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math.OC2025
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…
math.OC2025
Adaptive Gradient Descent on Riemannian Manifolds with Nonnegative Curvature
Aban Ansari-Önnestam, 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…
math.OC2025
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…