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Documents authored by de Givry, Simon

Document
DOI: 10.4230/LIPIcs.CP.2022.7
Parallel Hybrid Best-First Search

Authors: Abdelkader Beldjilali, Pierre Montalbano, David Allouche, George Katsirelos, and Simon de Givry

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

Abstract
While processor frequency has stagnated over the past two decades, the number of available cores in servers or clusters is still growing, offering the opportunity for significant speed-up in combinatorial optimization. Parallelization of exact methods remains a difficult challenge. We revisit the concept of parallel Branch-and-Bound in the framework of Cost Function Networks. We show how to adapt the anytime Hybrid Best-First Search algorithm in a Master-Worker protocol. The resulting parallel algorithm achieves good load-balancing without introducing new parameters to be tuned as is the case, for example, in Embarrassingly Parallel Search (EPS). It has also a small overhead due to its light communication messages. We performed an experimental evaluation on several benchmarks, comparing our parallel algorithm to its sequential version. We observed linear speed-up in some cases. Our approach compared favourably to the EPS approach and also to a state-of-the-art parallel exact integer programming solver.
Cite as

Abdelkader Beldjilali, Pierre Montalbano, David Allouche, George Katsirelos, and Simon de Givry. Parallel Hybrid Best-First Search. In 28th International Conference on Principles and Practice of Constraint Programming (CP 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 235, pp. 7:1-7:10, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)

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@InProceedings{beldjilali_et_al:LIPIcs.CP.2022.7,
  author =	{Beldjilali, Abdelkader and Montalbano, Pierre and Allouche, David and Katsirelos, George and de Givry, Simon},
  title =	{{Parallel Hybrid Best-First Search}},
  booktitle =	{28th International Conference on Principles and Practice of Constraint Programming (CP 2022)},
  pages =	{7:1--7:10},
  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.2022.7},
  URN =		{urn:nbn:de:0030-drops-166362},
  doi =		{10.4230/LIPIcs.CP.2022.7},
  annote =	{Keywords: Combinatorial Optimization, Parallel Branch-and-Bound, CFN}
}
Document
DOI: 10.4230/LIPIcs.CP.2022.37
Structured Set Variable Domains in Bayesian Network Structure Learning

Authors: Fulya Trösser, Simon de Givry, and George Katsirelos

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

Abstract
Constraint programming is a state of the art technique for learning the structure of Bayesian Networks from data (Bayesian Network Structure Learning - BNSL). However, scalability both for CP and other combinatorial optimization techniques for this problem is limited by the fact that the basic decision variables are set variables with domain sizes that may grow super polynomially with the number of random variables. Usual techniques for handling set variables in CP are not useful, as they lead to poor bounds. In this paper, we propose using decision trees as a data structure for storing sets of sets to represent set variable domains. We show that relatively simple operations are sufficient to implement all propagation and bounding algorithms, and that the use of these data structures improves scalability of a state of the art CP-based solver for BNSL.
Cite as

Fulya Trösser, Simon de Givry, and George Katsirelos. Structured Set Variable Domains in Bayesian Network Structure Learning. In 28th International Conference on Principles and Practice of Constraint Programming (CP 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 235, pp. 37:1-37:9, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)

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@InProceedings{trosser_et_al:LIPIcs.CP.2022.37,
  author =	{Tr\"{o}sser, Fulya and de Givry, Simon and Katsirelos, George},
  title =	{{Structured Set Variable Domains in Bayesian Network Structure Learning}},
  booktitle =	{28th International Conference on Principles and Practice of Constraint Programming (CP 2022)},
  pages =	{37:1--37:9},
  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.2022.37},
  URN =		{urn:nbn:de:0030-drops-166661},
  doi =		{10.4230/LIPIcs.CP.2022.37},
  annote =	{Keywords: Combinatorial Optimization, Bayesian Networks, Decision Trees}
}
Document
DOI: 10.4230/LIPIcs.CP.2021.23
Bounds on Weighted CSPs Using Constraint Propagation and Super-Reparametrizations

Authors: Tomáš Dlask, Tomáš Werner, and Simon de Givry

Published in: LIPIcs, Volume 210, 27th International Conference on Principles and Practice of Constraint Programming (CP 2021)

Abstract
We propose a framework for computing upper bounds on the optimal value of the (maximization version of) Weighted CSP (WCSP) using super-reparametrizations, which are changes of the weights that keep or increase the WCSP objective for every assignment. We show that it is in principle possible to employ arbitrary (under certain technical conditions) constraint propagation rules to improve the bound. For arc consistency in particular, the method reduces to the known Virtual AC (VAC) algorithm. Newly, we implemented the method for singleton arc consistency (SAC) and compared it to other strong local consistencies in WCSPs on a public benchmark. The results show that the bounds obtained from SAC are superior for many instance groups.
Cite as

Tomáš Dlask, Tomáš Werner, and Simon de Givry. Bounds on Weighted CSPs Using Constraint Propagation and Super-Reparametrizations. In 27th International Conference on Principles and Practice of Constraint Programming (CP 2021). Leibniz International Proceedings in Informatics (LIPIcs), Volume 210, pp. 23:1-23:18, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)

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@InProceedings{dlask_et_al:LIPIcs.CP.2021.23,
  author =	{Dlask, Tom\'{a}\v{s} and Werner, Tom\'{a}\v{s} and de Givry, Simon},
  title =	{{Bounds on Weighted CSPs Using Constraint Propagation and Super-Reparametrizations}},
  booktitle =	{27th International Conference on Principles and Practice of Constraint Programming (CP 2021)},
  pages =	{23:1--23:18},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-211-2},
  ISSN =	{1868-8969},
  year =	{2021},
  volume =	{210},
  editor =	{Michel, Laurent D.},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CP.2021.23},
  URN =		{urn:nbn:de:0030-drops-153149},
  doi =		{10.4230/LIPIcs.CP.2021.23},
  annote =	{Keywords: Weighted CSP, Super-Reparametrization, Linear Programming, Constraint Propagation}
}
Document
Tutorial
DOI: 10.4230/LIPIcs.STACS.2020.4
Graphical Models: Queries, Complexity, Algorithms (Tutorial)

Authors: Martin C. Cooper, Simon de Givry, and Thomas Schiex

Published in: LIPIcs, Volume 154, 37th International Symposium on Theoretical Aspects of Computer Science (STACS 2020)

Abstract
Graphical models (GMs) define a family of mathematical models aimed at the concise description of multivariate functions using decomposability. We restrict ourselves to functions of discrete variables but try to cover a variety of models that are not always considered as "Graphical Models", ranging from functions with Boolean variables and Boolean co-domain (used in automated reasoning) to functions over finite domain variables and integer or real co-domains (usual in machine learning and statistics). We use a simple algebraic semi-ring based framework for generality, define associated queries, relationships between graphical models, complexity results, and families of algorithms, with their associated guarantees.
Cite as

Martin C. Cooper, Simon de Givry, and Thomas Schiex. Graphical Models: Queries, Complexity, Algorithms (Tutorial). In 37th International Symposium on Theoretical Aspects of Computer Science (STACS 2020). Leibniz International Proceedings in Informatics (LIPIcs), Volume 154, pp. 4:1-4:22, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020)

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@InProceedings{cooper_et_al:LIPIcs.STACS.2020.4,
  author =	{Cooper, Martin C. and de Givry, Simon and Schiex, Thomas},
  title =	{{Graphical Models: Queries, Complexity, Algorithms}},
  booktitle =	{37th International Symposium on Theoretical Aspects of Computer Science (STACS 2020)},
  pages =	{4:1--4:22},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-140-5},
  ISSN =	{1868-8969},
  year =	{2020},
  volume =	{154},
  editor =	{Paul, Christophe and Bl\"{a}ser, Markus},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.STACS.2020.4},
  URN =		{urn:nbn:de:0030-drops-118654},
  doi =		{10.4230/LIPIcs.STACS.2020.4},
  annote =	{Keywords: Computational complexity, tree decomposition, graphical models, submodularity, message passing, local consistency, artificial intelligence, valued constraints, optimization}
}
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