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.

11.1 Central Idea.

11.2 Data Handling Examples.

11.3. Solutions to Differential Systems.

11.4 Summary.

References.

Index.