The Optimizer's Windfall: Bounding Hindsight Gain under Random Restrictions

Abstract

Suppose an optimization problem is randomly restricted, independently on each variable, and re-optimized after the restriction is revealed. For multilinear objectives, every fixed solution is unbiased after rescaling, but re-optimizing introduces an upward bias. We bound this excess, the hindsight gain, over products of arbitrary compact local domains in Euclidean unit balls. For interactions $A=(A_e)_e$ among $r$ distinct variables and independent random weights $ζ_i$, the gain is at most $O_r(\|A\|_F M^{(r-1)/2}σ_{\max})$, where $\|A\|_F^2$ is the sum of squared coefficients, $M=\sum_i\mathbb{E}ζ_i^2$, and $σ_{\max}^2=\max_i\mathrm{Var}(ζ_i)$; only second moments are needed. Under uniform retention with probability $p$, we sharpen this to $O_r(p^{r-1/2}\sqrt{1-p}\,n^{(r-1)/2}\|A\|_F)$, which is tight for every fixed $r$ and $p\in(0,1)$. Our proof combines a comparison principle along a single support direction with tensor projection, vector contraction, and repeated thinning. Applications: (i) For fixed arity and alphabet size, solving the subproblem induced by $O(\varepsilon^{-2})$ sampled variables estimates a dense Max-$r$CSP optimum within $\pm\varepsilon n^r$, and this sample size is optimal. (ii) The same approach estimates MaxCut and the distance to bipartiteness within $\pm\varepsilon n^2$ using $O(\varepsilon^{-4})$ nonadaptive queries and $O(\varepsilon^{-2})$ examined vertices, both optimal. (iii) We answer a question of Yufei Zhao: vertex percolation at rate $p$ preserves the max-cut fraction whenever $p\,e(G)/v(G)\to\infty$, and this condition is sharp. (iv) For bounded $r$-local quantum Hamiltonians of fixed local dimension, $O(\varepsilon^{-3})$ uniformly sampled particles suffice to estimate the ground-state energy within $\pm\varepsilon n^r$, improving the previous $O(\varepsilon^{-6}\log(1/\varepsilon))$ bound.