Extensions of algorithmic senstivity calculations nonlinear programming using barrier and penalty functions

Abstract

We provide a theoretical basis for approximating the sensitivity of a perturbed solution and the local optimalvalue function, using information generated by a sequential unconstrained minimization technique in the normal course of solving a mathematical program. We show that various algorithmic sensitivity results can be obtained without other assumptions than those needed for the corresponding nonalgorithmic results. Our results extend the algorithmic calculation of sensitivity information introduced by Fiacco, utilizing the logarithmic barrier function and quadratic penalty function

Keywords:

• Algorithmic Sensitivity
• Nonlinear Programming
• Barrier and Penalty Function