Submodular maximization subject to a knapsack constraint: Combinatorial algorithms with near-optimal adaptive complexity

Submodular maximization is a classic algorithmic problem with multiple applications in data mining and machine learning; there, the growing need to deal with massive instances motivates the design of algorithms balancing the quality of the solution with applicability. For the latter, an important me...

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Published in:Theoretical computer science Vol. 1060; p. 115629
Main Authors: Amanatidis, Georgios, Fusco, Federico, Lazos, Philip, Leonardi, Stefano, Marchetti-Spaccamela, Alberto, Reiffenhäuser, Rebecca
Format: Journal Article
Language:English
Published: Elsevier B.V 18.01.2026
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ISSN:0304-3975
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Abstract Submodular maximization is a classic algorithmic problem with multiple applications in data mining and machine learning; there, the growing need to deal with massive instances motivates the design of algorithms balancing the quality of the solution with applicability. For the latter, an important measure is the adaptive complexity, which captures the number of sequential rounds of parallel computation needed by an algorithm to terminate. In this work, we obtain the first constant factor approximation algorithm for non-monotone submodular maximization subject to a knapsack constraint with near-optimal O(log n) adaptive complexity. Low adaptivity by itself, however, is not enough: a crucial feature to account for is represented by the total number of function evaluations (or value queries). Our algorithm asks O˜(n2) value queries but can be modified to run with only O˜(n), while retaining a low adaptive complexity of O(log2n). Besides the above improvement in adaptivity, this is also the first combinatorial approach with sublinear adaptive complexity for the problem and yields algorithms comparable to the state-of-the-art even for the special cases of cardinality constraints or monotone objectives.
AbstractList Submodular maximization is a classic algorithmic problem with multiple applications in data mining and machine learning; there, the growing need to deal with massive instances motivates the design of algorithms balancing the quality of the solution with applicability. For the latter, an important measure is the adaptive complexity, which captures the number of sequential rounds of parallel computation needed by an algorithm to terminate. In this work, we obtain the first constant factor approximation algorithm for non-monotone submodular maximization subject to a knapsack constraint with near-optimal O(log n) adaptive complexity. Low adaptivity by itself, however, is not enough: a crucial feature to account for is represented by the total number of function evaluations (or value queries). Our algorithm asks O˜(n2) value queries but can be modified to run with only O˜(n), while retaining a low adaptive complexity of O(log2n). Besides the above improvement in adaptivity, this is also the first combinatorial approach with sublinear adaptive complexity for the problem and yields algorithms comparable to the state-of-the-art even for the special cases of cardinality constraints or monotone objectives.
ArticleNumber 115629
Author Leonardi, Stefano
Amanatidis, Georgios
Reiffenhäuser, Rebecca
Fusco, Federico
Lazos, Philip
Marchetti-Spaccamela, Alberto
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  surname: Fusco
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  email: r.e.m.reiffenhauser@uva.nl
  organization: Institute for Logic, Language and Computation, University of Amsterdam, The Netherlands
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Keywords knapsack
adaptive complexity
submodular maximization
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Snippet Submodular maximization is a classic algorithmic problem with multiple applications in data mining and machine learning; there, the growing need to deal with...
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SubjectTerms adaptive complexity
knapsack
submodular maximization
Title Submodular maximization subject to a knapsack constraint: Combinatorial algorithms with near-optimal adaptive complexity
URI https://dx.doi.org/10.1016/j.tcs.2025.115629
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