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J Mach Learn Res. 2017;18(1):110-114.

SnapVX: A Network-Based Convex Optimization Solver.

Author information

1
Department of Electrical Engineering, Stanford University, Stanford, CA, 94305.
2
Department of Computer Science, Stanford University, Stanford, CA, 94305.

Abstract

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: 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 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 at http://snap.stanford.edu/snapvx.

KEYWORDS:

ADMM; convex optimization; data mining; graphs; network analytics

PMID:
29599649
PMCID:
PMC5870756

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