5 papers · 1 filter
From Saddle Points Toward Global Minima: A Newton-Type Method on Wasserstein Space
Razvan-Andrei Lascu, Taiji Suzuki
We study the minimization of non-convex functionals over the Wasserstein space. While recent work has showed that perturbed Wasserstein gradient methods can avoid saddle points for…
Mirror Descent-Ascent for mean-field min-max problems
Razvan-Andrei Lascu, Mateusz B. Majka, Åukasz Szpruch
We study two variants of the mirror descent-ascent (MDA) algorithm for solving min-max problems on the space of measures: simultaneous and alternating. We work under assumptions of…
Non-convex entropic mean-field optimization via Best Response flow
Razvan-Andrei Lascu, Mateusz B. Majka
We study the problem of minimizing non-convex functionals on the space of probability measures, regularized by the relative entropy (KL divergence) with respect to a fixed referenc…
Entropic mean-field min-max problems via Best Response flow
Razvan-Andrei Lascu, Mateusz B. Majka, Åukasz Szpruch
We investigate the convergence properties of a continuous-time optimization method, the \textit{Mean-Field Best Response} flow, for solving convex-concave min-max games with entrop…
Linear convergence of proximal descent schemes on the Wasserstein space
Razvan-Andrei Lascu, Mateusz B. Majka, David Šiška +1
We investigate proximal descent methods, inspired by the minimizing movement scheme introduced by Jordan, Kinderlehrer and Otto, for optimizing entropy-regularized functionals on t…