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Deterministic Algorithms for Submodular Maximization Problems by Niv Buchbinder; Moran Feldman is a Computer Science article available to read on EtoBox.

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Randomization is a fundamental tool used in many theoretical and practical areas of computer science. We study here the role of randomization in the area of submodular function maximization. In this area, most algorithms are randomized, and in almost all cases the approximation ratios obtained by current randomized algorithms are superior to the best results obtained by known deterministic algorithms. Derandomization of algorithms for general submodular function maximization seems hard since the access to the function is done via a value oracle. This makes it hard, for example, to apply standard derandomization techniques such as conditional expectations. Therefore, an interesting fundamental problem in this area is whether randomization is inherently necessary for obtaining good approximation ratios. In this work, we give evidence that randomization is not necessary for obtaining good algorithms by presenting a new technique for derandomization of algorithms for submodular function maximization. Our high level idea is to maintain explicitly a (small) distribution over the states of the algorithm, and carefully update it using marginal values obtained from an extreme point solution

Who reads Deterministic Algorithms for Submodular Maximization Problems?

Author

Niv Buchbinder; Moran Feldman

Publisher

Association for Computing Machinery; Association for Computing Machinery (ACM) (ISSN 1549-6325)

Published

2018

Language

EN

Field

Computer Science (Physical Sciences)