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Since the early 2000s physicists have developed an ingenious but non-rigorous formalism called the cavity method to put forward precise conjectures on phase transitions in random problems [Mezard, Parisi, Zecchina: Science 2002]. The cavity…

Combinatorics · Mathematics 2018-11-02 Amin Coja-Oghlan , Konstantinos Panagiotou

Phase transition is an important feature of SAT problem. For random k-SAT model, it is proved that as r (ratio of clauses to variables) increases, the structure of solutions will undergo a sudden change like satisfiability phase transition…

Artificial Intelligence · Computer Science 2007-05-23 Ke Xu , Wei Li

We introduce an efficient message passing scheme for solving Constraint Satisfaction Problems (CSPs), which uses stochastic perturbation of Belief Propagation (BP) and Survey Propagation (SP) messages to bypass decimation and directly…

Artificial Intelligence · Computer Science 2016-01-05 Siamak Ravanbakhsh , Russell Greiner

This talk surveys the recent development of message passing procedures for solving constraint satisfaction problems. The cavity method from statistical physics provides a generalization of the belief propagation strategy that is able to…

Disordered Systems and Neural Networks · Physics 2007-05-23 Marc Mezard

This paper is concerned with the complex behavior arising in satisfiability problems. We present a new statistical physics-based characterization of the satisfiability problem. Specifically, we design an algorithm that is able to produce…

Data Structures and Algorithms · Computer Science 2013-04-04 Claudio Angione , Annalisa Occhipinti , Giovanni Stracquadanio , Giuseppe Nicosia

Recent work has made substantial progress in understanding the transitions of random constraint satisfaction problems. In particular, for several of these models, the exact satisfiability threshold has been rigorously determined, confirming…

Probability · Mathematics 2023-11-09 Allan Sly , Nike Sun , Yumeng Zhang

In this paper we study biased random K-SAT problems in which each logical variable is negated with probability $p$. This generalization provides us a crossover from easy to hard problems and would help us in a better understanding of the…

Disordered Systems and Neural Networks · Physics 2009-11-10 A. Ramezanpour , S. Moghimi-Araghi

Models of confluent tissues are built out of tessellations of the space (both in two and three dimensions) in which the cost function is constructed in such a way that individual cells try to optimize their volume and surface in order to…

Disordered Systems and Neural Networks · Physics 2023-06-13 Pierfrancesco Urbani

The study of phase transition phenomenon of NP complete problems plays an important role in understanding the nature of hard problems. In this paper, we follow this line of research by considering the problem of counting solutions of…

Artificial Intelligence · Computer Science 2011-02-25 Minghao Yin , Ping Huang

We discuss the implementation of two distributed solvers of the random K-SAT problem, based on some development of the recently introduced survey-propagation (SP) algorithm. The first solver, called the "SP diffusion algorithm", diffuses as…

Disordered Systems and Neural Networks · Physics 2009-11-11 Joel Chavas , Cyril Furtlehner , Marc Mezard , Riccardo Zecchina

An instance of a random constraint satisfaction problem defines a random subset S (the set of solutions) of a large product space (the set of assignments). We consider two prototypical problem ensembles (random k-satisfiability and…

Several algorithms for solving constraint satisfaction problems are based on survey propagation, a variational inference scheme used to obtain approximate marginal probability estimates for variable assignments. These marginals correspond…

Artificial Intelligence · Computer Science 2020-01-29 Aditya Grover , Tudor Achim , Stefano Ermon

The distribution of overlaps of solutions of a random CSP is an indicator of the overall geometry of its solution space. For random $k$-SAT, nonrigorous methods from Statistical Physics support the validity of the ``one step replica…

Discrete Mathematics · Computer Science 2007-05-23 Gabriel Istrate

To study the structure of solutions for random k-SAT and random CSPs, this paper introduces the concept of average similarity degree to characterize how solutions are similar to each other. It is proved that under certain conditions, as r…

Artificial Intelligence · Computer Science 2007-05-23 Ke Xu , Wei Li

A number of problems in statistical physics and computer science can be expressed as the computation of marginal probabilities over a Markov random field. Belief propagation, an iterative message-passing algorithm, computes exactly such…

Machine Learning · Statistics 2012-10-23 Victorin Martin , Jean-Marc Lasgouttes , Cyril Furtlehner

Phase transitions in many complex combinational problems have been widely studied in the past decade. In this paper, we investigate phase transitions in the knowledge compilation empirically, where DFA, OBDD and d-DNNF are chosen as the…

Artificial Intelligence · Computer Science 2011-06-06 Jian Gao , Minghao Yin , Ke Xu

The study of phase transition behaviour in SAT has led to deeper understanding and algorithmic improvements of modern SAT solvers. Motivated by these prior studies of phase transitions in SAT, we seek to study the behaviour of size and…

Artificial Intelligence · Computer Science 2020-07-22 Rahul Gupta , Subhajit Roy , Kuldeep S. Meel

It is found that the statistical level fluctuations of the AQC 3-SAT problem undergo a transition from a poisson (regular) fluctuation form to a form consistent with the predictions of Random Matrix Theory. We present data which suggests…

Quantum Physics · Physics 2009-08-17 David R. Mitchell

In this paper we derive the equations for Loop Corrected Belief Propagation on a continuous variable Gaussian model. Using the exactness of the averages for belief propagation for Gaussian models, a different way of obtaining the…

Artificial Intelligence · Computer Science 2007-06-01 Bastian Wemmenhove , Bert Kappen

Belief propagation (BP) is a powerful tool to solve distributed inference problems, though it is limited by short cycles in the corresponding factor graph. Such cycles may lead to incorrect solutions or oscillatory behavior. Only for…

Systems and Control · Computer Science 2018-02-08 Christopher Lindberg , Julien M. Hendrickx , Henk Wymeersch