neural Networks
Chapter 14: Global Search Algorithms
Part III: Linear Programming
Chapter 15: Introduction to Linear Programming
Chapter 16: Simplex Method
Chapter 17: Duality
Chapter 18: Nonsimplex Methods
Chapter 19: Integer Linear Programming
Part IV: Nonlinear Constrained Optimization
Chapter 20: Problems with Equality Constraints
Chapter 21: Problems with Inequality Constraints
Chapter 22: Convex Optimization Problems
Chapter 23: Algorithms for Constrained Optimization
Chapter 24: Multiobjective Optimization
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