Approximate Counting via Correlation Decay Pinyan Lu Microsoft Research.

Slides:



Advertisements
Similar presentations
Slow and Fast Mixing of Tempering and Swapping for the Potts Model Nayantara Bhatnagar, UC Berkeley Dana Randall, Georgia Tech.
Advertisements

Bart Jansen 1.  Problem definition  Instance: Connected graph G, positive integer k  Question: Is there a spanning tree for G with at least k leaves?
1 LP Duality Lecture 13: Feb Min-Max Theorems In bipartite graph, Maximum matching = Minimum Vertex Cover In every graph, Maximum Flow = Minimum.
Approximation Algorithms
Bipartite Matching, Extremal Problems, Matrix Tree Theorem.
Queries with Difference on Probabilistic Databases Sanjeev Khanna Sudeepa Roy Val Tannen University of Pennsylvania 1.
Theory of Computing Lecture 18 MAS 714 Hartmut Klauck.
Umans Complexity Theory Lectures Lecture 2a: Reductions & Completeness.
Optimization problems, subexponential time, & Lasserre algorithms Featuring work by: Ryan O’DonnellCMU Venkat GuruswamiCMU Ali K. SinopCMU David WitmerCMU.
Introduction to PCP and Hardness of Approximation Dana Moshkovitz Princeton University and The Institute for Advanced Study 1.
A 3-Query PCP over integers a.k.a Solving Sparse Linear Systems Prasad Raghavendra Venkatesan Guruswami.
Inapproximability from different hardness assumptions Prahladh Harsha TIFR 2011 School on Approximability.
CS774. Markov Random Field : Theory and Application Lecture 04 Kyomin Jung KAIST Sep
CSC5160 Topics in Algorithms Tutorial 2 Introduction to NP-Complete Problems Feb Jerry Le
The Analysis and Design of Approximation Algorithms for the Maximum Induced Planar Subgraph Problem Kerri Morgan Supervisor: Dr. G. Farr.
Approximation Algoirthms: Semidefinite Programming Lecture 19: Mar 22.
Computational problems, algorithms, runtime, hardness
Semidefinite Programming
Recent Development on Elimination Ordering Group 1.
1 Optimization problems such as MAXSAT, MIN NODE COVER, MAX INDEPENDENT SET, MAX CLIQUE, MIN SET COVER, TSP, KNAPSACK, BINPACKING do not have a polynomial.
Computability and Complexity 24-1 Computability and Complexity Andrei Bulatov Approximation.
Job Scheduling Lecture 19: March 19. Job Scheduling: Unrelated Multiple Machines There are n jobs, each job has: a processing time p(i,j) (the time to.
Lower Bounds for Property Testing Luca Trevisan U C Berkeley.
1 On the Computation of the Permanent Dana Moshkovitz.
Accelerating Simulated Annealing for the Permanent and Combinatorial Counting Problems.
1 Joint work with Shmuel Safra. 2 Motivation 3 Motivation.
1 Spanning Tree Polytope x1 x2 x3 Lecture 11: Feb 21.
Dana Moshkovitz, MIT Joint work with Subhash Khot, NYU.
CSE 589 Applied Algorithms Spring Colorability Branch and Bound.
Nattee Niparnan. Easy & Hard Problem What is “difficulty” of problem? Difficult for computer scientist to derive algorithm for the problem? Difficult.
RESOURCES, TRADE-OFFS, AND LIMITATIONS Group 5 8/27/2014.
An FPTAS for #Knapsack and Related Counting Problems Parikshit Gopalan Adam Klivans Raghu Meka Daniel Štefankovi č Santosh Vempala Eric Vigoda.
Incidentor coloring: methods and results A.V. Pyatkin "Graph Theory and Interactions" Durham, 2013.
An Efficient Algorithm for Enumerating Pseudo Cliques Dec/18/2007 ISAAC, Sendai Takeaki Uno National Institute of Informatics & The Graduate University.
CSCI 3160 Design and Analysis of Algorithms Chengyu Lin.
Techniques for Proving NP-Completeness Show that a special case of the problem you are interested in is NP- complete. For example: The problem of finding.
Spanning and Sparsifying Rajmohan Rajaraman Northeastern University, Boston May 2012 Chennai Network Optimization WorkshopSpanning and Sparsifying1.
Lower Bounds for Property Testing Luca Trevisan U.C. Berkeley.
NP-COMPLETE PROBLEMS. Admin  Two more assignments…  No office hours on tomorrow.
PhD Projects Rahul Santhanam University of Edinburgh.
1/19 Minimizing weighted completion time with precedence constraints Nikhil Bansal (IBM) Subhash Khot (NYU)
Approximate Inference: Decomposition Methods with Applications to Computer Vision Kyomin Jung ( KAIST ) Joint work with Pushmeet Kohli (Microsoft Research)
The Markov Chain Monte Carlo Method Isabelle Stanton May 8, 2008 Theory Lunch.
CSE 589 Part V One of the symptoms of an approaching nervous breakdown is the belief that one’s work is terribly important. Bertrand Russell.
Inference Algorithms for Bayes Networks
CPS Computational problems, algorithms, runtime, hardness (a ridiculously brief introduction to theoretical computer science) Vincent Conitzer.
Unique Games Approximation Amit Weinstein Complexity Seminar, Fall 2006 Based on: “Near Optimal Algorithms for Unique Games" by M. Charikar, K. Makarychev,
Date: 2005/4/25 Advisor: Sy-Yen Kuo Speaker: Szu-Chi Wang.
1 CPSC 320: Intermediate Algorithm Design and Analysis July 30, 2014.
Spatial decay of correlations and efficient methods for computing partition functions. David Gamarnik Joint work with Antar Bandyopadhyay (U of Chalmers),
Chapter 11 Introduction to Computational Complexity Copyright © 2011 The McGraw-Hill Companies, Inc. Permission required for reproduction or display. 1.
NPC.
1 Relational Factor Graphs Lin Liao Joint work with Dieter Fox.
1 Structure Learning (The Good), The Bad, The Ugly Inference Graphical Models – Carlos Guestrin Carnegie Mellon University October 13 th, 2008 Readings:
CSCI-256 Data Structures & Algorithm Analysis Lecture Note: Some slides by Kevin Wayne. Copyright © 2005 Pearson-Addison Wesley. All rights reserved. 3.
CHAPTER SIX T HE P ROBABILISTIC M ETHOD M1 Zhang Cong 2011/Nov/28.
Chapter 10 NP-Complete Problems.
Lower Bounds for Property Testing
COMPLEXITY THEORY IN PRACTICE
Circuit Lower Bounds A combinatorial approach to P vs NP
Possibilities and Limitations in Computation
Markov Networks.
Introduction to PCP and Hardness of Approximation
Problem Solving 4.
MCMC for PGMs: The Gibbs Chain
CSE 6408 Advanced Algorithms.
Complexity Theory in Practice
Umans Complexity Theory Lectures
Complexity Theory in Practice
Markov Networks.
Presentation transcript:

Approximate Counting via Correlation Decay Pinyan Lu Microsoft Research

Outline Counting and probability distribution Correlation decay approach Examples

Counting Problems SAT: Is there a satisfying assignment for a given a CNF formula? Counting SAT: How many? Counting Colorings of a graph Counting Independent sets of a graph Counting perfect matchings of a bipartite graph (Permanent) ……

Counting Problems Probability

Counting Problems Probability Partition function on statistical physics

Counting Problems Probability Partition function on statistical physics Inference on Graphical Models

Counting Problems Probability Partition function on statistical physics Inference on Graphical Models Query on probabilistic database Optimization on stochastic model …… ……

Approximate Counting

Counting vs Distribution

Counting Sampling FPRAS

Counting vs Distribution Counting Probability Sampling FPRAS Correlation Decay Other methods

Outline Counting and probability distribution Correlation decay approach Examples

Computation Tree

Correlation Decay

Computational Efficient Correlation Decay

Proof Sketch for Correlation Decay

Potential Function

Outline Counting and probability distribution Correlation decay approach Examples

Spin Systems

A model in Statistical Physics and Applied Probability A framework of many combinatorial counting problems Have applications in AI, coding theory and so on.

Constraint Satisfaction Problems

Two-State Spin System

Uniqueness Condition

Hardcore Model [Weitz 2006]

Ising Model [Sinclair, Srivastava, and Thurley 2011]

General cases[Li, L., Yin 2012,13] Potential Function Computational Efficient Correlation Decay

Ferromagnetic two-spin systems

Coloring Potential Function

Multi-spin System More efficient recursive function

Counting Edge Covers A set of edges such that every vertex has at least one adjacent edge in it

Counting Edge Covers A set of edges such that every vertex has at least one adjacent edge in it FPRAS for 3-regular graphs based on Markov Chain Monte Carlo[Bezakova, Rummler 2009]. FPTAS for general graphs. [Lin, Liu, L. 2014] Computational Efficient Correlation Decay

Counting weighted Edge Covers FPTAS for general edge weighted graphs, given that the weights are bounded away from 0. [Liu, L., Zhang 2014] For exponential small weights, it becomes counting perfect matchings for general graphs. Similar situation as counting weighted matchings. Potential Function

Counting Monotone CNF [Liu, L. 2015] A CNF formula is monotone if each variable appears positively. We give a FPTAS to count the number of solutions for a monotone CNF when each variable appears at most five times. It is NP-hard to approximately count if we allow a variable to appear six times. Two-layer recursive function

#BIS Counting independent sets for bipartite graphs. Conjectured to be of intermediate complexity. Plays a similar role as the Unique Game for optimization problems. A large number of other problems are proved to have the same complexity as #BIS (#BIS-equivalent) or at least as hard as #BIS (#BIS-hard)

#BIS Local MCMCs mixes slowly even on bipartite graphs. For NP-hardness of #IS, reduction from Max- CUT, which is always easy for bipartite graphs. #BIS with a maximum degree of 6 is already as hard as general #BIS (#BIS-hard). [CGGGJSV14] No algorithmic evidence to distinguish #BIS from #IS.

#BIS FPTAS for #BIS when the maximum degree for one side is no larger than 5. [Liu, L. 2015] No restriction for the degrees on the other side. Identical to Weitz's algorithm for general #IS. Combine two recursion steps into one, and work with this two-layer recursion instead.

Analogy with MCMC Algorithm design part is kind of standard. The over framework for the analysis is the same. A number of techniques and tools have been developed to the analysis. However, it is still very challenging to prove and many problems remains open.