1 citations · 1 across the 4 of their papers we have counts for
6 papers
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…
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…
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…
Multiscale Methods for Discretized Continuous Optimization: Convergence and Cost Analysis
Nicholas J. E. Richardson, Noah Marusenko, Michael P. Friedlander
Discretized versions of optimization problems over continuous arguments are routinely solved at a single fine resolution, incurring a per-iteration cost that grows, often superline…
Decentralized Optimization with Topology-Independent Communication
Ying Lin, Yao Kuang, Ahmet Alacaoglu +1
Distributed optimization requires nodes to coordinate, yet full synchronization scales poorly. When nodes collaborate through pairwise regularizers, standard methods demand…
Scalable Data-Driven Basis Selection for Linear Machine Learning Interatomic Potentials
Tina Torabi, Matthias Militzer, Michael P. Friedlander +1
Machine learning interatomic potentials (MLIPs) provide an effective approach for accurately and efficiently modeling atomic interactions, expanding the capabilities of atomistic s…