2020arXiv (Cornell University)Open access

Iteratively reweighted greedy set cover

Marc Alexa

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Abstract

We empirically analyze a simple heuristic for large sparse set cover problems. It uses the weighted greedy algorithm as a basic building block. By multiplicative updates of the weights attached to the elements, the greedy solution is iteratively improved. The implementation of this algorithm is trivial and the algorithm is essentially free of parameters that would require tuning. More iterations can only improve the solution. This set of features makes the approach attractive for practical problems.

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What this paper is about

We empirically analyze a simple heuristic for large sparse set cover problems. It uses the weighted greedy algorithm as a basic building block. By multiplicative updates of the weights attached to the elements, the greedy solution is iteratively improved. The implementation of this algorithm is trivial and the algorithm is essentially free of parameters that would require tuning. More iterations can only improve the solution. This set of features makes the approach attractive for practical problems.

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Available abstract

We empirically analyze a simple heuristic for large sparse set cover problems. It uses the weighted greedy algorithm as a basic building block. By multiplicative updates of the weights attached to the elements, the greedy solution is iteratively improved. The implementation of this algorithm is trivial and the algorithm is essentially free of parameters that would require tuning. More iterations can only improve the solution. This set of features makes the approach attractive for practical problems.

Key concepts: Set cover problem, Greedy algorithm, Cover (algebra), Heuristic, Multiplicative function, Set (abstract data type), Simple (philosophy), Mathematical optimization

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