Generalized sensitivity analysis of nonlinear programs using a sequence of quadratic programs. (4th March 2019)

• Record Type: Journal Article
• Authors: Stechlinski, Peter; Jäschke, Johannes; Barton, Paul I.
• Is Part Of: Optimization. Volume 68:Number 2/3(2019)
• Page Start: 485
• Page End: 508
• Publication Date: 2019-03-04
• Subjects: Sensitivity analysis -- nonsmooth analysis -- generalized derivatives -- B-subdifferential -- parametric optimization -- NLP KKT systems; 49J52 -- 90C31 90C33; Mathematical optimization -- Periodicals; 519.7
• DOI: 10.1080/02331934.2018.1517159
• Languages: English
• ISSNs: 0233-1934

Abstract

ABSTRACT: Local sensitivity information is obtained for KKT points of parametric NLPs that may exhibit active set changes under parametric perturbations; under appropriate regularity conditions, computationally relevant generalized derivatives of primal and dual variable solutions of parametric NLPs are calculated. Ralph and Dempe obtained directional derivatives of solutions of parametric NLPs exhibiting active set changes from the unique solution of an auxiliary quadratic program. This article uses lexicographic directional derivatives, a newly developed tool in nonsmooth analysis, to generalize the classical NLP sensitivity analysis theory of Ralph and Dempe. By viewing said auxiliary quadratic program as a parametric NLP, the results of Ralph and Dempe are applied to furnish a sequence of coupled QPs, whose unique solutions yield generalized derivative information for the NLP. A practically implementable algorithm is provided. The theory developed here is motivated by widespread applications of nonlinear programming sensitivity analysis, such as in dynamic control and optimization problems.