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ADMM for 0/1 D-optimality and Maximum-Entropy Sampling Re...
[Submitted on 5 Nov 2024 (v1), last revised 16 Jun 2026 (this ve · 2026-06-17 · via math updates on arXiv.org

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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.

Submission history

From: Jon Lee [view email]
[v1] Tue, 5 Nov 2024 19:24:30 UTC (144 KB)
[v2] Tue, 25 Mar 2025 15:14:45 UTC (461 KB)
[v3] Tue, 16 Jun 2026 14:59:25 UTC (515 KB)