A New Architecture for Optimization Modeling Frameworks
Stanford University
arXiv:1609.03488 [math.OC], (12 Sep 2016)
@article{wytock2016architecture,
title={A New Architecture for Optimization Modeling Frameworks},
author={Wytock, Matt and Diamond, Steven and Heide, Felix and Boyd, Stephen},
year={2016},
month={sep},
archivePrefix={"arXiv"},
primaryClass={math.OC}
}
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.
September 13, 2016 by hgpu
