Bose-Einstein Condensation in Satisfiability Problems

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 graphs starting from a k–SAT instance, in order to analyze them and show whether a Bose-Einstein condensation occurs. We observe that, analogously to complex networks, the networks of k–SAT instances follow Bose statistics and can undergo Bose-Einstein condensation. In particular, k–SAT instances move from a fit-get-rich network to a winner-takes-all network as the ratio of clauses to variables decreases, and the phase transition of k– SAT approximates the critical temperature for the Bose-Einstein condensation. Finally, we employ the fitness-based classification to enhance SAT solvers (e.g., ChainSAT) and obtain the consistently highest performing SAT solver for CNF formulas, and therefore a new class of efficient hardware and software verification tools.