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Generalized sensitivity analysis of nonlinear programs using a sequence of quadratic programs

Stechlinski, Peter; Jaeschke, Johannes; Barton, Paul I.
Journal article, Peer reviewed
Accepted version
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NLP_SequentialQPs_secondsubmission_blackfont.pdf (425.5Kb)
Permanent lenke
http://hdl.handle.net/11250/2564970
Utgivelsesdato
2018
Metadata
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  • Institutt for kjemisk prosessteknologi [1240]
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Originalversjon
10.1080/02331934.2018.1517159
Sammendrag
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.
Utgiver
Taylor & Francis
Tidsskrift
Optimization

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