Open AccessProceedings Article
An Approximate, Efficient LP Solver for LP Rounding
Srikrishna Sridhar,Stephen J. Wright,Christopher Ré,Ji Liu,Victor Bittorf,Ce Zhang +5 more
- 05 Dec 2013
- Vol. 26, pp 2895-2903
TL;DR: This paper shows that on such combinatorial problems as vertex cover, independent set and multiway-cut, the approximate rounding scheme is up to an order of magnitude faster than Cplex while producing solutions of similar quality.
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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 exact 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
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Yuri Boykov,Vladimir Kolmogorov +1 more
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