Minimization of marginal functions in mathematical programming based on continuous outer subdifferentials

Beitrag in einer Fachzeitschrift
(Originalarbeit)


Details zur Publikation

Autorinnen und Autoren: Knossalla M
Zeitschrift: Optimization
Jahr der Veröffentlichung: 2018
Seitenbereich: 1-21
ISSN: 0233-1934
eISSN: 1029-4945
Sprache: Englisch


Abstract


Typically, exact information of the whole subdifferential is not available for intrinsically nonsmooth objective functions such as for marginal functions. Therefore, the semismoothness of the objective function cannot be proved or is even violated. In particular, in these cases standard nonsmooth methods cannot be used. In this paper, we propose a new approach to develop a converging descent method for this class of nonsmooth functions. This approach is based on continuous outer subdifferentials introduced by us. Further, we introduce on this basis a conceptual optimization algorithm and prove its global convergence. This leads to a constructive approach enabling us to create a converging descentmethod. Within the algorithmic framework, neither semismoothness nor calculation of exact subgradients are required. This is in contrast to other approaches which are usually based on the assumption of semismoothness of the


objective function.


FAU-Autorinnen und Autoren / FAU-Herausgeberinnen und Herausgeber

Knossalla, Martin
Lehrstuhl für Angewandte Mathematik


Zitierweisen

APA:
Knossalla, M. (2018). Minimization of marginal functions in mathematical programming based on continuous outer subdifferentials. Optimization, 1-21. https://dx.doi.org/10.1080/02331934.2018.1426579

MLA:
Knossalla, Martin. "Minimization of marginal functions in mathematical programming based on continuous outer subdifferentials." Optimization (2018): 1-21.

BibTeX: 

Zuletzt aktualisiert 2019-01-01 um 19:10