An Introduction To Optimization

An Introduction To Optimization Fourth Edition. 

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Fully updated to reflect new developments in the field, the Fourth Edition of Introduction to Optimization fills the need for accessible treatment on 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 and algorithms
  • Numerous diagrams and figures that complement the written of key concepts
  • MATLAB® M-files for implementation of the discussed theory and algorithms (available via the book’s website)

Edwin K. P. Chong(Autor)

Table of Contents


Part I. Mathematical Review

1 Methods of Proof and Some Notation
2 Vector Spaces and Matrices
3 Transformations
4 Concepts from Geometry
5 Elements of Calculus

Part II. Unconstrained Optimization

6 Basics of Set-Constrained and Unconstrained Optimization
7 One-Dimensional Search Methods
8 Gradient Methods
9 Newton’s Method
10 Conjugate Direction Methods
11 Quasi-Newton Methods
12 Solving Linear Equations
13 Unconstrained Optimization and Neural Networks
14 Global Search Algorithms

Part III. Linear Programming

15 Introduction to Linear Programming
16 Simplex Method
17 Duality
18 Nonsimplex Methods
19 Integer Linear Programming

Part IV. Nonlinear Constrained Optimization

20 Problems with Equality Constraints
21 Problems With Inequality Constraints
22 Convex Optimization Problems
23 Algorithms for Constrained Optimization
24 Multiobjective Optimization

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