TY - GEN
T1 - One-step replica symmetry breaking of random regular NAE-SAT
AU - Nam, Danny
AU - Sly, Allan
AU - Sohn, Youngtak
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - In a broad class of sparse random constraint satisfaction problems (CSP), deep heuristics from statistical physics predict that there is a condensation phase transition before the satisfiability threshold, governed by one-step replica symmetry breaking (1RSB). In fact, in random regular k-NAE-SAT, which is one of such random CSPS, it was verified [1] that its free energy is well-defined and the explicit value follows the 1RSB prediction. However, for any model of sparse random CSP, it has been unknown whether the solution space indeed condensates on O(1) clusters according to the 1RSB prediction. In this paper, we give an affirmative answer to this question for the random regular k-NAE-SAT model. Namely, we prove that with probability close to one, most of the solutions lie inside a bounded number of solution clusters whose sizes are comparable to the scale of the free energy. Furthermore, we establish that the overlap between two independently drawn solutions concentrates precisely at two values. This is the defining property of the one-step replica symmetry breaking class which we establish for the first time in a sparse random CSP, Our proof is based on a detailed moment analysis of a spin system, which has an infinite spin space that encodes the structure of solution clusters. We develop new techniques to study the partition function as well as enhance previous approaches which were only applicable to spin systems with finitely many spins. We believe that our method is applicable to a broad range of random CSPS in the 1RSB universality class.
AB - In a broad class of sparse random constraint satisfaction problems (CSP), deep heuristics from statistical physics predict that there is a condensation phase transition before the satisfiability threshold, governed by one-step replica symmetry breaking (1RSB). In fact, in random regular k-NAE-SAT, which is one of such random CSPS, it was verified [1] that its free energy is well-defined and the explicit value follows the 1RSB prediction. However, for any model of sparse random CSP, it has been unknown whether the solution space indeed condensates on O(1) clusters according to the 1RSB prediction. In this paper, we give an affirmative answer to this question for the random regular k-NAE-SAT model. Namely, we prove that with probability close to one, most of the solutions lie inside a bounded number of solution clusters whose sizes are comparable to the scale of the free energy. Furthermore, we establish that the overlap between two independently drawn solutions concentrates precisely at two values. This is the defining property of the one-step replica symmetry breaking class which we establish for the first time in a sparse random CSP, Our proof is based on a detailed moment analysis of a spin system, which has an infinite spin space that encodes the structure of solution clusters. We develop new techniques to study the partition function as well as enhance previous approaches which were only applicable to spin systems with finitely many spins. We believe that our method is applicable to a broad range of random CSPS in the 1RSB universality class.
KW - condensation phase transition
KW - one-step replica symmetry breaking
KW - random constraint satisfaction problems
KW - random regular NAE-SAT model
UR - http://www.scopus.com/inward/record.url?scp=85127120622&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85127120622&partnerID=8YFLogxK
U2 - 10.1109/FOCS52979.2021.00039
DO - 10.1109/FOCS52979.2021.00039
M3 - Conference contribution
AN - SCOPUS:85127120622
T3 - Proceedings - Annual IEEE Symposium on Foundations of Computer Science, FOCS
SP - 310
EP - 318
BT - Proceedings - 2021 IEEE 62nd Annual Symposium on Foundations of Computer Science, FOCS 2021
PB - IEEE Computer Society
T2 - 62nd IEEE Annual Symposium on Foundations of Computer Science, FOCS 2021
Y2 - 7 February 2022 through 10 February 2022
ER -