6 papers
Mirror descent algorithms with logarithmic barriers
Alberto De Marchi, Yura Malitsky, Adrien B. Taylor
This work derives convergence guarantees for mirror descent and proximal mirror descent algorithms when a logarithmic barrier is used as a distance-generating function. Standard ap…
Towards Weaker Variance Assumptions for Stochastic Optimization
Ahmet Alacaoglu, Yura Malitsky, Stephen J. Wright
We revisit a classical assumption for analyzing stochastic gradient algorithms where the squared norm of the stochastic subgradient (or the variance for smooth problems) is allowed…
Entropic Mirror Descent for Linear Systems: Polyak's Stepsize and Implicit Bias
Yura Malitsky, Alexander Posch
This paper focuses on applying entropic mirror descent to solve linear systems, where the main challenge for the convergence analysis stems from the unboundedness of the domain. To…
A First-Order Algorithm for Decentralised Min-Max Problems
Yura Malitsky, Matthew K. Tam
In this work, we consider a connected network of finitely many agents working cooperatively to solve a min-max problem with convex-concave structure. We propose a decentralised fir…
Adaptive Gradient Descent on Riemannian Manifolds with Nonnegative Curvature
Aban Ansari-Ãnnestam, Yura Malitsky
In this paper, we present an adaptive gradient descent method for geodesically convex optimization on a Riemannian manifold with nonnegative sectional curvature. The method automat…
Adaptive Proximal Gradient Method for Convex Optimization
Yura Malitsky, Konstantin Mishchenko
In this paper, we explore two fundamental first-order algorithms in convex optimization, namely, gradient descent (GD) and proximal gradient method (ProxGD). Our focus is on making…