Amplification and Derandomization Without Slowdown
Abstract
We present techniques for decreasing the error probability of randomized algorithms and for converting randomized algorithms to deterministic (non-uniform) algorithms. Unlike most existing techniques that involve repetition of the randomized algorithm and hence a slowdown, our techniques produce algorithms with a similar run-time to the original randomized algorithms. The amplification technique is related to a certain stochastic multi-armed bandit problem. The derandomization technique - which is the main contribution of this work - points to an intriguing connection between derandomization and sketching/sparsification. We demonstrate the techniques by showing applications to Max-Cut on dense graphs, approximate clique on graphs that contain a large clique, constraint satisfaction problems on dense bipartite graphs and the list decoding to unique decoding problem for the Reed-Muller code.
Cite
@article{arxiv.1509.08123,
title = {Amplification and Derandomization Without Slowdown},
author = {Ofer Grossman and Dana Moshkovitz},
journal= {arXiv preprint arXiv:1509.08123},
year = {2015}
}