Failed Value Consistencies for Constraint Satisfaction

Lecoutre, Christophe; Roussel, Olivier

doi:10.1007/978-3-642-04244-7_44

Cited by 1 publication

(1 citation statement)

References 17 publications

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“…For a given upper bound, these effects are obtained without changing the global energy distribution. Compared to Local Consistency enforcing, DEE, which has also been studied in CFN under the name of substitutability does not preserve the global energy distribution as it may remove feasible suboptimal solutions. In CFN, the pruning and non‐naïve lower bounding based on

c_{normal∅}

is done incrementally at each visited node during DFBB, using various branching schemes, variable and value ordering schemes as well as specialized upper bounding algorithms.…”

Section: Solving the Optimization And Enumeration Of Cpd Problemsmentioning

confidence: 99%

Fast search algorithms for computational protein design

et al. 2016

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One of the main challenges in Computational Protein Design (CPD) is the huge size of the protein sequence and conformational space that has to be computationally explored. Recently, we showed that state-of-the-art combinatorial optimization technologies based on Cost Function Network (CFN) processing allow to speed up provable rigid backbone protein design methods by several orders of magnitudes. Building up on this, we improved and injected CFN technology into the well-established CPD package Osprey to allow all Osprey CPD algorithms to benefit from associated speedups. Because Osprey fundamentally relies on the ability of A* to produce conformations in increasing order of energy, we defined new A* strategies combining CFN lower bounds, with new Side Chain Positioning (SCP)–based branching scheme. Beyond the speedups obtained in the new A*-CFN combination, this new branching scheme enables a much faster enumeration of sub-optimal sequences, far beyond what is reachable without it. Together with the immediate and important speedups provided by CFN technology, these developments directly benefit to all the algorithms that previously relied on the DEE/A* combination inside Osprey and make it possible to solve larger CPD problems with provable algorithms.

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c_{normal∅}

is done incrementally at each visited node during DFBB, using various branching schemes, variable and value ordering schemes as well as specialized upper bounding algorithms.…”

Section: Solving the Optimization And Enumeration Of Cpd Problemsmentioning

confidence: 99%