Universality of the Random-Cluster Model and Applications to Quantum Systems
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Image Segmentation Combining Markov Random Fields and Dirichlet Processes
ANR meeting Image segmentation combining Markov Random Fields and Dirichlet Processes Jessica SODJO IMS, Groupe Signal Image, Talence Encadrants : A. Giremus, J.-F. Giovannelli, F. Caron, N. Dobigeon Jessica SODJO ANR meeting 1 / 28 ANR meeting Plan 1 Introduction 2 Segmentation using DP models Mixed MRF / DP model Inference : Swendsen-Wang algorithm 3 Hierarchical segmentation with shared classes Principle HDP theory 4 Conclusion and perspective Jessica SODJO ANR meeting 2 / 28 ANR meeting Introduction Segmentation – partition of an image in K homogeneous regions called classes – label the pixels : pixel i $ zi 2 f1;:::; K g Bayesian approach – prior on the distribution of the pixels – all the pixels in a class have the same distribution characterized by a parameter vector Uk – Markov Random Fields (MRF) : exploit the similarity of pixels in the same neighbourhood Constraint : K must be fixed a priori Idea : use the BNP models to directly estimate K Jessica SODJO ANR meeting 3 / 28 ANR meeting Segmentation using DP models Plan 1 Introduction 2 Segmentation using DP models Mixed MRF / DP model Inference : Swendsen-Wang algorithm 3 Hierarchical segmentation with shared classes Principle HDP theory 4 Conclusion and perspective Jessica SODJO ANR meeting 4 / 28 ANR meeting Segmentation using DP models Notations – N is the number of pixels – Y is the observed image – Z = fz1;:::; zN g – Π = fA1;:::; AK g is a partition and m = fm1;:::; mK g with mk = jAk j A1 A2 m1 = 1 m2 = 5 A3 m3 = 6 mK = 4 AK FIGURE: Example of partition Jessica SODJO ANR -
Lecturenotes 4 MCMC I – Contents
Lecturenotes 4 MCMC I – Contents 1. Statistical Physics and Potts Models 2. Sampling and Re-weighting 3. Importance Sampling and Markov Chain Monte Carlo 4. The Metropolis Algorithm 5. The Heatbath Algorithm (Gibbs Sampler) 6. Start and Equilibration 7. Energy Checks 8. Specific Heat 1 Statistical Physics and Potts Model MC simulations of systems described by the Gibbs canonical ensemble aim at calculating estimators of physical observables at a temperature T . In the following we choose units so that β = 1/T and consider the calculation of the expectation value of an observable O. Mathematically all systems on a computer are discrete, because a finite word length has to be used. Hence, K X (k) Ob = Ob(β) = hOi = Z−1 O(k) e−β E (1) k=1 K X (k) where Z = Z(β) = e−β E (2) k=1 is the partition function. The index k = 1,...,K labels all configurations (or microstates) of the system, and E(k) is the (internal) energy of configuration k. To distinguish the configuration index from other indices, it is put in parenthesis. 2 We introduce generalized Potts models in an external magnetic field on d- dimensional hypercubic lattices with periodic boundary conditions. Without being overly complicated, these models are general enough to illustrate the essential features we are interested in. In addition, various subcases of these models are by themselves of physical interest. Generalizations of the algorithmic concepts to other models are straightforward, although technical complications may arise. We define the energy function of the system by (k) (k) (k) −β E = −β E0 + HM (3) where (k) X (k) (k) (k) (k) 2 d N E = −2 J (q , q ) δ(q , q ) + (4) 0 ij i j i j q hiji N 1 for qi = qj (k) X (k) with δ(qi, qj) = and M = 2 δ(1, qi ) . -
Mathematisches Forschungsinstitut Oberwolfach Scaling Limits in Models of Statistical Mechanics
Mathematisches Forschungsinstitut Oberwolfach Report No. 41/2018 DOI: 10.4171/OWR/2018/41 Scaling Limits in Models of Statistical Mechanics Organised by Dmitry Ioffe, Haifa Gady Kozma, Rehovot Fabio Toninelli, Lyon 9 September – 15 September 2018 Abstract. This conference (part of a long running series) aims to cover the interplay between probability and mathematical statistical mechanics. Specific topics addressed during the 22 talks include: Universality and critical phenomena, disordered models, Gaussian free field (GFF), random planar graphs and unimodular planar maps, reinforced random walks and non-linear σ-models, non-equilibrium dynamics. Less stress is given to topics which have running series of Oberwolfach conferences devoted to them specifically, such as random matrices or integrable models and KPZ universality class. There were 50 participants, including 9 postdocs and graduate students, working in diverse intertwining areas of probability, statistical mechanics and mathematical physics. Subject classification: MSC: 60,82; IMU: 10,13. Introduction by the Organisers This workshop was a sequel to a MFO conference, by the same organizers, which took place in 2015. More broadly, it is a sequel to MFO conferences in 2006, 2009 and 2012, organised by Ken Alexander, Marek Biskup, Remco van der Hofstad and Vladas Sidoravicius. The main focus of the conference remained on probabilistic and analytic methods of non-integrable statistical mechanics. With respect to the previous editions, greater emphasis was put on statistical mechanics models on groups and general graphs, as a lot has happened in this arena recently. The list of 50 participants reflects our attempts to maintain an optimal balance between diverse fields, leading experts and promising young researchers. -
Random and Out-Of-Equilibrium Potts Models Christophe Chatelain
Random and Out-of-Equilibrium Potts models Christophe Chatelain To cite this version: Christophe Chatelain. Random and Out-of-Equilibrium Potts models. Statistical Mechanics [cond- mat.stat-mech]. Université de Lorraine, 2012. tel-00959733 HAL Id: tel-00959733 https://tel.archives-ouvertes.fr/tel-00959733 Submitted on 15 Mar 2014 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Habilitation `aDiriger des Recherches Mod`eles de Potts d´esordonn´eset hors de l’´equilibre Christophe Chatelain Soutenance publique pr´evue le 17 d´ecembre 2012 Membres du jury : Rapporteurs : Werner Krauth Ecole´ Normale Sup´erieure, Paris Marco Picco Universit´ePierre et Marie Curie, Paris 6 Heiko Rieger Universit´ede Saarbr¨ucken, Allemagne Examinateurs : Dominique Mouhanna Universit´ePierre et Marie Curie, Paris 6 Wolfhard Janke Universit´ede Leipzig, Allemagne Bertrand Berche Universit´ede Lorraine Institut Jean Lamour Facult´edes Sciences - 54500 Vandœuvre-l`es-Nancy Table of contents 1. Random and Out-of-Equililibrium Potts models 4 1.1.Introduction................................. 4 1.2.RandomPottsmodels .......................... 6 1.2.1.ThepurePottsmodel ......................... 6 1.2.1.1. Fortuin-Kasteleyn representation . 7 1.2.1.2. From loop and vertex models to the Coulomb gas . -
Theory of Continuum Percolation I. General Formalism
Theory of continuum percolation I. General formalism Alon Drory Dipartimento di Fisica, Universit´a La Sapienza Piazzale Aldo Moro 2, Roma 00187, Italy Abstract The theoretical basis of continuum percolation has changed greatly since its beginning as little more than an analogy with lattice systems. Nevertheless, there is yet no comprehensive theory of this field. A ba- sis for such a theory is provided here with the introduction of the Potts fluid, a system of interacting s-state spins which are free to move in the continuum. In the s → 1 limit, the Potts magnetization, suscepti- bility and correlation functions are directly related to the percolation probability, the mean cluster size and the pair-connectedness, respec- tively. Through the Hamiltonian formulation of the Potts fluid, the standard methods of statistical mechanics can therefore be used in the continuum percolation problem. arXiv:cond-mat/9606195v1 26 Jun 1996 1 1 Introduction The theoretical basis of continuum percolation [1] changed drastically during the 1970’s. In their seminal 1970 paper, Scher and Zallen [2] considered continuum percolation as an extension of lattice models. Noting that the critical volume φc is approximately universal, i. e., essentially independent of the lattice structure, they suggested it might be carried over into continuum systems. Such a view of the continuum as the representation of what is lattice- independent sits well with the ideas of the renormalization group where the continuum often represents merely a scale at which the underlying lattice blurs and vanishes. Yet continuum percolation turned out to be much richer than suggested by this conception. -
Arxiv:1504.02898V2 [Cond-Mat.Stat-Mech] 7 Jun 2015 Keywords: Percolation, Explosive Percolation, SLE, Ising Model, Earth Topography
Recent advances in percolation theory and its applications Abbas Ali Saberi aDepartment of Physics, University of Tehran, P.O. Box 14395-547,Tehran, Iran bSchool of Particles and Accelerators, Institute for Research in Fundamental Sciences (IPM) P.O. Box 19395-5531, Tehran, Iran Abstract Percolation is the simplest fundamental model in statistical mechanics that exhibits phase transitions signaled by the emergence of a giant connected component. Despite its very simple rules, percolation theory has successfully been applied to describe a large variety of natural, technological and social systems. Percolation models serve as important universality classes in critical phenomena characterized by a set of critical exponents which correspond to a rich fractal and scaling structure of their geometric features. We will first outline the basic features of the ordinary model. Over the years a variety of percolation models has been introduced some of which with completely different scaling and universal properties from the original model with either continuous or discontinuous transitions depending on the control parameter, di- mensionality and the type of the underlying rules and networks. We will try to take a glimpse at a number of selective variations including Achlioptas process, half-restricted process and spanning cluster-avoiding process as examples of the so-called explosive per- colation. We will also introduce non-self-averaging percolation and discuss correlated percolation and bootstrap percolation with special emphasis on their recent progress. Directed percolation process will be also discussed as a prototype of systems displaying a nonequilibrium phase transition into an absorbing state. In the past decade, after the invention of stochastic L¨ownerevolution (SLE) by Oded Schramm, two-dimensional (2D) percolation has become a central problem in probability theory leading to the two recent Fields medals. -
CFT Interpretation of Merging Multiple SLE Traces
CFT Interpretation of Merging Multiple SLE Traces Annekathrin Muller-Lohmann¨ ∗ Institut f¨ur theoretische Physik University of Hannover Appelstraße 2 D-30167 Hannover, Germany November 1, 2018 Abstract In this paper we give a physical interpretation of the probability of a Stochastic Lowner¨ Evolution (SLE) trace approaching a marked point in the upper half plane, e. g. on another trace. Our approach is based on the concept of fusion of boundary with bulk or other boundary fields in Conformal Field Theory (CFT). In this context we also state a proposal how the results can be extended to situations that happen not sufficiently far away from the boundary. Contents 1 Introduction 2 1.1 Motivation .............................. 2 1.2 Outline ................................ 3 2 Basic Definitions and Notations 3 2.1 SingleSLE .............................. 3 2.2 MultipleSLE ............................. 4 2.3 BoundaryCFTRevisited ...................... 5 2.4 SLE Martingales and Physical Quantities in BCFT . 7 2.5 FusionandtheOPEinCFT .................... 10 3 ProbabilitiesinBCFTandSLE 11 arXiv:0711.3165v1 [math-ph] 20 Nov 2007 3.1 Partition Functions and Probabilities in BCFT . 11 3.2 The SLE Probability of Intersecting a Disc . 12 4 TheEffectofFusionontheScalingProperties 13 4.1 TheL¨owner EquationDifferential Operators. 13 4.2 ThePurelyBoundaryDifferentialOperators. 14 5 Interpretation of Merging multiple SLE traces 15 5.1 SLE Traces Visiting a Point in the Upper Half Plane . 15 5.2 SLETracesMerging ......................... 17 5.3 TheAngularDependency . 18 ∗[email protected] 6 MultipleSLEasMartingaleWeightedSLE 19 6.1 The Associated Martingale Mt ................... 19 6.2 Probability Interpretation for Mt .................. 20 6.3 Fusion in the Associated Martingale . 21 7 Discussion 22 A Appendix: How to Compute “Fused” Differential Operators 23 1 Introduction 1.1 Motivation Stochastic Lowner¨ Evolutions (SLE) as introduced by O. -
A Novel Approach for Markov Random Field with Intractable Normalising Constant on Large Lattices
A novel approach for Markov Random Field with intractable normalising constant on large lattices W. Zhu ∗ and Y. Fany February 19, 2018 Abstract The pseudo likelihood method of Besag (1974), has remained a popular method for estimating Markov random field on a very large lattice, despite various documented deficiencies. This is partly because it remains the only computationally tractable method for large lattices. We introduce a novel method to estimate Markov random fields defined on a regular lattice. The method takes advantage of conditional independence structures and recur- sively decomposes a large lattice into smaller sublattices. An approximation is made at each decomposition. Doing so completely avoids the need to com- pute the troublesome normalising constant. The computational complexity arXiv:1601.02410v1 [stat.ME] 11 Jan 2016 is O(N), where N is the the number of pixels in lattice, making it computa- tionally attractive for very large lattices. We show through simulation, that the proposed method performs well, even when compared to the methods using exact likelihoods. Keywords: Markov random field, normalizing constant, conditional indepen- dence, decomposition, Potts model. ∗School of Mathematics and Statistics, University of New South Wales, Sydney 2052 Australia. Communicating Author Wanchuang Zhu: Email [email protected]. ySchool of Mathematics and Statistics, University of New South Wales, Sydney 2052 Australia. Email [email protected]. 1 1 Introduction Markov random field (MRF) models have an important role in modelling spa- tially correlated datasets. They have been used extensively in image and texture analyses ( Nott and Ryden´ 1999, Hurn et al. 2003), image segmentation (Pal and Pal 1993, Van Leemput et al. -
Stochastic Analysis: Geometry of Random Processes
Mathematisches Forschungsinstitut Oberwolfach Report No. 26/2017 DOI: 10.4171/OWR/2017/26 Stochastic Analysis: Geometry of Random Processes Organised by Alice Guionnet, Lyon Martin Hairer, Warwick Gr´egory Miermont, Lyon 28 May – 3 June 2017 Abstract. A common feature shared by many natural objects arising in probability theory is that they tend to be very “rough”, as opposed to the “smooth” objects usually studied in other branches of mathematics. It is however still desirable to understand their geometric properties, be it from a metric, a topological, or a measure-theoretic perspective. In recent years, our understanding of such “random geometries” has seen spectacular advances on a number of fronts. Mathematics Subject Classification (2010): 60xx, 82xx. Introduction by the Organisers The workshop focused on the latest progresses in the study of random geome- tries, with a special emphasis on statistical physics models on regular lattices and random maps, and their connexions with the Gaussian free field and Schramm- Loewner evolutions. The week opened with a spectacular talk by H. Duminil-Copin (based on his work with Raoufi and Tassion), presenting a new, groundbreaking use of decision trees in the context of statistical physics models in order to prove the sharpness of the phase transition, and vastly extending earlier results. Most notably, this new approach allows to circumvent the use of either the BK inequality or of planarity properties. In particular, it applies to the random cluster model FK(d, q) on Zd for every parameter q 1 and dimension d 2. A significant proportion≥ of the talks focused≥ on various and ever growing aspects of discrete random geometries, notably random planar maps, and their links with the planar Gaussian free field (GFF). -
The FKG Inequality for Partially Ordered Algebras
J Theor Probab (2008) 21: 449–458 DOI 10.1007/s10959-007-0117-7 The FKG Inequality for Partially Ordered Algebras Siddhartha Sahi Received: 20 December 2006 / Revised: 14 June 2007 / Published online: 24 August 2007 © Springer Science+Business Media, LLC 2007 Abstract The FKG inequality asserts that for a distributive lattice with log- supermodular probability measure, any two increasing functions are positively corre- lated. In this paper we extend this result to functions with values in partially ordered algebras, such as algebras of matrices and polynomials. Keywords FKG inequality · Distributive lattice · Ahlswede-Daykin inequality · Correlation inequality · Partially ordered algebras 1 Introduction Let 2S be the lattice of all subsets of a finite set S, partially ordered by set inclusion. A function f : 2S → R is said to be increasing if f(α)− f(β) is positive for all β ⊆ α. (Here and elsewhere by a positive number we mean one which is ≥0.) Given a probability measure μ on 2S we define the expectation and covariance of functions by Eμ(f ) := μ(α)f (α), α∈2S Cμ(f, g) := Eμ(fg) − Eμ(f )Eμ(g). The FKG inequality [8]assertsiff,g are increasing, and μ satisfies μ(α ∪ β)μ(α ∩ β) ≥ μ(α)μ(β) for all α, β ⊆ S. (1) then one has Cμ(f, g) ≥ 0. This research was supported by an NSF grant. S. Sahi () Department of Mathematics, Rutgers University, New Brunswick, NJ 08903, USA e-mail: [email protected] 450 J Theor Probab (2008) 21: 449–458 A special case of this inequality was previously discovered by Harris [11] and used by him to establish lower bounds for the critical probability for percolation. -
Polynomial Time Randomised Approximation Schemes for the Tutte Polynomial of Dense Graphs
Polynomial time randomised approximation schemes for the Tutte polynomial of dense graphs Noga Alon∗ Alan Friezey Dominic Welshz Abstract (iii) How many acyclic orientations has G? The Tutte-Gr¨othendieck polynomial T (G; x; y) of a graph Each of these is a special case of the general problem of G encodes numerous interesting combinatorial quanti- evaluating the Tutte polynomial of a graph (or matroid) ties associated with the graph. Its evaluation in various at a particular point of the (x; y)-plane | in other words points in the (x; y) plane give the number of spanning is a Tutte-Gr¨othendieck invariant. Other invariants in- forests of the graph, the number of its strongly connected clude: orientations, the number of its proper k-colorings, the (all terminal) reliability probability of the graph, and var- (iv) the chromatic and flow polynomials of a graph; ious other invariants the exact computation of each of which is well known to be #P -hard. Here we develop (v) the partition function of a Q-state Potts model; a general technique that supplies fully polynomial ran- domised approximation schemes for approximating the (vi) the Jones polynomial of an alternating link; value of T (G; x; y) for any dense graph G, that is, any graph on n vertices whose minimum degree is Ω(n), (vii) the weight enumerator of a linear code over GF (q). whenever x 1 and y 1, and in various additional ≥ ≥ points. This region includes evaluations of reliability and It has been shown in Vertigan and Welsh [19] that apart partition functions of the ferromagnetic Q-state Potts from a few special points and 2 special hyperbolae, the model. -
Negative Dependence Via the FKG Inequality
Negative Dependence via the FKG Inequality Devdatt Dubhashi Department of Computer Science and Engineering Chalmers University SE 412 96, G¨oteborg, SWEDEN [email protected] Abstract We show how the FKG Inequality can be used to prove negative dependence properties. 1 Introduction Pemantle[21] points out that there is a need for a theory of negatively dependent events anal- ogous to the existing natural and useful theory of positively dependent events. Informally speaking, random variables are said to be negatively dependent, if they have the following property: if any one subset of the variables is \high", then other (disjoint) subsets of the vari- ables are \low". He gives a survey of various notions of negative dependence, two of which are reviewed below, and points out several applications of consequences of one of these concepts: negative association. These examples include natural stochastic processes and structures such as the uniform random spanning tree[16], the simple exclusion process[15], the random clus- ter model and occupancy numbers of competing bins[6]. Such variables arise frequently in the analysis of algorithms where a stream of random bits influences either the input or the execution of the algorithm. Negative dependence may also be used to obtain information on the distribution of functionals such as the sum of the variables involved (or more generally, a non-decreasing function). Newman[19] shows that under negative dependence, one obtains a Central Limit Theorem (CLT) for stationary sequences of random variables. Dubhashi and Ranjan[6] show that under negative dependence conditions, one can employ classical tools of concentration of measure such as the Chernoff-Hoeffding (CH) bounds and related martingale inequalities (see also [20, 23, 4, 14]).