SnapVX: A Network-Based Convex Optimization Solver
Abstract
SnapVX is a high-performance Python solver for convex optimization problems defined on networks. For these problems, it provides a fast and scalable solution with guaranteed global convergence. SnapVX combines the capabilities of two open source software packages: Snap.py and CVXPY. Snap.py is a large scale graph processing library, and CVXPY provides a general modeling framework for small-scale subproblems. SnapVX offers a customizable yet easy-to-use interface with out-of-the-box functionality. Based on the Alternating Direction Method of Multipliers (ADMM), it is able to efficiently store, analyze, and solve large optimization problems from a variety of different applications. Documentation, examples, and more can be found on the SnapVX website at http://snap.stanford.edu/snapvx.
Keywords
Cite
@article{arxiv.1509.06397,
title = {SnapVX: A Network-Based Convex Optimization Solver},
author = {David Hallac and Christopher Wong and Steven Diamond and Abhijit Sharang and Rok Sosic and Stephen Boyd and Jure Leskovec},
journal= {arXiv preprint arXiv:1509.06397},
year = {2017}
}