Description
Book SynopsisThis book presents the mathematics of finite dimensional constrained optimization problems. It offers a solid presentation of real analysis and provides a basis for the mathematical study of convexity, of more general optimization problems, and of numerical algorithms for the solution of finite dimensional optimization problems.
Trade Review"...a nice introduction to finite-dimensional optimization..."(Zentralblatt Math, Vol.991, No.16, 2002)
"A textbook for a one-semester...course for students ofengineering, economics, operations research, and mathematics."(SciTech Book News, Vol. 26, No. 2, June 2002)
"...a fine introductory textbook that provides a solid introductionto the subject as well as a good foundation for further study..."(Mathematical Reviews, 2003a)
Table of ContentsPreface.
I: Topics in Real Analysis.
1. Introduction.
2. Vectors in R".
3. Algebra of Sets.
4. Metric Topology of R".
5. Limits and Continuity.
6. Basic Propertyof Real Numbers.
7. Compactness.
8. Equivalent Norms and Cartesian Products.
9. Fundamental Existence Theorem.
10. Linear Transformations.
11. Differentiation in R".
II: Convex Sets in R".
1. Lines and Hyperplanes in R".
2. Properties of Convex Sets.
3. Separation Theorems.
4. Supporting Hyperplanes:Extreme Points.
5. Systems of Linear Inequalities:Theorems of the Alternative.
6. Affine Geometry.
7. More on Separation and Support.
III: Convex Functions.
1. Definition and Elementary Properties.
2. Subgradients.
3. Differentiable Convex Functions.
4. Alternative Theorems for Convex Functions.
5. Application to Game Theory.
IV: Optimization Problems.
1. Introduction.
2. Differentiable Unconstrained Problems.
3. Optimization of Convex Functions.
4. Linear Programming Problems.
5. First-Order Conditions for Differentiable NonlinearProgrammingProblems.
6. Second-Order Conditions.
V: Convex Programming and Duality.
1. Problem Statement.
2. Necessary Conditions and Sufficient Conditions.
3. Perturbation Theory.
4. Lagrangian Duality.
5. Geometric Interpretation.
6. Quadratic Programming.
7. Dualityin Linear Programming.
VI: Simplex Method.
1. Introduction.
2. Extreme Points of Feasible Set.
3. Preliminaries to Simplex Method.
4. Phase II of Simplex Method.
5. Termination and Cycling.
6. Phase I of Simplex Method.
7. Revised Simplex Method.
Bibliography.
Index.
