Short description Over the last few decades, optimization techniques have been streamlined by the use of computers and artificial intelligence methods to analyze more variables (especially under non-linear, multivariable conditions) more quickly than ever before. This book covers all classical linear and nonlinear optimization techniques while focusing on the standard mathematical engine, MATLAB. As with the first edition, the author uses MATLAB in examples for running computer-based optimization problems. New coverage in this edition includes design optimization techniques such as Multidisciplinary Optimization, Explicit Solution for Boundary Value Problems, and Particle Swarm Optimization.
From the contents Preface to the Second Edition.
Preface.
Chapter 1: Introduction.
1.1 Optimization Fundamentals.
1.2 Introduction to MATLAB.
Problems.
Chapter 2: Graphical Optimization.
2.1 Problem Definition.
2.2 Graphical Solution.
2.3 Additional Examples.
2.4 Additional MATLAB Graphics.
References.
Problems.
Chapter 3: Linear Programming.
3.1 Problem Definition.
3.2 Graphical Solution.
3.3 Numerical Solution - The Simplex Method.
3.4 Additional Examples.
3.5.Additional Topics in Linear Programming.
References.
Problems.
Chapter 4: Nonlinear Programming.
4.1 Problem Definition.
4.2 Mathematical Concepts.
4.3 Analytical Conditions.
4.4 Examples.
4.5 Additional Topics.
References.
Problems.
Chapter 5: Numerical Techniques - The One Dimensional Problem.
5.1 Problem Definition.
5.2 Numerical Techniques.
5.3 Importance of the One Dimensional Problem.
5.4 Additional Examples.
References.
Problems.
Chapter 6: Numerical Techniques for Unconstrained Optimization.
6.1 Problem Definition.
6.2 Numerical Techniques: Non Gradient Methods.
6.3 Numerical Technique: Gradient Based Methods.
6.4 Numerical Technique: Second Order.
6.5 Additional Examples.
6.6 Summary.
References.
Problems.
Chapter 7: Numerical Techniques for Constrained Optimization.
7.1 Problem Definition.
7.2 Indirect Methods for Constrained Optimization.
7.3 Direct Methods for Constrained Optimization.
7.4 Additional Examples.
References.
Problems.
Chapter 8: Discrete Optimization.
8.1 Concepts in Discrete Programming.
8.2 Discrete Optimization Techniques.
8.3 Additional Examples.
References.
Problems.
Chapter 9: Global Optimization.
9.1 Problem Definition.
9.2 Numerical Techniques and Additional Examples.
References.
Problems.
Chapter 10: Optimization Toolbox from MATLAB.
10.1 The Optimization Toolbox.
10.2 Examples.
References.
Chapter 11: Hybrid Mathematics: An Application of.
