Learning Trees of $\ell_0$-Minimization Problems
Machine Learning
2023-02-07 v1 Numerical Analysis
Numerical Analysis
Abstract
The problem of computing minimally sparse solutions of under-determined linear systems is hard in general. Subsets with extra properties, may allow efficient algorithms, most notably problems with the restricted isometry property (RIP) can be solved by convex -minimization. While these classes have been very successful, they leave out many practical applications. In this paper, we consider adaptable classes that are tractable after training on a curriculum of increasingly difficult samples. The setup is intended as a candidate model for a human mathematician, who may not be able to tackle an arbitrary proof right away, but may be successful in relatively flexible subclasses, or areas of expertise, after training on a suitable curriculum.
Cite
@article{arxiv.2302.02548,
title = {Learning Trees of $\ell_0$-Minimization Problems},
author = {G. Welper},
journal= {arXiv preprint arXiv:2302.02548},
year = {2023}
}