ADMM for 0/1 D-Opt and MESP relaxations
Optimization and Control
2025-03-26 v2
Abstract
The 0/1 D-optimality problem and the Maximum-Entropy Sampling problem are two well-known NP-hard discrete maximization problems in experimental design. Algorithms for exact optimization (of moderate-sized instances) are based on branch-and-bound. The best upper-bounding methods are based on convex relaxation. We present ADMM (Alternating Direction Method of Multipliers) algorithms for solving these relaxations and experimentally demonstrate their practical value.
Keywords
Cite
@article{arxiv.2411.03461,
title = {ADMM for 0/1 D-Opt and MESP relaxations},
author = {Gabriel Ponte and Marcia Fampa and Jon Lee and Luze Xu},
journal= {arXiv preprint arXiv:2411.03461},
year = {2025}
}