SnapVX: A Network-Based Convex Optimization Solver

September 21, 2015 · Entered Twilight · 🏛 Journal of machine learning research

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Repo contents: .gitignore, DevTests, Examples, LICENSE.md, MANIFEST, README.md, Tests, developerDoc, doc, setup.py, snapvx.py

Authors David Hallac, Christopher Wong, Steven Diamond, Abhijit Sharang, Rok Sosic, Stephen Boyd, Jure Leskovec arXiv ID 1509.06397 Category cs.SI: Social & Info Networks Cross-listed cs.MS, math.OC Citations 25 Venue Journal of machine learning research Repository https://github.com/snap-stanford/snapvx ⭐ 66 Last Checked 11 days ago

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.