Constraint optimization and landscapes

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dc.creatorKrzakala, Florent
dc.creatorKurchan, Jorge
dc.date2007-09-07
dc.date.accessioned2026-07-07T10:04:46Z
dc.date.available2026-07-07T10:04:46Z
dc.descriptionWe describe an effective landscape introduced in [1] for the analysis of Constraint Satisfaction problems, such as Sphere Packing, K-SAT and Graph Coloring. This geometric construction reexpresses these problems in the more familiar terms of optimization in rugged energy landscapes. In particular, it allows one to understand the puzzling fact that unsophisticated programs are successful well beyond what was considered to be the `hard' transition, and suggests an algorithm defining a new, higher, easy-hard frontier.
dc.descriptionContribution to STATPHYS23
dc.identifierhttps://arxiv.org/abs/0709.1023
dc.identifierhttp://arxiv.org/abs/0709.1023
dc.identifierEur. Phys. J. B 64, 563-565 (2008)
dc.identifierdoi:10.1140/epjb/e2008-00052-x
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/169798
dc.subjectQuantum Physics
dc.subjectStatistical Mechanics
dc.subjectComputational Complexity
dc.subjectAdaptation and Self-Organizing Systems
dc.titleConstraint optimization and landscapes
dc.typetext

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