A unified majorization framework and double-scaling bound enhancement yield stronger convex relaxations for the NP-hard maximum entropy sampling problem, with proven dominance over linx and Gamma factorization methods and superior numerical performance.
Thus, 1 2 [OptΓ + OptΓc]≤f linx-o(x∗, κ)≤f linx-o(x∗, ρ∗
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From Majorization to Scaling: Advancing Convex Relaxations of Maximum Entropy Sampling Problem
A unified majorization framework and double-scaling bound enhancement yield stronger convex relaxations for the NP-hard maximum entropy sampling problem, with proven dominance over linx and Gamma factorization methods and superior numerical performance.