On computing sparse universal solvers for key problems in statistics
Optimization and Control
2025-09-05 v1
Abstract
We give sparsity results and present algorithms for calculating minimum (vector) 1-norm universal solvers connected to least-squares problems. In particular, besides universal least-squares solvers, we consider minimum-rank universal least-squares solvers, and simultaneous universal minimum-norm/least-squares solvers. For all of these, we present and compare several new alternative linear-optimization formulations and very effective proximal-point algorithms. Overall, we found that our new Douglas-Rachford splitting algorithms for these problems performed best.
Cite
@article{arxiv.2509.04264,
title = {On computing sparse universal solvers for key problems in statistics},
author = {Ananias Sousa Machado and Marcia Fampa and Jon Lee},
journal= {arXiv preprint arXiv:2509.04264},
year = {2025}
}