Nonlinear programming provides an excellent opportunity to explore an interesting variety of pure and solidly applicable mathematics, numerical analysis, and computing. This text develops some of the ideas and techniques involved in the optimization methods using calculus, leading to the study of convexity. This is followed by material on basic numerical methods, least squares, the Karush-Kuhn-Tucker theorem, penalty functions, and Lagrange multipliers. The authors have aimed their presentation at the student who has a working knowledge of matrix algebra and advanced calculus, but has had no previous exposure to optimization.

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Preface; 1: Unconstrained Optimization via Calculus; 2: Convex and Convex Functions; 3: Iterative Methods for Unconstrained Optimization; 4: Least Squares Optimization; 5: Convex Programming and the Karush-Kuhn-Tucker Conditions; 6: Penalty Methods; 7: Optimization with Equality Constraints; Index

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