"Linear and Nonlinear Programming" is considered a classic textbook in Optimization. While it is a classic, it also reflects modern theoretical insights. These insights provide structure to what might otherwise be simply a collection of techniques and results, and this is valuable both as a means for learning existing material and for developing new results. One major insight of this type is the connection between the purely analytical character of an optimization problem, expressed perhaps by properties of the necessary conditions, and the behavior of algorithms used to solve a problem. This was a major theme of the first edition of this book and the second edition expands and further illustrates this relationship.

"Linear and Nonlinear Programming" covers the central concepts of practical optimization techniques. It is designed for either self-study by professionals or classroom work at the undergraduate or graduate level for technical students. Like the field of optimization itself, which involves many classical disciplines, the book should be useful to system analysts, operations researchers, numerical analysts, management scientists, and other specialists from the host of disciplines from which practical optimization applications are drawn.

About The Author
David G. Luenberger has directed much of his career toward teaching "portable concepts" - organizing theory around concepts and actually "porting" the concepts to applications where, in the process, the general concepts are often discovered. The search for fundamentals has explicitly directed his research in the fields of control, optimization, planning, economics, and investments, and in turn, it is the discovery of these fundamentals that have motivated his textbook writing projects.

Table of Contents
1. Introduction
Part I: Linear Programming

• 2. Basic Properties of Linear Programs
• 3. The Simplex Method
• 4. Duality
• 5. Transportation and Network Flow Problems

Part II: Unconstrained Problems

• 6. Basic Properties of Solutions and Algorithms
• 7. Basic Descent Methods
• 8. Conjugate Direction Methods
• 9. Quasi- Newton Methods

Part III: Constrained Minimization

• 10. Constrained Minimization Conditions
• 11. Primal Methods. 12. Penalty and Barrier Methods
• 13. Dual and Cutting Plane Methods
• 14. Lagrange Methods

Appendix A: Mathematical Review

• A.1. Sets
• A.2. Matrix Notation
• A.3. Spaces
• A.4. Eigenvalues and Quadratic Forms
• A.5. Topological Concepts
• A.6. Functions

Appendix B: Convex Sets

• B.1. Basic Definitions
• B.2. Hyperplanes and Polytopes
• B.3. Separating and Supporting Hyperplanes
• B.4. Extreme Points

Appendix C: Gaussian Elimination
Bibliography
Index