Tight Lower Bounds under Asymmetric High-Order H\"older Smoothness and Uniform Convexity
Cedar Site Bai, Brian Bullins

TL;DR
This paper establishes tight lower bounds for the complexity of optimizing high-order smooth and uniformly convex functions, extending previous results and matching known upper bounds in both asymmetric cases.
Contribution
It provides the first tight lower bounds for high-order H"older smooth and uniformly convex functions in asymmetric cases, generalizing prior bounds.
Findings
Lower bounds match existing upper bounds in the setting.
Results cover both cases q > p + ν and q < p + ν.
Analysis applies to functions with p-th order H"older continuous derivatives.
Abstract
In this paper, we provide tight lower bounds for the oracle complexity of minimizing high-order H\"older smooth and uniformly convex functions. Specifically, for a function whose -order derivatives are H\"older continuous with degree and parameter , and that is uniformly convex with degree and parameter , we focus on two asymmetric cases: (1) , and (2) . Given up to -order oracle access, we establish worst-case oracle complexities of in the first case with an -ball-truncated-Gaussian smoothed hard function and $\Omega\left(\left(\frac{H}{\sigma}\right)^\frac{2}{3(p+\nu)-2}+…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
Taxonomy
TopicsMathematical Approximation and Integration · Risk and Portfolio Optimization · Optimization and Variational Analysis
MethodsFocus
