Guess free maximization of submodular and linear sums

Moran Feldman

doi:10.1007/978-3-030-24766-9_28

Guess free maximization of submodular and linear sums

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

We consider the problem of maximizing the sum of a monotone submodular function and a linear function subject to a general solvable polytope constraint. Recently, Sviridenko et al. [16] described an algorithm for this problem whose approximation guarantee is optimal in some intuitive and formal senses. Unfortunately, this algorithm involves a guessing step which makes it less clean and significantly affects its time complexity. In this work we describe a clean alternative algorithm that uses a novel weighting technique in order to avoid the problematic guessing step while keeping the same approximation guarantee as the algorithm of [16].

Original language	English
Title of host publication	Algorithms and Data Structures - 16th International Symposium, WADS 2019, Proceedings
Editors	Zachary Friggstad, Mohammad R. Salavatipour, Jörg-Rüdiger Sack
Publisher	Springer Verlag
Pages	380-394
Number of pages	15
ISBN (Print)	9783030247652
DOIs	https://doi.org/10.1007/978-3-030-24766-9_28
State	Published - 2019
Externally published	Yes
Event	16th International Symposium on Algorithms and Data Structures, WADS 2019 - Edmonton, Canada Duration: 5 Aug 2019 → 7 Aug 2019

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11646 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	16th International Symposium on Algorithms and Data Structures, WADS 2019
Country/Territory	Canada
City	Edmonton
Period	5/08/19 → 7/08/19

Bibliographical note

Publisher Copyright:
© Springer Nature Switzerland AG 2019.

Keywords

Continuous greedy
Curvature
Submodular maximization

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-030-24766-9_28

Cite this

Feldman, M. (2019). Guess free maximization of submodular and linear sums. In Z. Friggstad, M. R. Salavatipour, & J.-R. Sack (Eds.), Algorithms and Data Structures - 16th International Symposium, WADS 2019, Proceedings (pp. 380-394). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11646 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-030-24766-9_28

Feldman, Moran. / Guess free maximization of submodular and linear sums. Algorithms and Data Structures - 16th International Symposium, WADS 2019, Proceedings. editor / Zachary Friggstad ; Mohammad R. Salavatipour ; Jörg-Rüdiger Sack. Springer Verlag, 2019. pp. 380-394 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "We consider the problem of maximizing the sum of a monotone submodular function and a linear function subject to a general solvable polytope constraint. Recently, Sviridenko et al. [16] described an algorithm for this problem whose approximation guarantee is optimal in some intuitive and formal senses. Unfortunately, this algorithm involves a guessing step which makes it less clean and significantly affects its time complexity. In this work we describe a clean alternative algorithm that uses a novel weighting technique in order to avoid the problematic guessing step while keeping the same approximation guarantee as the algorithm of [16].",

keywords = "Continuous greedy, Curvature, Submodular maximization",

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note = "Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2019.; 16th International Symposium on Algorithms and Data Structures, WADS 2019 ; Conference date: 05-08-2019 Through 07-08-2019",

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doi = "10.1007/978-3-030-24766-9\_28",

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Feldman, M 2019, Guess free maximization of submodular and linear sums. in Z Friggstad, MR Salavatipour & J-R Sack (eds), Algorithms and Data Structures - 16th International Symposium, WADS 2019, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11646 LNCS, Springer Verlag, pp. 380-394, 16th International Symposium on Algorithms and Data Structures, WADS 2019, Edmonton, Canada, 5/08/19. https://doi.org/10.1007/978-3-030-24766-9_28

Guess free maximization of submodular and linear sums. / Feldman, Moran.
Algorithms and Data Structures - 16th International Symposium, WADS 2019, Proceedings. ed. / Zachary Friggstad; Mohammad R. Salavatipour; Jörg-Rüdiger Sack. Springer Verlag, 2019. p. 380-394 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11646 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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Feldman M. Guess free maximization of submodular and linear sums. In Friggstad Z, Salavatipour MR, Sack JR, editors, Algorithms and Data Structures - 16th International Symposium, WADS 2019, Proceedings. Springer Verlag. 2019. p. 380-394. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-24766-9_28

Guess free maximization of submodular and linear sums

Abstract

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Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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