Greedy box splitting reads gradient signs at the centre and retains one smaller box. Its summable travel budget makes an incorrect exclusion irreversible. We determine when this geometry is safe. Halving is minimax optimal in the sign-vector oracle model on a coordinatewise sign-consistent class that need not be convex. A dimensionless sign defect yields a sharp scalar envelope, attained by one smooth convex potential; a three-dimensional trajectory ends farther from the target than its initial radius. For quadratics, universal convergence with aspect $w$ and contraction beta holds exactly when the weighted absolute row defect is at most $(2 \beta - 1)$. Perron weights minimise this defect. Adaptive half-sizes avoid initial enlargement and, under persistent error, converge to an exact capped fixed-point limit. For binary comparisons, an independent probe fraction $q$ permits universal localisation on common-target monotone sections exactly when $(q \leq 2 \beta - 1)$; coupled probes give the sharp threshold $2/3$. Matching information bounds distinguish comparisons from sign-vector queries. Error bands give finite-time guarantees and sharp residual floors. Extensions and reproducible experiments identify the assumptions and costs of freezing, smoothing, voting, multistart and random-matrix certificates.
Powell asked whether the smoothness assumptions underlying classical superlinear convergence force a fixed power law between adjacent iterates of exact-line-search variable-metric methods. We answer this question negatively for BFGS: within the smooth strongly convex setting, the smallest possible adjacent-iterate Q-or...
We consider stochastic nonconvex optimization with sample-wise Lipschitz gradients and Hessians, and reusable opaque sample handles. Gradient and complete-Hessian evaluations are charged separately, including both endpoints of a same-sample difference. For fixed positive problem parameters and in the high-dimensional w...
Qian Chen, Wen-Dao Wu, Hai-Han Zhang et al.· 0 citations
It is proved that Covariance-matched analytic uniformly log-concave sources can have a greatest useful-data-only private release with infinite information, while no greatest full-data release exists.
We determine the exact worst-case function-value suboptimality after $N$ first-order oracle calls on $L$-smooth, $\mu$-strongly convex functions under a nonnegative weighted combination of the initial squared distance, function-value suboptimality, and squared gradient norm. Excluding the case where no strict improveme...
Does randomization improve the worst-case first-order complexity of nonconvex optimization when the objective is very smooth? We prove that, for every fixed finite integer $p\ge3$, finding an $\eps$-stationary point requires $\Omega_p(\eps^{-5/3})$ exact value-and-gradient queries, even for $C^\infty$ objectives with i...
Hai-Han Zhang, Wen-Dao Wu, Fang-Qing Zhang et al.· 0 citations
Restricted eigenvalue (RE) bounds govern stable recovery by norm-regularized estimators. For isotropic sub-Gaussian measurements, the benchmark sample size is $1+w(A)^2$, where $w(A)$ is the Gaussian width of the normalized descent cone. The COLT 2015 open-problem note (Banerjee et al., 2015) asked whether the same law...
Shi Fu, Hui-Bo Xu, Qi-Xin Zhang et al.· 0 citations
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