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Submodular Maximization Subject to Matroid Intersection on the Fly

Authors Moran Feldman, Ashkan Norouzi-Fard, Ola Svensson, Rico Zenklusen

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Author Details

Moran Feldman
  • University of Haifa, Israel
Ashkan Norouzi-Fard
  • Google Research, Zürich, Switzerland
Ola Svensson
  • EPFL, Lausanne, Switzerland
Rico Zenklusen
  • ETH Zürich, Switzerland

Funding

  • Feldman, Moran: Research supported in part by the Israel Science Foundation (ISF) grant no. 459/20.
  • Svensson, Ola: Research supported by the Swiss National Science Foundation project 200021-184656 "Randomness in Problem Instances and Randomized Algorithms.
  • Zenklusen, Rico: Research supported in part by Swiss National Science Foundation grant number 200021_184622. This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 817750).

Cite AsGet BibTex

Moran Feldman, Ashkan Norouzi-Fard, Ola Svensson, and Rico Zenklusen. Submodular Maximization Subject to Matroid Intersection on the Fly. In 30th Annual European Symposium on Algorithms (ESA 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 244, pp. 52:1-52:14, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)
https://doi.org/10.4230/LIPIcs.ESA.2022.52

Abstract

Despite a surge of interest in submodular maximization in the data stream model, there remain significant gaps in our knowledge about what can be achieved in this setting, especially when dealing with multiple constraints. In this work, we nearly close several basic gaps in submodular maximization subject to k matroid constraints in the data stream model. We present a new hardness result showing that super polynomial memory in k is needed to obtain an o(k/(log k))-approximation. This implies near optimality of prior algorithms. For the same setting, we show that one can nevertheless obtain a constant-factor approximation by maintaining a set of elements whose size is independent of the stream size. Finally, for bipartite matching constraints, a well-known special case of matroid intersection, we present a new technique to obtain hardness bounds that are significantly stronger than those obtained with prior approaches. Prior results left it open whether a 2-approximation may exist in this setting, and only a complexity-theoretic hardness of 1.91 was known. We prove an unconditional hardness of 2.69.

Subject Classification

ACM Subject Classification
  • Theory of computation → Communication complexity
Keywords
  • Submodular Maximization
  • Matroid Intersection
  • Streaming Algorithms

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References

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