TL;DR
This paper revisits the approximate Carathéodory problem using the Frank-Wolfe algorithm, providing simplified analysis, improved bounds, and practical methods for sparse projections in various $\, ext{l}_p$-norm scenarios.
Contribution
It introduces a Frank-Wolfe based approach for the approximate Carathéodory problem, offering simplified analysis, improved bounds, and methods for sparse projections across different norms.
Findings
Derived natural cardinality bounds using Frank-Wolfe convergence rates.
Provided bounds for cases when the point is interior or a convex combination with small diameter.
Extended bounds to nonsmooth variants for $p$ in [1,2) and infinity.
Abstract
The approximate Carath\'eodory theorem states that given a compact convex set and , each point can be approximated to -accuracy in the -norm as the convex combination of vertices of , where is the diameter of in the -norm. A solution satisfying these properties can be built using probabilistic arguments or by applying mirror descent to the dual problem. We revisit the approximate Carath\'eodory problem by solving the primal problem via the Frank-Wolfe algorithm, providing a simplified analysis and leading to an efficient practical method. Furthermore, improved cardinality bounds are derived naturally using existing convergence rates of the Frank-Wolfe algorithm in different scenarios, when is in the interior of…
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.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
