From the 1 of 4 linked papers with an AI index.
4 papers
Multiscale Methods for Discretized Continuous Optimization: Convergence and Cost Analysis
Nicholas J. E. Richardson, Noah Marusenko, Michael P. Friedlander
The paper studies a multiscale algorithm that solves a sequence of increasingly fine discretizations of continuous optimization problems, using coarse solutions to warm‑start finer…
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…
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…
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…