Year: 2026
Type: preprint
Abstract: We establish matching lower bounds for deterministic first-order optimization of globally Lipschitz geodesically convex functions on hyperbolic space. A global minimizer lies within distance $r$ of a known point, each query returns an exact function value and a selected Riemannian subgradient, and the target objective gap is $\varepsilon Lr$, where $L$ is the Lipschitz constant. For sectional curvature $-k^2<0$, let $ρ=kr$ and $ζ=ρ/\tanhρ$. The optimal worst-case query complexity $Q^\star$, taken uniformly over dimensions, satisfies $Q^\star(ρ,\varepsilon)=Θ(ζ\varepsilon^{-2})$ for every $ρ>0$ and $0<\varepsilon\le1/64$. The new lower bound covers arbitrary adaptive deterministic queries and arbitrary outputs, matching the projected-subgradient upper rate of Zhang and Sra (2016). It removes the geodesic-span and historical-halfspace restrictions of earlier matching lower bounds. The construction extends a radial source from a convex carrier with boundary. A small residual cut selects minimizer candidates while an exact hyperbolic persistence inequality keeps every historical contact valid under future carrier growth. One fixed globally convex objective and one fixed subgradient selection consequently realize all exact replies simultaneously. Each query consumes $O(\varepsilon^2)$ of a geometric potential whose available budget is $Θ(ζ)$, yielding the product lower bound in a dimension linear in the query horizon.
Source: arXiv (Cornell University)
Institution: Peking University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7221153545