Optimal Parameter-Free Gradient Minimization in ${\ell }_p$ Geometry

Abstract

We study the first-order oracle complexity of finding a queried point with small gradient in ${\ell }_p$ geometry, with particular attention to the information needed to adapt the unknown smoothness and distance scales. In the strict counted local value--gradient model, no finite complexity bound can depend only on $LR/\eps$ without a nondegenerate local scale observation: a one-dimensional construction keeps $LR/\eps=4$ while defeating every prescribed finite query budget. We resolve Diakonikolas's general-${\ell }_p$ parameter-free extension question for every fixed $1<p<\infty$. Under a nondegenerate secant initialization, the method knows neither the smoothness constant $L$, the initial solution distance $R$, nor $f^{\ast }$, and returns a queried point $\hat{x}$ with $\|\nabla f(\widehat x)\|_q\le\eps$. For fixed finite $p>2$, we first establish the dimension-free deterministic known-parameter upper exponent $p/(p+2)$ in $K=LR/\eps$, matching the published lower polynomial exponent under its horizon and dimension qualifications. The finite local routine fits the same observable scale--radius procedure, so this exponent is preserved without knowing $L$ or $R$. Writing $\Kbar=\max\{1,LR/\eps\}$, the post-initialization pair-oracle complexity is $O_p(\Kbar^{1/2})$ for $1<p<2$, $O(\Kbar^{1/2})$ for $p=2$, and $O_p(\Kbar^{p/(p+2)})$ for $p>2$, together with the additive calibration cost $O_p(\log (e+L/M_0))$ in every regime.