Optimization : algorithms and applications / Rajesh Kumar Arora.

Format:

Book

Author/Creator:

Arora, Rajesh Kumar.

Language:

English

Subjects (All):

MATLAB.

Mathematical optimization.

Physical Description:

1 online resource (xv, 450 p.) : ill.

Edition:

1st edition

Other Title:

Algorithms and applications

Place of Publication:

Boca Raton, Fla. : CRC P., 2015.

Summary:

Optimization: Algorithms and Applications presents a variety of solution techniques for optimization problems, emphasizing concepts rather than rigorous mathematical details and proofs. The book covers both gradient and stochastic methods as solution techniques for unconstrained and constrained optimization problems. It discusses the conjugate gradient method, Broyden-Fletcher-Goldfarb-Shanno algorithm, Powell method, penalty function, augmented Lagrange multiplier method, sequential quadratic programming, method of feasible directions, genetic algorithms, particle swarm optimization (PSO), simulated annealing, ant colony optimization, and tabu search methods. The author shows how to solve non-convex multi-objective optimization problems using simple modifications of the basic PSO code. The book also introduces multidisciplinary design optimization (MDO) architectures - one of the first optimization books to do so - and develops software codes for the simplex method and affine-scaling interior point method for solving linear programming problems. In addition, it examines Gomory’s cutting plane method, the branch-and-bound method, and Balas’ algorithm for integer programming problems. The author follows a step-by-step approach to developing the MATLAB® codes from the algorithms. He then applies the codes to solve both standard functions taken from the literature and real-world applications, including a complex trajectory design problem of a robot, a portfolio optimization problem, and a multi-objective shape optimization problem of a re-entry body. This hands-on approach improves your understanding and confidence in handling different solution methods.

Contents:

1. Introduction

2. 1-D Optimization Algorithms

3. Unconstrained Optimization

4. Linear Programming

5. Guided Random Search Methods

6. Constrained Optimization

7. Multiobjective Optimization

8. Geometric Programming

9. Multidisciplinary Design Optimization

10. Integer Programming

11. Dynamic Programming

Bibliography

Appendix A: Introduction to MATLAB

Appendix B: MATLAB Code

Appendix C: Solutions to Chapter Problems

Index.

Notes:

A Chapman & Hall book.

Includes bibliographical references.

ISBN:

9781040072677

1040072674

9780429162527

0429162529

OCLC:

909904131