One-Shot Klein Cutting Planes for Lipschitz Geodesically Convex Optimization in Hyperbolic Space
Yutong Zhang,Yaoran Yang,Yifan Zhu,Wentao Zhang

TL;DR
This paper introduces a one-shot Klein cutting-plane method for Lipschitz geodesically convex optimization in hyperbolic space, achieving near-optimal oracle complexity and localization without convex coordinate pullback.
Contribution
It provides the first deterministic first-order method for negative curvature hyperbolic spaces with provable guarantees, extending convex optimization techniques to quasiconvex settings.
Findings
Achieves oracle complexity of O(d^2 ζ_s log(e/ε)) for hyperbolic convex optimization.
Localization update costs O(d^2) operations for dimensions d≥2.
Method works in quasiconvex setting without convex coordinate pullback.
Abstract
We solve the negative constant-curvature case of the COLT 2023 open problem of Criscitiello, Mart\'inez-Rubio, and Boumal on deterministic first-order methods for Lipschitz geodesically convex optimization. Let \[ \HH^d_{-\kappaC^2}=\{X\in\R^{d+1}:\ipL{X}{X}=-1,\ X_0>0\}, \qquad \ip{U}{V}_{X}=\kappaC^{-2}\ipL{U}{V}, \] so the sectional curvature is . If \[ f:\bar B_{\HH}(x_0,r)\to\R \] is geodesically convex and -Lipschitz, and , our one-shot Klein cutting-plane method returns a queried point with \[ f(\hat x)-\min_{\bar B_{\HH}(x_0,r)}f\le \eps Mr \] using at most \[ \left\lceil 2d(d+1) \log\!\left(\frac{16\sinh s\cosh s}{s\eps}\right)\right\rceil \] oracle calls. For each localization update costs arithmetic operations; for an interval variant satisfies the same bound. Consequently \[…
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