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arXiv · 2509.04264

On computing sparse universal solvers for key problems in statistics

Ananias Sousa Machado·Marcia Fampa·Jon Lee
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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-programming formulations and very effective proximal-point algorithms. Overall, we found that our new Douglas-Rachford splitting algorithms for these problems performed best.

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Ananias Sousa Machado, Marcia Fampa, Jon Lee. 2025-09-04. On computing sparse universal solvers for key problems in statistics. https://arxiv.org/abs/2509.04264

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