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arXiv · 1302.1541

Algorithm Portfolio Design: Theory vs. Practice

Carla P. Gomes·Bart Selman
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Abstract

Stochastic algorithms are among the best for solving computationally hard search and reasoning problems. The runtime of such procedures is characterized by a random variable. Different algorithms give rise to different probability distributions. One can take advantage of such differences by combining several algorithms into a portfolio, and running them in parallel or interleaving them on a single processor. We provide a detailed evaluation of the portfolio approach on distributions of hard combinatorial search problems. We show under what conditions the protfolio approach can have a dramatic computational advantage over the best traditional methods.

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Carla P. Gomes, Bart Selman. 2013-02-06. Algorithm Portfolio Design: Theory vs. Practice. https://arxiv.org/abs/1302.1541

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