Optimization Learning

TL;DR

This paper introduces optimization learning, a methodology for creating trustworthy, scalable optimization proxies that learn input/output mappings, provide guarantees, and are applicable to large-scale problems like power systems.

Contribution

It proposes a novel framework for optimization proxies that are differentiable, self-supervised, and capable of scaling to large problems with performance guarantees.

Findings

Optimization proxies can be trained end-to-end in a self-supervised manner.

They provide feasibility and quality guarantees for solutions.

Applications demonstrated in power systems for real-time risk assessment.

Abstract

This article introduces the concept of optimization learning, a methodology to design optimization proxies that learn the input/output mapping of parametric optimization problems. These optimization proxies are trustworthy by design: they compute feasible solutions to the underlying optimization problems, provide quality guarantees on the returned solutions, and scale to large instances. Optimization proxies are differentiable programs that combine traditional deep learning technology with repair or completion layers to produce feasible solutions. The article shows that optimization proxies can be trained end-to-end in a self-supervised way. It presents methodologies to provide performance guarantees and to scale optimization proxies to large-scale optimization problems. The potential of optimization proxies is highlighted through applications in power systems and, in particular, real-time risk assessment and security-constrained optimal power flow.