ML Anthology

Pareto Optimization for Subset Selection with Dynamic Partition Matroid Constraints

Anh Viet Do, Frank Neumann
AAAI 2021 pp. 12284-12292
doi:10.1609/AAAI.V35I14.17458 /aaai/2021/do2021aaai-pareto/

Abstract

In this study, we consider the subset selection problems with submodular or monotone discrete objective functions under partition matroid constraints where the thresholds are dynamic. We focus on POMC, a simple Pareto optimization approach that has been shown to be effective on such problems. Our analysis departs from singular constraint problems and extends to problems of multiple constraints. We show that previous results of POMC's performance also hold for multiple constraints. Our experimental investigations on random undirected maxcut problems demonstrate POMC's competitiveness against the classical GREEDY algorithm with restart strategy.

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Cite

Text

Do and Neumann. "Pareto Optimization for Subset Selection with Dynamic Partition Matroid Constraints." AAAI Conference on Artificial Intelligence, 2021. doi:10.1609/AAAI.V35I14.17458

Markdown

[Do and Neumann. "Pareto Optimization for Subset Selection with Dynamic Partition Matroid Constraints." AAAI Conference on Artificial Intelligence, 2021.](https://mlanthology.org/aaai/2021/do2021aaai-pareto/) doi:10.1609/AAAI.V35I14.17458

BibTeX

@inproceedings{do2021aaai-pareto,
  title     = {{Pareto Optimization for Subset Selection with Dynamic Partition Matroid Constraints}},
  author    = {Do, Anh Viet and Neumann, Frank},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2021},
  pages     = {12284-12292},
  doi       = {10.1609/AAAI.V35I14.17458},
  url       = {https://mlanthology.org/aaai/2021/do2021aaai-pareto/}
}