7 papers
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…
On propagation of chaos for the Fisher-Rao gradient flow in entropic mean-field optimization
Petra LaziÄ, Linshan Liu, Mateusz B. Majka
We consider a class of optimization problems on the space of probability measures motivated by the mean-field approach to studying neural networks. Such problems can be solved by c…
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…
Geometric ergodicity of modified Euler schemes for SDEs with super-linearity
Jianhai Bao, Mateusz B. Majka, Jian Wang
As a well-known fact, the classical Euler scheme works merely for SDEs with coefficients of linear growth. In this paper, we study a general framework of modified Euler schemes, wh…
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…