For example, we had to ignore the extensive progress which has been made in the development of numerical methods which do not require convexity assumptions to find local maxima and minima of non-linear optimization problems.

Contenuti

I. Linear Optimization.- �1. Introduction.- �2. Linear Optimization and Polyhedra.- �3. Vertex Exchange and the Simplex Method.- �4. Algorithmic Implementation of the Simplex Method.- �5. Dual Linear Optimization Problems.- II. Convex Optimization.- �6. Introduction.- �7. A Characterization of Minimal Solutions for Convex Optimization.- �8. Convex Optimization for Differentiable Functions.- �9. Convex Optimization with Affine Linear Constraints.- �10. The Numerical Treatment of Convex Optimization Problems.- III. Quadratic Optimization.- �11. Introduction.- �12. The Kuhn-Tucker Theorem and Applications..- �13. Duality for Quadratic Optimization.- �14. The Numerical Treatment of Quadratic Optimization Problems.- IV. Tchebychev Approximation and Optimization.- � 15. Introduction.- �16. Discrete Linear Tchebychev Approximation.- �17. Further Types of Approximation Problems.- V. Elements of Game Theory.- �18. Matrix Games (Two Person Zero Sum Games).- �19. n-Person Games.- Problems.

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