Im Rahmen des Forschung-Seminars Analyse und Optimierung hält Prof. Dr. Sorin-Mihai GRAD (Department für Angewandte Mathematik, ENSTA Paris, Polytechnisches Institut von Paris), am Donnerstag, 06.04.2023, ab 12:30 Uhr, einen Vortrag mit dem Titel:

Relaxed-inertial proximal point type algorithms for problems involving strongly quasiconvex functions

Abstrakt auf Englisch:

Introduced in the 1970’s by Martinet for minimizing convex functions and extended shortly afterwards by Rockafellar towards monotone inclusion problems, the proximal point algorithm turned out to be a viable computational method for solving various classes of optimization problems even beyond the convex framework.

In this talk we propose a relaxed-inertial proximal point type algorithm for solving optimization problems consisting in minimizing strongly quasiconvex functions whose variables lie in finitely dimensional linear subspaces. The method is then extended for equilibrium functions involving strongly quasiconvex functions. Computational results confirm the theoretical advances.

The talk contains joint work with Felipe Lara and Raúl Tintaya Marcavillaca (both from University of Tarapacá).

Die Präsentation findet online statt, auf der Plattform ZOOM, die Zugangsdaten finden Sie unten:

https://zoom.us/j/98704786553?pwd=RXBCYy9GRjBTK0RTM2QxbkV4NFVudz09

Meeting ID: 987 0478 6553
Passcode: optimizare