The set of solutions of random XORSAT formulae

The XOR-satisfiability (XORSAT) problem requires finding an assignment of $n$ Boolean variables that satisfy $m$ exclusive OR (XOR) clauses, whereby each clause constrains a subset of the variables. We consider random XORSAT instances, drawn uniformly at random from the ensemble of formulae containing $n$ variables and $m$ clauses of size $k$. This model presents several structural similarities to other ensembles of constraint satisfaction problems, such as $k$-satisfiability ($k$-SAT), hypergraph bicoloring and graph coloring. For many of these ensembles, as the number of constraints per variable grows, the set of solutions shatters into an exponential number of well-separated components. This phenomenon appears to be related to the difficulty of solving random instances of such problems. We prove a complete characterization of this clustering phase transition for random $k$-XORSAT. In particular, we prove that the clustering threshold is sharp and determine its exact location. We prove that the set of solutions has large conductance below this threshold and that each of the clusters has large conductance above the same threshold. Our proof constructs a very sparse basis for the set of solutions (or the subset within a cluster). This construction is intimately tied to the construction of specific subgraphs of the hypergraph associated with an instance of $k$-XORSAT. In order to study such subgraphs, we establish novel local weak convergence results for them.

[1]  Amin Coja-Oghlan A Better Algorithm for Random k-SAT , 2009, ICALP.

[2]  Andrea Montanari,et al.  Factor models on locally tree-like graphs , 2011, ArXiv.

[3]  O. Kallenberg Foundations of Modern Probability , 2021, Probability Theory and Stochastic Modelling.

[4]  Daniel A. Spielman,et al.  Analysis of low density codes and improved designs using irregular graphs , 1998, STOC '98.

[5]  N. Wormald Models of random regular graphs , 2010 .

[6]  Devdatt P. Dubhashi,et al.  Concentration of Measure for the Analysis of Randomized Algorithms: Contents , 2009 .

[7]  Gerry Leversha,et al.  Foundations of modern probability (2nd edn), by Olav Kallenberg. Pp. 638. £49 (hbk). 2002. ISBN 0 387 95313 2 (Springer-Verlag). , 2004, The Mathematical Gazette.

[8]  I. Benjamini,et al.  Percolation Beyond $Z^d$, Many Questions And a Few Answers , 1996 .

[9]  A. Dembo,et al.  Gibbs Measures and Phase Transitions on Sparse Random Graphs , 2009, 0910.5460.

[10]  Arjen K. Lenstra,et al.  Factorization of a 768-Bit RSA Modulus , 2010, CRYPTO.

[11]  Olivier Dubois,et al.  The 3-XORSAT threshold , 2002, The 43rd Annual IEEE Symposium on Foundations of Computer Science, 2002. Proceedings..

[12]  Michael Molloy,et al.  Cores in random hypergraphs and Boolean formulas , 2005, Random Struct. Algorithms.

[13]  Assaf Naor,et al.  Rigorous location of phase transitions in hard optimization problems , 2005, Nature.

[14]  Dimitris Achlioptas,et al.  THE THRESHOLD FOR RANDOM k-SAT IS 2k log 2 O(k) , 2004, FOCS 2004.

[15]  Amin Coja-Oghlan A Better Algorithm for Random k-SAT , 2010, SIAM J. Comput..

[16]  P. Hall Rates of convergence in the central limit theorem , 1983 .

[17]  Mikko Alava,et al.  Branching Processes , 2009, Encyclopedia of Complexity and Systems Science.

[18]  Yuval Peres,et al.  The threshold for random k-SAT is 2k (ln 2 - O(k)) , 2003, STOC '03.

[19]  D. Aldous,et al.  Processes on Unimodular Random Networks , 2006, math/0603062.

[20]  Nicholas C. Wormald,et al.  The asymptotic distribution of short cycles in random regular graphs , 1981, J. Comb. Theory, Ser. B.

[21]  M. Mézard,et al.  Analytic and Algorithmic Solution of Random Satisfiability Problems , 2002, Science.

[22]  Federico Ricci-Tersenghi,et al.  On the solution-space geometry of random constraint satisfaction problems , 2006, STOC '06.

[23]  Andrea Montanari,et al.  Gibbs states and the set of solutions of random constraint satisfaction problems , 2006, Proceedings of the National Academy of Sciences.

[24]  Andrea Montanari,et al.  Statistical Mechanics and Algorithms on Sparse and Random Graphs , 2014 .

[25]  Alan M. Frieze,et al.  Random graphs , 2006, SODA '06.

[26]  Yuval Peres,et al.  Bootstrap Percolation on Infinite Trees and Non-Amenable Groups , 2003, Combinatorics, Probability and Computing.

[27]  Amin Coja-Oghlan,et al.  Algorithmic Barriers from Phase Transitions , 2008, 2008 49th Annual IEEE Symposium on Foundations of Computer Science.

[28]  E. Friedgut,et al.  Sharp thresholds of graph properties, and the -sat problem , 1999 .

[29]  Béla Bollobás,et al.  A Probabilistic Proof of an Asymptotic Formula for the Number of Labelled Regular Graphs , 1980, Eur. J. Comb..

[30]  N. Linial,et al.  Expander Graphs and their Applications , 2006 .

[31]  S. Kak Information, physics, and computation , 1996 .

[32]  N. Wormald,et al.  Models of the , 2010 .

[33]  Joel H. Spencer,et al.  Sudden Emergence of a Giantk-Core in a Random Graph , 1996, J. Comb. Theory, Ser. B.

[34]  A. Dembo,et al.  Ising models on locally tree-like graphs , 2008, 0804.4726.

[35]  Rémi Monasson,et al.  Determining computational complexity from characteristic ‘phase transitions’ , 1999, Nature.

[36]  Andrea Montanari,et al.  Tight Thresholds for Cuckoo Hashing via XORSAT , 2009, ICALP.

[37]  A. Dembo,et al.  Finite size scaling for the core of large random hypergraphs , 2007, math/0702007.

[38]  M. Mézard,et al.  Two Solutions to Diluted p-Spin Models and XORSAT Problems , 2003 .

[39]  J. Michael Steele,et al.  The Objective Method: Probabilistic Combinatorial Optimization and Local Weak Convergence , 2004 .

[40]  Rüdiger L. Urbanke,et al.  Modern Coding Theory , 2008 .

[41]  R. Monasson,et al.  Rigorous decimation-based construction of ground pure states for spin-glass models on random lattices. , 2002, Physical review letters.

[42]  Daniel A. Spielman,et al.  Efficient erasure correcting codes , 2001, IEEE Trans. Inf. Theory.