3 Search Results for "Rudich, Isaac"

Document
DOI: 10.4230/LIPIcs.CP.2024.21
Strengthening Relaxed Decision Diagrams for Maximum Independent Set Problem: Novel Variable Ordering and Merge Heuristics

Authors: Mohsen Nafar and Michael Römer

Published in: LIPIcs, Volume 307, 30th International Conference on Principles and Practice of Constraint Programming (CP 2024)

Abstract
Finding high-quality bounds is key to devising efficient exact solution approaches for Discrete Optimization (DO) problems. To this end, Decision Diagrams (DDs) provide strong and generic bounding mechanisms. This paper focuses on so-called relaxed DDs which, by merging nodes, over-approximate the solution space of DO problems and provide dual bounds the quality of which hinges upon the ordering of the variables in the DD compilation and on the selection of the nodes to merge. Addressing the Maximum Independent Set Problem, we present a novel dynamic variable ordering strategy relying on induced subgraphs of the original graph, and a new tie-based merge heuristic. In a set of computational experiments, we show that our strategies yield much stronger bounds than the standard state-of-the-art approaches. Furthermore, implementing our heuristics in a DD-based branch-and-bound, we reduce the solution times by around 33 % on average and by more than 50 % on hard instances.
Cite as

Mohsen Nafar and Michael Römer. Strengthening Relaxed Decision Diagrams for Maximum Independent Set Problem: Novel Variable Ordering and Merge Heuristics. In 30th International Conference on Principles and Practice of Constraint Programming (CP 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 307, pp. 21:1-21:17, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{nafar_et_al:LIPIcs.CP.2024.21,
  author =	{Nafar, Mohsen and R\"{o}mer, Michael},
  title =	{{Strengthening Relaxed Decision Diagrams for Maximum Independent Set Problem: Novel Variable Ordering and Merge Heuristics}},
  booktitle =	{30th International Conference on Principles and Practice of Constraint Programming (CP 2024)},
  pages =	{21:1--21:17},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-336-2},
  ISSN =	{1868-8969},
  year =	{2024},
  volume =	{307},
  editor =	{Shaw, Paul},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CP.2024.21},
  URN =		{urn:nbn:de:0030-drops-207069},
  doi =		{10.4230/LIPIcs.CP.2024.21},
  annote =	{Keywords: Decision Diagram, Dynamic Programming, Maximum Independent Set Problem, Dual Bound}
}
Document
Track B: Automata, Logic, Semantics, and Theory of Programming
DOI: 10.4230/LIPIcs.ICALP.2024.147
Smoothed Analysis of Deterministic Discounted and Mean-Payoff Games

Authors: Bruno Loff and Mateusz Skomra

Published in: LIPIcs, Volume 297, 51st International Colloquium on Automata, Languages, and Programming (ICALP 2024)

Abstract
We devise a policy-iteration algorithm for deterministic two-player discounted and mean-payoff games, that runs in polynomial time with high probability, on any input where each payoff is chosen independently from a sufficiently random distribution and the underlying graph of the game is ergodic. This includes the case where an arbitrary set of payoffs has been perturbed by a Gaussian, showing for the first time that deterministic two-player games can be solved efficiently, in the sense of smoothed analysis. More generally, we devise a condition number for deterministic discounted and mean-payoff games played on ergodic graphs, and show that our algorithm runs in time polynomial in this condition number. Our result confirms a previous conjecture of Boros et al., which was claimed as a theorem [Boros et al., 2011] and later retracted [Boros et al., 2018]. It stands in contrast with a recent counter-example by Christ and Yannakakis [Christ and Yannakakis, 2023], showing that Howard’s policy-iteration algorithm does not run in smoothed polynomial time on stochastic single-player mean-payoff games. Our approach is inspired by the analysis of random optimal assignment instances by Frieze and Sorkin [Frieze and Sorkin, 2007], and the analysis of bias-induced policies for mean-payoff games by Akian, Gaubert and Hochart [Akian et al., 2018].
Cite as

Bruno Loff and Mateusz Skomra. Smoothed Analysis of Deterministic Discounted and Mean-Payoff Games. In 51st International Colloquium on Automata, Languages, and Programming (ICALP 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 297, pp. 147:1-147:16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{loff_et_al:LIPIcs.ICALP.2024.147,
  author =	{Loff, Bruno and Skomra, Mateusz},
  title =	{{Smoothed Analysis of Deterministic Discounted and Mean-Payoff Games}},
  booktitle =	{51st International Colloquium on Automata, Languages, and Programming (ICALP 2024)},
  pages =	{147:1--147:16},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-322-5},
  ISSN =	{1868-8969},
  year =	{2024},
  volume =	{297},
  editor =	{Bringmann, Karl and Grohe, Martin and Puppis, Gabriele and Svensson, Ola},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ICALP.2024.147},
  URN =		{urn:nbn:de:0030-drops-202908},
  doi =		{10.4230/LIPIcs.ICALP.2024.147},
  annote =	{Keywords: Mean-payoff games, discounted games, policy iteration, smoothed analysis}
}
Document
DOI: 10.4230/LIPIcs.CP.2022.35
Peel-And-Bound: Generating Stronger Relaxed Bounds with Multivalued Decision Diagrams

Authors: Isaac Rudich, Quentin Cappart, and Louis-Martin Rousseau

Published in: LIPIcs, Volume 235, 28th International Conference on Principles and Practice of Constraint Programming (CP 2022)

Abstract
Decision diagrams are an increasingly important tool in cutting-edge solvers for discrete optimization. However, the field of decision diagrams is relatively new, and is still incorporating the library of techniques that conventional solvers have had decades to build. We drew inspiration from the warm-start technique used in conventional solvers to address one of the major challenges faced by decision diagram based methods. Decision diagrams become more useful the wider they are allowed to be, but also become more costly to generate, especially with large numbers of variables. We present a method of peeling off a sub-graph of previously constructed diagrams and using it as the initial diagram for subsequent iterations that we call peel-and-bound. We test the method on the sequence ordering problem, and our results indicate that our peel-and-bound scheme generates stronger bounds than a branch-and-bound scheme using the same propagators, and at significantly less computational cost.
Cite as

Isaac Rudich, Quentin Cappart, and Louis-Martin Rousseau. Peel-And-Bound: Generating Stronger Relaxed Bounds with Multivalued Decision Diagrams. In 28th International Conference on Principles and Practice of Constraint Programming (CP 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 235, pp. 35:1-35:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)

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@InProceedings{rudich_et_al:LIPIcs.CP.2022.35,
  author =	{Rudich, Isaac and Cappart, Quentin and Rousseau, Louis-Martin},
  title =	{{Peel-And-Bound: Generating Stronger Relaxed Bounds with Multivalued Decision Diagrams}},
  booktitle =	{28th International Conference on Principles and Practice of Constraint Programming (CP 2022)},
  pages =	{35:1--35:20},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-240-2},
  ISSN =	{1868-8969},
  year =	{2022},
  volume =	{235},
  editor =	{Solnon, Christine},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CP.2022.35},
  URN =		{urn:nbn:de:0030-drops-166647},
  doi =		{10.4230/LIPIcs.CP.2022.35},
  annote =	{Keywords: decision diagrams, discrete optimization, branch-and-bound, sequencing, constraint programming}
}
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