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Documents authored by Pappik, Marcus

Document
RANDOM
DOI: 10.4230/LIPIcs.APPROX/RANDOM.2024.56
Fast and Slow Mixing of the Kawasaki Dynamics on Bounded-Degree Graphs

Authors: Aiya Kuchukova, Marcus Pappik, Will Perkins, and Corrine Yap

Published in: LIPIcs, Volume 317, Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2024)

Abstract
We study the worst-case mixing time of the global Kawasaki dynamics for the fixed-magnetization Ising model on the class of graphs of maximum degree Δ. Proving a conjecture of Carlson, Davies, Kolla, and Perkins, we show that below the tree uniqueness threshold, the Kawasaki dynamics mix rapidly for all magnetizations. Disproving a conjecture of Carlson, Davies, Kolla, and Perkins, we show that the regime of fast mixing does not extend throughout the regime of tractability for this model: there is a range of parameters for which there exist efficient sampling algorithms for the fixed-magnetization Ising model on max-degree Δ graphs, but the Kawasaki dynamics can take exponential time to mix. Our techniques involve showing spectral independence in the fixed-magnetization Ising model and proving a sharp threshold for the existence of multiple metastable states in the Ising model with external field on random regular graphs.
Cite as

Aiya Kuchukova, Marcus Pappik, Will Perkins, and Corrine Yap. Fast and Slow Mixing of the Kawasaki Dynamics on Bounded-Degree Graphs. In Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 317, pp. 56:1-56:24, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{kuchukova_et_al:LIPIcs.APPROX/RANDOM.2024.56,
  author =	{Kuchukova, Aiya and Pappik, Marcus and Perkins, Will and Yap, Corrine},
  title =	{{Fast and Slow Mixing of the Kawasaki Dynamics on Bounded-Degree Graphs}},
  booktitle =	{Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2024)},
  pages =	{56:1--56:24},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-348-5},
  ISSN =	{1868-8969},
  year =	{2024},
  volume =	{317},
  editor =	{Kumar, Amit and Ron-Zewi, Noga},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.APPROX/RANDOM.2024.56},
  URN =		{urn:nbn:de:0030-drops-210493},
  doi =		{10.4230/LIPIcs.APPROX/RANDOM.2024.56},
  annote =	{Keywords: ferromagnetic Ising model, fixed-magnetization Ising model, Kawasaki dynamics, Glauber dynamics, mixing time}
}
Document
RANDOM
DOI: 10.4230/LIPIcs.APPROX/RANDOM.2023.38
Perfect Sampling for Hard Spheres from Strong Spatial Mixing

Authors: Konrad Anand, Andreas Göbel, Marcus Pappik, and Will Perkins

Published in: LIPIcs, Volume 275, Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2023)

Abstract
We provide a perfect sampling algorithm for the hard-sphere model on subsets of R^d with expected running time linear in the volume under the assumption of strong spatial mixing. A large number of perfect and approximate sampling algorithms have been devised to sample from the hard-sphere model, and our perfect sampling algorithm is efficient for a range of parameters for which only efficient approximate samplers were previously known and is faster than these known approximate approaches. Our methods also extend to the more general setting of Gibbs point processes interacting via finite-range, repulsive potentials.
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Konrad Anand, Andreas Göbel, Marcus Pappik, and Will Perkins. Perfect Sampling for Hard Spheres from Strong Spatial Mixing. In Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 275, pp. 38:1-38:18, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

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@InProceedings{anand_et_al:LIPIcs.APPROX/RANDOM.2023.38,
  author =	{Anand, Konrad and G\"{o}bel, Andreas and Pappik, Marcus and Perkins, Will},
  title =	{{Perfect Sampling for Hard Spheres from Strong Spatial Mixing}},
  booktitle =	{Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2023)},
  pages =	{38:1--38:18},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-296-9},
  ISSN =	{1868-8969},
  year =	{2023},
  volume =	{275},
  editor =	{Megow, Nicole and Smith, Adam},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.APPROX/RANDOM.2023.38},
  URN =		{urn:nbn:de:0030-drops-188638},
  doi =		{10.4230/LIPIcs.APPROX/RANDOM.2023.38},
  annote =	{Keywords: perfect sampling, hard-sphere model, Gibbs point processes}
}
Document
DOI: 10.4230/DagRep.12.11.124
Counting and Sampling: Algorithms and Complexity (Dagstuhl Seminar 22482)

Authors: Holger Dell, Mark R. Jerrum, Haiko Müller, Konrad Anand, and Marcus Pappik

Published in: Dagstuhl Reports, Volume 12, Issue 11 (2023)

Abstract
This report documents the program and the outcomes of Dagstuhl Seminar 22482 "Counting and Sampling: Algorithms and Complexity". We document the talks presented, covering many advances in the area made over the last five years. As well, we document the progress made by working groups on future projects.
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Holger Dell, Mark R. Jerrum, Haiko Müller, Konrad Anand, and Marcus Pappik. Counting and Sampling: Algorithms and Complexity (Dagstuhl Seminar 22482). In Dagstuhl Reports, Volume 12, Issue 11, pp. 124-145, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

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@Article{dell_et_al:DagRep.12.11.124,
  author =	{Dell, Holger and Jerrum, Mark R. and M\"{u}ller, Haiko and Anand, Konrad and Pappik, Marcus},
  title =	{{Counting and Sampling: Algorithms and Complexity (Dagstuhl Seminar 22482)}},
  pages =	{124--145},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2023},
  volume =	{12},
  number =	{11},
  editor =	{Dell, Holger and Jerrum, Mark R. and M\"{u}ller, Haiko and Anand, Konrad and Pappik, Marcus},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagRep.12.11.124},
  URN =		{urn:nbn:de:0030-drops-178394},
  doi =		{10.4230/DagRep.12.11.124},
  annote =	{Keywords: Sampling, Counting, Algorithms, Complexity, Statistical Physics, Phase Transitions, Markov Chains, Graphs, Point Processes}
}
Document
Track A: Algorithms, Complexity and Games
DOI: 10.4230/LIPIcs.ICALP.2021.66
A Spectral Independence View on Hard Spheres via Block Dynamics

Authors: Tobias Friedrich, Andreas Göbel, Martin S. Krejca, and Marcus Pappik

Published in: LIPIcs, Volume 198, 48th International Colloquium on Automata, Languages, and Programming (ICALP 2021)

Abstract
The hard-sphere model is one of the most extensively studied models in statistical physics. It describes the continuous distribution of spherical particles, governed by hard-core interactions. An important quantity of this model is the normalizing factor of this distribution, called the partition function. We propose a Markov chain Monte Carlo algorithm for approximating the grand-canonical partition function of the hard-sphere model in d dimensions. Up to a fugacity of λ < e/2^d, the runtime of our algorithm is polynomial in the volume of the system. This covers the entire known real-valued regime for the uniqueness of the Gibbs measure. Key to our approach is to define a discretization that closely approximates the partition function of the continuous model. This results in a discrete hard-core instance that is exponential in the size of the initial hard-sphere model. Our approximation bound follows directly from the correlation decay threshold of an infinite regular tree with degree equal to the maximum degree of our discretization. To cope with the exponential blow-up of the discrete instance we use clique dynamics, a Markov chain that was recently introduced in the setting of abstract polymer models. We prove rapid mixing of clique dynamics up to the tree threshold of the univariate hard-core model. This is achieved by relating clique dynamics to block dynamics and adapting the spectral expansion method, which was recently used to bound the mixing time of Glauber dynamics within the same parameter regime.
Cite as

Tobias Friedrich, Andreas Göbel, Martin S. Krejca, and Marcus Pappik. A Spectral Independence View on Hard Spheres via Block Dynamics. In 48th International Colloquium on Automata, Languages, and Programming (ICALP 2021). Leibniz International Proceedings in Informatics (LIPIcs), Volume 198, pp. 66:1-66:15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)

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@InProceedings{friedrich_et_al:LIPIcs.ICALP.2021.66,
  author =	{Friedrich, Tobias and G\"{o}bel, Andreas and Krejca, Martin S. and Pappik, Marcus},
  title =	{{A Spectral Independence View on Hard Spheres via Block Dynamics}},
  booktitle =	{48th International Colloquium on Automata, Languages, and Programming (ICALP 2021)},
  pages =	{66:1--66:15},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-195-5},
  ISSN =	{1868-8969},
  year =	{2021},
  volume =	{198},
  editor =	{Bansal, Nikhil and Merelli, Emanuela and Worrell, James},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ICALP.2021.66},
  URN =		{urn:nbn:de:0030-drops-141353},
  doi =		{10.4230/LIPIcs.ICALP.2021.66},
  annote =	{Keywords: Hard-sphere Model, Markov Chain, Partition Function, Gibbs Distribution, Approximate Counting, Spectral Independence}
}
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