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Documents authored by Peng, Xiao

Document
DOI: 10.4230/LIPIcs.CP.2026.11
Solving the Multiple Constant Multiplication Problem with Constraint Programming

Authors: Théo Cantaloube, Xiao Peng, Christine Solnon, and Anastasia Volkova

Published in: LIPIcs, Volume 379, 32nd International Conference on Principles and Practice of Constraint Programming (CP 2026)

Abstract
The Multiple Constant Multiplication (MCM) problem arises in many applications such as, for example, digital signal processing or deep neural network inference. Given a set T of target constants, the goal of MCM is to find the most efficient way for multiplying an input number with every constant in T, where multiplications are realized through bit-shifts and additions, and where intermediate results may be shared to produce different target constants. In this paper, we first introduce a basic Constraint Programming (CP) model to solve MCM. Then, we introduce symmetry breaking rules and a global constraint to ensure them. We experimentally evaluate our approach on a widely used benchmark extracted from a collection of digital filter designs. We show that the basic CP model is competitive with state-of-the-art Integer Linear Programming (ILP) and SAT models, and that the addition of our global symmetry breaking constraint allows us to clearly outperform all other existing approaches on the considered benchmark.
Cite as

Théo Cantaloube, Xiao Peng, Christine Solnon, and Anastasia Volkova. Solving the Multiple Constant Multiplication Problem with Constraint Programming. In 32nd International Conference on Principles and Practice of Constraint Programming (CP 2026). Leibniz International Proceedings in Informatics (LIPIcs), Volume 379, pp. 11:1-11:19, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

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@InProceedings{cantaloube_et_al:LIPIcs.CP.2026.11,
  author =	{Cantaloube, Th\'{e}o and Peng, Xiao and Solnon, Christine and Volkova, Anastasia},
  title =	{{Solving the Multiple Constant Multiplication Problem with Constraint Programming}},
  booktitle =	{32nd International Conference on Principles and Practice of Constraint Programming (CP 2026)},
  pages =	{11:1--11:19},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-432-1},
  ISSN =	{1868-8969},
  year =	{2026},
  volume =	{379},
  editor =	{Beldiceanu, Nicolas},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CP.2026.11},
  URN =		{urn:nbn:de:0030-drops-266448},
  doi =		{10.4230/LIPIcs.CP.2026.11},
  annote =	{Keywords: Constraint Programming, Multiple Constant Multiplication, Hardware Optimization}
}
Document
DOI: 10.4230/LIPIcs.CP.2025.32
BFS-Based Canonical Codes for Generating Graphs with Constraint Programming

Authors: Xiao Peng and Christine Solnon

Published in: LIPIcs, Volume 340, 31st International Conference on Principles and Practice of Constraint Programming (CP 2025)

Abstract
We consider the problem of generating all graphs that satisfy some given additional constraints (on vertex degrees, or cycle lengths, for example). Most previous works have proposed to generate canonical codes associated with adjacency matrices. In this paper, we consider canonical codes based on Breadth First Search (BFS), and we show how to generate them with Constraint Programming (CP): we introduce a set of basic constraints that must be satisfied by all canonical codes, thus breaking many symmetries, and we introduce a global constraint to break other symmetries. We illustrate the interest of our approach on connected claw-free cubic graphs, and show that it outperforms state-of-the-art CP and SAT Modulo Theory (SMT) approaches.
Cite as

Xiao Peng and Christine Solnon. BFS-Based Canonical Codes for Generating Graphs with Constraint Programming. In 31st International Conference on Principles and Practice of Constraint Programming (CP 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 340, pp. 32:1-32:16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{peng_et_al:LIPIcs.CP.2025.32,
  author =	{Peng, Xiao and Solnon, Christine},
  title =	{{BFS-Based Canonical Codes for Generating Graphs with Constraint Programming}},
  booktitle =	{31st International Conference on Principles and Practice of Constraint Programming (CP 2025)},
  pages =	{32:1--32:16},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-380-5},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{340},
  editor =	{de la Banda, Maria Garcia},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CP.2025.32},
  URN =		{urn:nbn:de:0030-drops-238935},
  doi =		{10.4230/LIPIcs.CP.2025.32},
  annote =	{Keywords: Graph Generation, Automorphisms, Symmetry Breaking}
}
Document
DOI: 10.4230/LIPIcs.CP.2023.28
Using Canonical Codes to Efficiently Solve the Benzenoid Generation Problem with Constraint Programming

Authors: Xiao Peng and Christine Solnon

Published in: LIPIcs, Volume 280, 29th International Conference on Principles and Practice of Constraint Programming (CP 2023)

Abstract
The Benzenoid Generation Problem (BGP) aims at generating all benzenoid molecules that satisfy some given properties. This problem has important applications in chemistry, and Carissan et al (2021) have shown us that Constraint Programming (CP) is well suited for modelling this problem because properties defined by chemists are easy to express by means of constraints. Benzenoids are described by hexagon graphs and a key point for an efficient enumeration of these graphs is to be invariant to rotations and symmetries. In this paper, we introduce canonical codes that uniquely characterise hexagon graphs while being invariant to rotations and symmetries. We show that these codes may be defined by means of constraints. We also introduce a global constraint for ensuring that codes are canonical, and a global constraint for ensuring that a pattern is included in a code. We experimentally compare our new CP model with the CP-based approach of Carissan et al (2021), and we show that it has better scale-up properties.
Cite as

Xiao Peng and Christine Solnon. Using Canonical Codes to Efficiently Solve the Benzenoid Generation Problem with Constraint Programming. In 29th International Conference on Principles and Practice of Constraint Programming (CP 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 280, pp. 28:1-28:17, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

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@InProceedings{peng_et_al:LIPIcs.CP.2023.28,
  author =	{Peng, Xiao and Solnon, Christine},
  title =	{{Using Canonical Codes to Efficiently Solve the Benzenoid Generation Problem with Constraint Programming}},
  booktitle =	{29th International Conference on Principles and Practice of Constraint Programming (CP 2023)},
  pages =	{28:1--28:17},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-300-3},
  ISSN =	{1868-8969},
  year =	{2023},
  volume =	{280},
  editor =	{Yap, Roland H. C.},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CP.2023.28},
  URN =		{urn:nbn:de:0030-drops-190650},
  doi =		{10.4230/LIPIcs.CP.2023.28},
  annote =	{Keywords: Benzenoid Generation Problem, Canonical Code, Hexagon Graph}
}
Document
DOI: 10.4230/LIPIcs.CP.2021.45
Solving the Non-Crossing MAPF with CP

Authors: Xiao Peng, Christine Solnon, and Olivier Simonin

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

Abstract
We introduce a new Multi-Agent Path Finding (MAPF) problem which is motivated by an industrial application. Given a fleet of robots that move on a workspace that may contain static obstacles, we must find paths from their current positions to a set of destinations, and the goal is to minimise the length of the longest path. The originality of our problem comes from the fact that each robot is attached with a cable to an anchor point, and that robots are not able to cross these cables. We formally define the Non-Crossing MAPF (NC-MAPF) problem and show how to compute lower and upper bounds by solving well known assignment problems. We introduce a Variable Neighbourhood Search (VNS) approach for improving the upper bound, and a Constraint Programming (CP) model for solving the problem to optimality. We experimentally evaluate these approaches on randomly generated instances.
Cite as

Xiao Peng, Christine Solnon, and Olivier Simonin. Solving the Non-Crossing MAPF with CP. In 27th International Conference on Principles and Practice of Constraint Programming (CP 2021). Leibniz International Proceedings in Informatics (LIPIcs), Volume 210, pp. 45:1-45:16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)

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@InProceedings{peng_et_al:LIPIcs.CP.2021.45,
  author =	{Peng, Xiao and Solnon, Christine and Simonin, Olivier},
  title =	{{Solving the Non-Crossing MAPF with CP}},
  booktitle =	{27th International Conference on Principles and Practice of Constraint Programming (CP 2021)},
  pages =	{45:1--45:16},
  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.2021.45},
  URN =		{urn:nbn:de:0030-drops-153367},
  doi =		{10.4230/LIPIcs.CP.2021.45},
  annote =	{Keywords: Constraint Programming (CP), Multi-Agent Path Finding (MAPF), Assignment Problems}
}
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