Presents research contributions and tutorial expositions on current methodologies for sensitivity, stability and approximation analyses of mathematical programming and related problem structures involving parameters. The text features up-to-date findings on important topics, covering such areas as the effect of perturbations on the performance of algorithms, approximation techniques for optimal control problems, and global error bounds for convex inequalities.

Presents research contributions and tutorial expositions on current methodologies for sensitivity, stability and approximation analyses of mathematical programming and related problem structures involving parameters. The text features up-to-date findings on important topics, covering such areas as the effect of perturbations on the performance of algorithms, approximation techniques for optimal control problems, and global error bounds for convex inequalities.

This book presents theoretical results, including an extension of constant rank and implicit function theorems, continuity and stability bounds results for infinite dimensional problems, and the interrelationship between optimal value conditions and shadow prices for stable and unstable programs.

Theorem of constant rank to lipschitzian maps; Lipschitzian perturbations of infinite optimization problems; On the continuity of the optimum set in parametric semiinfinite programming; Optimality conditions and shadow prices; Optimal value continuity and differential stability bounds under the mangasarian-fromovitz constraint qualification; Iteration and sensitivity for a nonlinear spatial equilibrium problem; A sensitivity analysis approach to iteration skipping in the harmonic mean algorithm; Least squares optimization with implicit model equations.

Basic results; Applications and interfaces.

Introduction to Sensitivity and Stability Analysis in Nonlinear Programming