SnapVX: A Network-Based Convex Optimization Solver

D. Hallac, C. Wong, S. Diamond, A. Sharang, R. Sosic, S. Boyd, and J. Leskovec

Journal of Machine Learning Research, (18):1-5, 2017.

SnapVX is a high-performance solver for convex optimization problems defined on networks. For problems of this form, SnapVX provides a fast and scalable solution with guaranteed global convergence. It combines the capabilities of two open source software packages: and CVXPY. 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 Python interface with out-of-the-box functionality. Based on the Alternating Direction Method of Multipliers (ADMM), it is able to efficiently store, analyze, parallelize, and solve large optimization problems from a variety of different applications. Documentation, examples, and more can be found on the SnapVX website.