An Approximate, Efficient Solver for LP Rounding

Srikrishna Sridhar, Victor Bittorf, Ji Liu, Ce Zhang, Christopher Ré and Stephen J. Wright

Preprints

• NIPS 2013 Preprint

• Arxiv

Presentations

• EMC University Day 2013 Poster

• Pivotal Inc. 2013 Talk

• NIPS 2013 Poster

Abstract

Many problems in machine learning can be solved by rounding the solution of an appropriate linear program (LP). This paper shows that we can recover solutions of comparable quality by rounding an approximate LP solution instead of the ex- act one. These approximate LP solutions can be computed efficiently by applying a parallel stochastic-coordinate-descent method to a quadratic-penalty formulation of the LP. We derive worst-case runtime and solution quality guarantees of this scheme using novel perturbation and convergence analysis. Our experiments demonstrate that on such combinatorial problems as vertex cover, independent set and multiway-cut, our approximate rounding scheme is up to an order of magnitude faster than Cplex (a commercial LP solver) while producing solutions of similar quality.

Code

Coming soon!

Instances

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