An Introduction to Optimization, 4 edition

An Introduction to Optimization, 4 edition
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Book Description

An Introduction to Optimization, 4 edition by Edwin K. P. Chong, Stanislaw H. Zak

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.

Content

Part I: Mathematical Review
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 Set-Constrained and Unconstrained Optimization
Chapter 7: One-Dimensional Search Methods
Chapter 8: Gradient Methods
Chapter 9: Newton’s Method
Chapter 10: Conjugate Direction Methods
Chapter 11: Quasi-Newton 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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