Towards Constituting Mathematical Structures for Learning to Optimize

Author name not available (Why is that?)

Publication date: 29 May 2023

Abstract: Learning to Optimize (L2O), a technique that utilizes machine learning to learn an optimization algorithm automatically from data, has gained arising attention in recent years. A generic L2O approach parameterizes the iterative update rule and learns the update direction as a black-box network. While the generic approach is widely applicable, the learned model can overfit and may not generalize well to out-of-distribution test sets. In this paper, we derive the basic mathematical conditions that successful update rules commonly satisfy. Consequently, we propose a novel L2O model with a mathematics-inspired structure that is broadly applicable and generalized well to out-of-distribution problems. Numerical simulations validate our theoretical findings and demonstrate the superior empirical performance of the proposed L2O model.

Has companion code repository: https://github.com/xhchrn/ms4l2o

This page was built for publication: Towards Constituting Mathematical Structures for Learning to Optimize

Report a bug (only for logged in users!)Click here to report a bug for this page (MaRDI item Q6438409)