activity
19962003
most citedApproximate analysis of search algorithms with "physical" methods

14 citations · 15 across the 3 of their papers we have counts for

collaborators

6 papers

cs.CC200314 cited

Approximate analysis of search algorithms with "physical" methods

Simona Cocco, Remi Monasson, Andrea Montanari +1

An overview of some methods of statistical physics applied to the analysis of algorithms for optimization problems (satisfiability of Boolean constraints, vertex cover of graphs, d…

cond-mat.stat-mech20021 cited

Restart method and exponential acceleration of random 3-SAT instances resolutions: a large deviation analysis of the Davis-Putnam-Loveland-Logemann algorithm

S. Cocco, R. Monasson

The analysis of the solving complexity of random 3-SAT instances using the Davis-Putnam-Loveland-Logemann (DPLL) algorithm slightly below threshold is presented. While finding a so…

cond-mat.soft2002

Theoretical models for single-molecule DNA and RNA experiments: from elasticity to unzipping

S. Cocco, J. F. Marko, R. Monasson

We review statistical-mechanical theories of single-molecule micromanipulation experiments on nucleic acids. First, models for describing polymer elasticity are introduced. We then…

cond-mat.stat-mech2001

Statistical mechanics methods and phase transitions in optimization problems

O. C. Martin, R. Monasson, R. Zecchina

Recently, it has been recognized that phase transitions play an important role in the probabilistic analysis of combinatorial optimization problems. However, there are in fact many…

cond-mat.dis-nn1999

2+p-SAT: Relation of Typical-Case Complexity to the Nature of the Phase Transition

R. Monasson, R. Zecchina, S. Kirkpatrick +2

Heuristic methods for solution of problems in the NP-Complete class of decision problems often reach exact solutions, but fail badly at "phase boundaries", across which the decisio…

cond-mat1996

Learning and generalization theories of large committee--machines

Remi Monasson, Riccardo Zecchina

The study of the distribution of volumes associated to the internal representations of learning examples allows us to derive the critical learning capacity ($α_c=\frac{16}π \sqrt{\…