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arXiv · 2411.03461

ADMM for 0/1 D-optimality and Maximum-Entropy Sampling Relaxations

Gabriel Ponte·Marcia Fampa·Jon Lee·Luze Xu
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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.

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Gabriel Ponte, Marcia Fampa, Jon Lee, Luze Xu. 2024-11-05. ADMM for 0/1 D-optimality and Maximum-Entropy Sampling Relaxations. https://arxiv.org/abs/2411.03461

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