EricVigoda

Archive of posts with tag 'EricVigoda'

  • Eric Vigoda, Analyzing Markov Chains using Belief Propagation

    Analyzing Markov Chains using Belief Propagation
    Eric Vigoda
    School of Computer Science, Georgia Institute of Technology, Atlanta, Georgia, USA
    2016/12/16 Friday 3PM-4PM
    For counting weighted independent sets weighted by a parameter λ (known as the hard-core model) there is a beautiful connection between statistical physics phase transitions on infinite, regular trees and the computational complexity of approximate counting on graphs of maximum degree D. For λ below the critical point there is an elegant algorithm devised by Weitz (2006). The drawback of his algorithm is that the running time is exponential in log D. In this talk we’ll describe recent work which shows O(n log n) mixing time of the single-site Markov chain when the girth>6 and D is at least a sufficiently large constant. Our proof utilizes BP (belief propagation) to design a distance function in our coupling analysis. This is joint work with C. Efthymiou, T. Hayes, D. Stefankovic, and Y. Yin which appeared in FOCS ’16.
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  • [FYI] KAIST Theory Day (May 31, 2015)

    FYI: (KAIST Theory Day organized by Jinwoo Shin and Eric Vigoda)

    KAIST Theory Day
    2015/5/31 Sun 10AM-5PM (Building N1, room 102, KAIST)
    9:30 – Coffee and refreshments
    10:00 – Daniel Stefankovic (Rochester):  Spin models: connections between complexity, phase transition, belief propagation, and induced matrix norms
    11:00 – Yitong Yin (Nanjing)Phase transition of hypergraph matchings
    12:00 – Lunch break
    1:30 – Euiwoong Lee (CMU):  Hardness of Graph Pricing through Generalized Max-Dicut
    2:30 – Sewoong Oh (UIUC):  Data processing inequality for differential privacy and applications
    3:30 – Coffee break
    4:00 – Martin Ziegler (TU Darmstadt):  Algebraic Complexity Theory and Quantum Logic

    Speaker: Daniel Stefankovic

    Title: Spin models: connections between complexity, phase transition, belief propagation, and induced matrix norms

    What are the typical configurations of a spin model (for example, Ising model, or Potts model) on a random regular graph? We show that the answer to this question is characterized by tree recursions (belief propagation) and p->q operator matrix norms.

    Understanding the typical configurations allows us to show hardness of approximating the partition function for certain multispin models (for example, Potts model) on d-regular graphs in the non-uniqueness regime (that is, when the Gibbs measure on infinite d-regular tree is not unique). Joint work with Andreas Galanis, and Eric Vigoda.

    Speaker: Yitong Yin

    Title: Phase transition of hypergraph matchings

    We study the problem of approximately counting weighted matchings in hypergraphs of bounded maximum edge size and maximum degree. The problem is expressed as evaluating the partition function, which is the weighted sum of all macthings in a hypergraph where each macthing M is assigned a weight λ M in terms of a fixed activity parameter λ. This model unifies two important problems in approximate counting arising from statistical physics: counting independent set (the hardcore model) and counting matchings (the monomer-dimer model).

    We show that for hypergraph matchings, the critical activity λc= dd/k(d-1)(d+1) is the uniqueness threshold for the Gibbs measure on (d+1)-uniform (k+1)-regular hyper-tree, and when λ<λc, the hypergraphs of maximum edge size at most d+1 and maximum degree at most k+1 exhibit strong spatial mixing. As a consequence, there is an FPTAS for counting matchings in hypergraphs of bounded maximum edge size at most and bounded maximum degree as long as the uniqueness condition is satisfied.

    As for the inapproximability, it can be easily shown that there is no FPRAS for the problem when λ>2λc, unless NP=RP. This left an intriguing gap between λc and 2λc. A key step towards tight inapproximability result is the local weak convergence from a sequence of finite graphs to the extremal measures for the uniqueness threshold on the infinite tree. For hypergraph matchings, we discover that such local weak convergence does not exist for any sequence of finite hypergraphs, which indicates that approximate counting on hypergraphs (or general CSPs) contains much richer structure yet to be understood.

    Speaker: Euiwoong Lee

    Title: Hardness of Graph Pricing through Generalized Max-Dicut

    The Graph Pricing problem is among the fundamental problems whose approximability is not well-understood. While there is a simple combinatorial ¼-approximation algorithm, the best hardness result remains at ½ assuming the Unique Games Conjecture (UGC). We show that it is NP-hard to approximate within a factor better than ¼ under the UGC, so that the simple combinatorial algorithm might be the best possible.

    This work is based on the effort to view the Graph Pricing problem as a Constraint Satisfaction Problem (CSP) simpler than the standard and complicated formulation. We propose the problem called Generalized Max-Dicut(T), which has a domain size T + 1 for every T ≥ 1 . Generalized Max-Dicut(1) is well-known Max-Dicut. Besides its connection to Graph Pricing, the hardness of Generalized Max-Dicut is interesting in its own right since in most arity two CSPs studied in the literature, SDP-based algorithms perform better than LP-based or combinatorial algorithms — for this arity two CSP, a simple combinatorial algorithm does the best.

    Speaker: Sewoong Oh

    Title: Data processing inequality for differential privacy and applications

    We provide a hypothesis testing interpretation to differential privacy and derive natural forward and reverse data processing inequalities. These inequalities are very useful in deriving tight impossibility results, as demonstrated by the following two applications: composing multiple queries and multi-party computation. The impossibility results hold for arbitrary parameter settings (privacy levels, number of queries, etc) and for both standard and approximate differential privacy settings. Further, these impossibility results cannot be improved upon.

    Speaker: Martin Ziegler

    Title: Algebraic Complexity Theory and Quantum Logic

    Algebraic models of computation (like register machines) make one operation per step regardless of the value processed. They underly algorithms for sorting or in computational geometry. As opposed to bit models, algebraic methods permit to establish non-trivial lower bounds:

    We recall Morgenstern’s elegant proof that the fast fourier transform is optimal; then proceed to the structural complexity theory and prove the quantum satisfiability problem complete for NP_R^0: a well-known discrete complexity class between NP and PSPACE. (Joint work with Christian Herrmann.)

  • Eric Vigoda, Computational Phase Transitions for the Potts Model

    Computational Phase Transitions for the Potts Model
    Eric Vigoda
    College of Computing, Georgia Institute of Technology, Atlanta, GA, USA
    2015/4/14 Tue 4PM-5PM
    This is a followup talk to my CS colloquium on March 2. In that talk I discussed the problems of counting independent sets and colorings. Those problems are examples of antiferromagnetic systems in which neighboring vertices prefer different assignments. In this talk we will look at ferromagnetic systems where neighboring vertices prefer the same assignment. We will focus on the ferromagnetic Potts model. We will look at the phase transitions in this model, and their connections to the complexity of associated counting/sampling problems and the performance of related Markov chains.
    The talk is based on joint works with Andreas Galanis, Daniel Stefankovic, and Linji Yang.
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  • [Lecture Series] Eric Vigoda, Approximating the Permanent

    FYI (Lecture Series)

    Approximating the Permanent
    Eric Vigoda
    College of Computing, Georgia Institute of Technology, Atlanta, GA, USA
    2015/3/11 Wed 9AM-10:15AM (E3-1, Room 1501)
    I’ll explain the canonical paths method for bounding the mixing time of a
    Markov chain. I’ll show the analysis of a Markov chain for generating a
    random matching of a graph and its extension for the estimating the
    number of perfect matchings of a bipartite graph.
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  • [CS Colloquium] Eric Vigoda, Phase Transitions in Approximate Counting

    FYI (CS Colloquium)

    Phase Transitions in Approximate Counting
    Eric Vigoda
    College of Computing, Georgia Institute of Technology, Atlanta, GA, USA
    2015/3/2 Mon 4PM-5PM (E3-1, Room 1501)
    In this talk we will explain a series of recent works that establish a beautiful connection between the computational complexity of approximate counting problems and statistical physics phase transitions. Our focus is on counting problems such as given a graph with n vertices can we estimate the number of independent sets or k-colorings of this graph in time polynomial in n? We will show that these problems experience a computational phase transition – on one side there is an efficient approximation algorithm and on the other side it is NP-hard to approximate. The critical point for this computational change coincides exactly with a statistical physics phase transition on infinite trees. Our recent work extends these connections to more general models. The key technical contribution is a novel approach for analyzing random regular graphs. This is joint work with Andreas Galanis (Oxford) and Daniel Stefankovic (Rochester) that appeared in STOC ’14.
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  • Eric Vigoda, Improved bound on the phase transition for independent sets in the square lattice

    Improved bound on the phase transition for independent sets in the square lattice
    Eric Vigoda
    College of Computing, Georgia Institute of Technology, Atlanta, USA</a>
    2011/7/14 Thu 4PM-5PM (Room 3433, Bldg. E6-1)
    The hard-core model has received much attention in the past couple of decades as a lattice gas model with hard constraints in statistical physics, a multicast model of calls in communication networks, and as a weighted independent set problem in combinatorics, probability and theoretical computer science.
    In this model, each independent set I in a graph G is weighted proportionally to λ|I|, for a positive real parameter λ. For large λ, computing the partition function (namely, the normalizing constant which makes the weighting a probability distribution on a finite graph) on graphs of maximum degree Δ≥3, is a well known computationally challenging problem. More concretely, let λc(TΔ) denote the critical value for the so-called uniqueness threshold of the hard-core model on the infinite Δ-regular tree; recent breakthrough results of Dror Weitz (2006) and Allan Sly (2010) have identified λc(TΔ) as a threshold where the hardness of estimating the above partition function undergoes a computational transition.
    We focus on the well-studied particular case of the square lattice Z2, and provide a new lower bound for the uniqueness threshold, in particular taking it well above λc(T4). Our technique refines and builds on the tree of self-avoiding walks approach of Weitz, resulting in a new technical sufficient criterion (of wider applicability) for establishing strong spatial mixing (and hence uniqueness) for the hard-core model. Our new criterion achieves better bounds on strong spatial mixing when the graph has extra structure, improving upon what can be achieved by just using the maximum degree. Applying our technique to Z2 we prove that strong spatial mixing holds for all λ<2.3882, improving upon the work of Weitz that held for λ<27/16=1.6875. Our results imply a fully-polynomial deterministic approximation algorithm for estimating the partition function, as well as rapid mixing of the associated Glauber dynamics to sample from the hard-core distribution.
    This is joint work with Ricardo Restrepo, Jinwoo Shin, Prasad Tetali, and Linji Yang. A preprint is available from the arXiv at: arxiv:1105.0914
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  • Eric Vigoda, Markov Chain Monte Carlo and Approximating the Permanent

    Markov Chain Monte Carlo and Approximating the Permanent
    Eric Vigoda
    College of Computing, Georgia Institute of Technology, Atlanta, USA</a>
    2009/04/13 Monday 5PM-6PM
    The Markov Chain Monte Carlo (MCMC) method is a widely-used algorithmic approach for randomly sampling from and estimating the cardinality of large sets. In this talk I will give an introduction to the MCMC approach. Then I will explain a more sophisticated variant that gives a polynomial-time algorithm to approximate the permanent of a non-negative matrix. In graph-theoretic terminology, the permanent corresponds to the number of perfect matchings of a bipartite graph.
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