A New Architecture for Optimization Modeling Frameworks

TL;DR

This paper introduces a novel architecture for optimization modeling frameworks that leverages computation graphs, enabling better support for high-performance platforms and specialized solvers, demonstrated through the cvxflow framework.

Contribution

It presents a new architecture for optimization frameworks using computation graphs, facilitating GPU and distributed computing, and introduces cvxflow, an open-source implementation.

Findings

cvxflow outperforms existing solvers

architecture supports GPUs and distributed systems

enhances customization of solvers

Abstract

We propose a new architecture for optimization modeling frameworks in which solvers are expressed as computation graphs in a framework like TensorFlow rather than as standalone programs built on a low-level linear algebra interface. Our new architecture makes it easy for modeling frameworks to support high performance computational platforms like GPUs and distributed clusters, as well as to generate solvers specialized to individual problems. Our approach is particularly well adapted to first-order and indirect optimization algorithms. We introduce cvxflow, an open-source convex optimization modeling framework in Python based on the ideas in this paper, and show that it outperforms the state of the art.