A bundle-free implicit programming approach for a class of elliptic MPECs in function space

Using a standard first-order optimality condition for nonsmooth optimization problems, a general framework for a descent method is developed. This setting is applied to a class of mathematical programs with equilibrium constraints in function space from which a new algorithm is derived. Global conve...

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Veröffentlicht in:Mathematical programming Jg. 160; H. 1-2; S. 271 - 305
Hauptverfasser: Hintermüller, M., Surowiec, T.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Berlin/Heidelberg Springer Berlin Heidelberg 01.11.2016
Springer Nature B.V
Schlagworte:
Algorithms
Approximation
Calculus of Variations and Optimal Control; Optimization
Combinatorics
Convergence
Experiments
Full Length Paper
Function space
Mathematical analysis
Mathematical and Computational Physics
Mathematical Methods in Physics
Mathematical models
Mathematical programming
Mathematics
Mathematics and Statistics
Mathematics of Computing
Numerical Analysis
Optimization
Partial differential equations
Programming
Studies
Theoretical
Implicit programming
49K21
49K20
Elliptic MPEC
49J52
90C33
Nonsmooth optimization
49M05
Optimal control of variational inequalities
65Kxx
Elliptic variational inequality
ISSN:0025-5610, 1436-4646
Online-Zugang:Volltext
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Beschreibung
Zusammenfassung:Using a standard first-order optimality condition for nonsmooth optimization problems, a general framework for a descent method is developed. This setting is applied to a class of mathematical programs with equilibrium constraints in function space from which a new algorithm is derived. Global convergence of the algorithm is demonstrated in function space and the results are then illustrated by numerical experiments.
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ISSN:0025-5610
1436-4646
DOI:10.1007/s10107-016-0983-9