9 research outputs found
Analysing Survey Propagation Guided Decimationon Random Formulas
Let be a uniformly distributed random -SAT formula with
variables and clauses. For clauses/variables ratio the formula is satisfiable with high
probability. However, no efficient algorithm is known to provably find a
satisfying assignment beyond with a non-vanishing
probability. Non-rigorous statistical mechanics work on -CNF led to the
development of a new efficient "message passing algorithm" called \emph{Survey
Propagation Guided Decimation} [M\'ezard et al., Science 2002]. Experiments
conducted for suggest that the algorithm finds satisfying assignments
close to . However, in the present paper we prove that the
basic version of Survey Propagation Guided Decimation fails to solve random
-SAT formulas efficiently already for
with almost a factor below
.Comment: arXiv admin note: substantial text overlap with arXiv:1007.1328 by
other author
The condensation phase transition in random graph coloring
Based on a non-rigorous formalism called the "cavity method", physicists have
put forward intriguing predictions on phase transitions in discrete structures.
One of the most remarkable ones is that in problems such as random -SAT or
random graph -coloring, very shortly before the threshold for the existence
of solutions there occurs another phase transition called "condensation"
[Krzakala et al., PNAS 2007]. The existence of this phase transition appears to
be intimately related to the difficulty of proving precise results on, e.g.,
the -colorability threshold as well as to the performance of message passing
algorithms. In random graph -coloring, there is a precise conjecture as to
the location of the condensation phase transition in terms of a distributional
fixed point problem. In this paper we prove this conjecture for exceeding a
certain constant
On structural and algorithmic bounds in random constraint satisfaction problems
Random constraint satisfaction problems have been on the agenda of various sciences such as discrete mathematics, computer science, statistical physics and a whole series of additional areas of application since the 1990s at least. The objective is to find a state of a system, for instance an assignment of a set of variables, satisfying a bunch of constraints. To understand the computational hardness as well as the underlying random discrete structures of these problems analytically and to develop efficient algorithms that find optimal solutions has triggered a huge amount of work on random constraint satisfaction problems up to this day. Referring to this context in this thesis we present three results for two random constraint satisfaction problems. ..
The condensation phase transition in random graph coloring
Based on a non-rigorous formalism called the “cavity method”, physicists have made intriguing predictions on phase transitions in discrete structures. One of the most remarkable ones is that in problems such as random k-SAT or random graph k-coloring, very shortly before the threshold for the existence of solutions there occurs another phase transition called condensation [Krzakala et al., PNAS 2007]. The existence of this phase transition seems to be intimately related to the difficulty of proving precise results on, e. g., the k-colorability threshold as well as to the performance of message passing algorithms. In random graph k-coloring, there is a precise conjecture as to the location of the condensation phase transition in terms of a distributional fixed point problem. In this paper we prove this conjecture, provided that k exceeds a certain constant k0