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This article introduces new algorithms for the uniform random generation of labelled planar graphs. Its principles rely on Boltzmann samplers, as recently developed by Duchon, Flajolet, Louchard, and Schaeffer. It combines the Boltzmann…

Combinatorics · Mathematics 2008-12-18 Eric Fusy

The Boltzmann model for the random generation of "decomposable" combinatorial structures is a set of techniques that allows for efficient random sampling algorithms for a large class of families of discrete objects. The usual requirement of…

Data Structures and Algorithms · Computer Science 2011-12-23 Philippe Duchon

Boltzmann samplers, introduced by Duchon et al. in 2001, make it possible to uniformly draw approximate size objects from any class which can be specified through the symbolic method. This, through by evaluating the associated generating…

Discrete Mathematics · Computer Science 2014-11-14 Olivier Bodini , Jérémie Lumbroso , Nicolas Rolin

The "necklace process", a procedure constructing necklaces of black and white beads by randomly choosing positions to insert new beads (whose color is uniquely determined based on the chosen location), is revisited. This article illustrates…

Probability · Mathematics 2018-07-25 Benjamin Hackl , Helmut Prodinger

In this paper, we give a general framework for the Boltzmann generation of colored objects belonging to combinatorial constructible classes. We propose an intuitive notion called profiled objects which allows the sampling of size-colored…

Discrete Mathematics · Computer Science 2009-11-17 Olivier Bodini , Alice Jacquot

Boltzmann sampling is commonly used to uniformly sample objects of a particular size from large combinatorial sets. For this technique to be effective, one needs to prove that (1) the sampling procedure is efficient and (2) objects of the…

Data Structures and Algorithms · Computer Science 2023-04-11 Megan Bernstein , Matthew Fahrbach , Dana Randall

Restricted Boltzmann Machines are simple and powerful generative models that can encode any complex dataset. Despite all their advantages, in practice the trainings are often unstable and it is difficult to assess their quality because the…

Machine Learning · Computer Science 2023-03-16 Nicolas Béreux , Aurélien Decelle , Cyril Furtlehner , Beatriz Seoane

Diffusion models have shown promising potential for advancing Boltzmann Generators. However, two critical challenges persist: (1) inherent errors in samples due to model imperfections, and (2) the requirement of hundreds of functional…

Machine Learning · Computer Science 2024-09-12 Fengzhe Zhang , Jiajun He , Laurence I. Midgley , Javier Antorán , José Miguel Hernández-Lobato

In some recent papers the classical `splitting necklace theorem' is linked in an interesting way with a geometric `pattern avoidance problem'. We explore the topological constraints on the existence of a (relaxed) measurable coloring of R^d…

Combinatorics · Mathematics 2013-06-03 Sinisa Vrecica , Rade Zivaljevic

The use of generative models to sample equilibrium distributions of many-body systems, as first demonstrated by Boltzmann Generators, has attracted substantial interest due to their ability to produce unbiased and uncorrelated samples in…

Statistical Mechanics · Physics 2025-10-23 Maximilian Schebek , Frank Noé , Jutta Rogal

We consider the distinct elements problem, where the goal is to estimate the number of distinct colors in an urn containing $ k $ balls based on $n$ samples drawn with replacements. Based on discrete polynomial approximation and…

Statistics Theory · Mathematics 2018-01-16 Yihong Wu , Pengkun Yang

Constrained decoding enables Language Models (LMs) to produce samples that provably satisfy hard constraints. However, existing constrained-decoding approaches often distort the underlying model distribution, a limitation that is especially…

Artificial Intelligence · Computer Science 2025-06-09 Emmanuel Anaya Gonzalez , Sairam Vaidya , Kanghee Park , Ruyi Ji , Taylor Berg-Kirkpatrick , Loris D'Antoni

Computing equilibrium states in condensed-matter many-body systems, such as solvated proteins, is a long-standing challenge. Lacking methods for generating statistically independent equilibrium samples in "one shot", vast computational…

Machine Learning · Statistics 2019-07-15 Frank Noé , Simon Olsson , Jonas Köhler , Hao Wu

A model named `Colored Percolation' has been introduced with its infinite number of versions in two dimensions. The sites of a regular lattice are randomly occupied with probability $p$ and are then colored by one of the $n$ distinct colors…

Statistical Mechanics · Physics 2017-09-13 Sumanta Kundu , S. S. Manna

Simple formulas for the number of different cyclic and dihedral necklaces containing $n_j$ beads of the $j$-th color, $j\leq m$ and $\sum_{j=1}^mn_j=N$, are derived.

Combinatorics · Mathematics 2007-05-23 Leonid G. Fel , Yoram Zimmels

Graded posets frequently arise throughout combinatorics, where it is natural to try to count the number of elements of a fixed rank. These counting problems are often $\#\textbf{P}$-complete, so we consider approximation algorithms for…

Data Structures and Algorithms · Computer Science 2023-04-11 Prateek Bhakta , Ben Cousins , Matthew Fahrbach , Dana Randall

Efficient sampling from the Boltzmann distribution given its energy function is a key challenge for modeling complex physical systems such as molecules. Boltzmann Generators address this problem by leveraging continuous normalizing flows to…

Machine Learning · Computer Science 2025-10-17 Rishal Aggarwal , Jacky Chen , Nicholas M. Boffi , David Ryan Koes

Invention involves combination, or more precisely, ratios of composition. According to Thomas Edison, "Genius is one percent inspiration and 99 percent perspiration" is an example. In many situations, researchers and inventors already have…

Machine Learning · Statistics 2019-07-01 Yachiko Obara , Tetsuro Morimura , Hiroki Yanagisawa

Scalable sampling of molecular states in thermodynamic equilibrium is a long-standing challenge in statistical physics. Boltzmann generators tackle this problem by pairing normalizing flows with importance sampling to obtain uncorrelated…

Machine Learning · Computer Science 2026-01-21 Charlie B. Tan , Avishek Joey Bose , Chen Lin , Leon Klein , Michael M. Bronstein , Alexander Tong

In this paper we present an efficient algorithm to produce a provably dense sample of a smooth compact variety. The procedure is partly based on computing $\textit{bottlenecks}$ of the variety. Using geometric information such as the…

Algebraic Geometry · Mathematics 2020-10-19 Sandra Di Rocco , David Eklund , Oliver Gäfvert
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