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arxiv: 2411.03461 · v3 · pith:FXBCGKPUnew · submitted 2024-11-05 · 🧮 math.OC

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

Gabriel Ponte , Marcia Fampa , Jon Lee , Luze Xu This is my paper
classification 🧮 math.OC
keywords admmalgorithmsd-optimalitymaximum-entropyproblemrelaxationssamplingalternating
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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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