2 Search Results for "Knaack, Martin"

Document
DOI: 10.4230/LIPIcs.ESA.2025.92
Incremental Maximization for a Broad Class of Objectives

Authors: Yann Disser and David Weckbecker

Published in: LIPIcs, Volume 351, 33rd Annual European Symposium on Algorithms (ESA 2025)

Abstract
We consider incremental maximization problems, where the solution has to be built up gradually by adding elements one after the other. In every step, the incremental solution must be competitive, compared against the optimum solution of the current cardinality. We prove that a competitive solution always exists when the objective function is monotone and β-accountable, by providing a scaling algorithm that guarantees a constant competitive ratio. This generalizes known results and, importantly, yields the first competitive algorithm for the natural class of monotone and subadditive objective functions.
Cite as

Yann Disser and David Weckbecker. Incremental Maximization for a Broad Class of Objectives. In 33rd Annual European Symposium on Algorithms (ESA 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 351, pp. 92:1-92:13, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{disser_et_al:LIPIcs.ESA.2025.92,
  author =	{Disser, Yann and Weckbecker, David},
  title =	{{Incremental Maximization for a Broad Class of Objectives}},
  booktitle =	{33rd Annual European Symposium on Algorithms (ESA 2025)},
  pages =	{92:1--92:13},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-395-9},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{351},
  editor =	{Benoit, Anne and Kaplan, Haim and Wild, Sebastian and Herman, Grzegorz},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ESA.2025.92},
  URN =		{urn:nbn:de:0030-drops-245613},
  doi =		{10.4230/LIPIcs.ESA.2025.92},
  annote =	{Keywords: incremental maximization, competitive analysis, subadditive functions}
}
Document
APPROX
DOI: 10.4230/LIPIcs.APPROX/RANDOM.2022.49
Maximizing a Submodular Function with Bounded Curvature Under an Unknown Knapsack Constraint

Authors: Max Klimm and Martin Knaack

Published in: LIPIcs, Volume 245, Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2022)

Abstract
This paper studies the problem of maximizing a monotone submodular function under an unknown knapsack constraint. A solution to this problem is a policy that decides which item to pack next based on the past packing history. The robustness factor of a policy is the worst case ratio of the solution obtained by following the policy and an optimal solution that knows the knapsack capacity. We develop an algorithm with a robustness factor that is decreasing in the curvature c of the submodular function. For the extreme cases c = 0 corresponding to a modular objective, it matches a previously known and best possible robustness factor of 1/2. For the other extreme case of c = 1 it yields a robustness factor of ≈ 0.35 improving over the best previously known robustness factor of ≈ 0.06.
Cite as

Max Klimm and Martin Knaack. Maximizing a Submodular Function with Bounded Curvature Under an Unknown Knapsack Constraint. In Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 245, pp. 49:1-49:19, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)

Copy BibTex To Clipboard

@InProceedings{klimm_et_al:LIPIcs.APPROX/RANDOM.2022.49,
  author =	{Klimm, Max and Knaack, Martin},
  title =	{{Maximizing a Submodular Function with Bounded Curvature Under an Unknown Knapsack Constraint}},
  booktitle =	{Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2022)},
  pages =	{49:1--49:19},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-249-5},
  ISSN =	{1868-8969},
  year =	{2022},
  volume =	{245},
  editor =	{Chakrabarti, Amit and Swamy, Chaitanya},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.APPROX/RANDOM.2022.49},
  URN =		{urn:nbn:de:0030-drops-171711},
  doi =		{10.4230/LIPIcs.APPROX/RANDOM.2022.49},
  annote =	{Keywords: submodular function, knapsack, approximation algorithm, robust optimization}
}
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