SnapVX: A Network-Based Convex Optimization Solver

SnapVX: A Network-Based Convex Optimization Solver

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 lar...

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Veröffentlicht in:Journal of machine learning research 2017, Vol.18 (1), p.110-114
Hauptverfasser: Hallac, David, Wong, Christopher, Diamond, Steven, Sharang, Abhijit, Sosič, Rok, Boyd, Stephen, Leskovec, Jure
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container_end_page 114
container_issue 1
container_start_page 110
container_title Journal of machine learning research
container_volume 18
creator Hallac, David
Wong, Christopher
Diamond, Steven
Sharang, Abhijit
Sosič, Rok
Boyd, Stephen
Leskovec, Jure
description 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.
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title SnapVX: A Network-Based Convex Optimization Solver
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