This textbook on Linear and Nonlinear Optimization is intended for graduate and advanced undergraduate students in operations research and related fields. It is both literate and mathematically strong, yet requires no prior course in optimization. As suggested by its title, the book is divided...

Chapter 1. Introduction -- Chapter 2. The Simplex Method -- Chapter 3. Degeneracy -- Chapter 4. Efficiency of the Simplex Method -- Chapter 5. Duality Theory -- Chapter 6. The Simplex Method in Matrix Notational -- Chapter 7. Sensitivity and Parametric Analyses -- Chapter 8. Implementation...

Introduction to Semidefinite, Conic and Polynomial Optimization -- The Approach of Moments for Polynomial Equations -- Algebraic Degree in Semidefinite and Polynomial Optimization -- Semidefinite Representation of Convex Sets and Convex Hulls -- Convex Hulls of Algebraic Sets -- Convex Relations...

Potential Function Methods For Approximately Solving Linear Programming Problems breaks new ground in linear programming theory. The book draws on the research developments in three broad areas: linear and integer programming, numerical analysis, and the computational architectures which enable...

This Fourth Edition introduces the latest theory and applications in optimization. It emphasizes constrained optimization, beginning with a substantial treatment of linear programming and then proceeding to convex analysis, network flows, integer programming, quadratic programming, and convex...