most citedScalable Data-Driven Basis Selection for Linear Machine Learning Interatomic Potentials

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math.OC2026

Last-Iterate Convergence of Stochastic Gradient Methods for Unbounded Problems

Kevin Kurian Thomas Vaidyan, Ahmet Alacaoglu, Michael P. Friedlander

Last-iterate convergence guarantees for stochastic gradient methods have attracted significant attention as practical implementations typically return the last iterate instead of a…

math.OC2026

Smoothed analysis of stability radii for the assignment problem

Michael P. Friedlander

Every linear program can tolerate small changes to its costs and still yield a solution of the original problem. The size of this tolerance measures how robust an optimal decision…

math.OC2026

Convergence Rate of the Last Iterate of Stochastic Proximal Algorithms

Kevin Kurian Thomas Vaidyan, Michael P. Friedlander, Ahmet Alacaoglu

We analyze two classical algorithms for solving additively composite convex optimization problems where the objective is the sum of a smooth term and a nonsmooth regularizer: proxi…