Abstract
In this paper, the monotone submodular maximization problem (SM) is studied. SM is to find a subset of size ? from a universe of size n that maximizes a monotone submodular objective function f. We show using a novel analysis that the Pareto optimization algorithm achieves a worst-case ratio of (1 - e)(1 - 1/e) in expectation for every cardinality constraint ?
Cite
CITATION STYLE
APA
Crawford, V. G. (2021). Faster Guarantees of Evolutionary Algorithms for Maximization of Monotone Submodular Functions. In IJCAI International Joint Conference on Artificial Intelligence (pp. 1661–1667). International Joint Conferences on Artificial Intelligence. https://doi.org/10.24963/ijcai.2021/229
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