Soft arc consistency revisited

Cooper, Martin; Givry, Simon de; Sánchez, Martí; Schiex, Thomas; Zytnicki, Matthias; Werner, Tomáš

doi:10.1016/j.artint.2010.02.001

Cited by 117 publications

(207 citation statements)

References 33 publications

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“…After DEE pruning, the A * algorithm allows to expand a sequence-conformation tree, so that sequence-conformations are extracted and sorted on the basis of their energy values. At depth d of the tree, the lower bound used by A * [13] is exactly the PFC-DAC lower bound [37,23] used in WCSP and later obsoleted by soft arc consistencies [34,22,5]:…”

Section: Existing Approaches For the Cpdmentioning

confidence: 99%

“…The continuous relaxation of this 0/1 linear programming model is known do be the dual of the LP problem encoded by Optimal Soft Arc Consistency [6,5]. When the upper bound k is infinite, OSAC is known to be stronger than any other soft "arc level" arc consistency and especially stronger than the default Existential Directional Arc Consistency (EDAC) [22] used in toulbar2.…”

Section: Integer Linear Programming Modelmentioning

confidence: 99%

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Computational Protein Design as a Cost Function Network Optimization Problem

Allouche¹,

Traoré

André

et al. 2012

Lecture Notes in Computer Science

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Abstract. Proteins are chains of simple molecules called amino acids. The three-dimensional shape of a protein and its amino acid composition define its biological function. Over millions of years, living organisms have evolved and produced a large catalog of proteins. By exploring the space of possible amino-acid sequences, protein engineering aims at similarly designing tailored proteins with specific desirable properties. In Computational Protein Design (CPD), the challenge of identifying a protein that performs a given task is defined as the combinatorial optimization problem of a complex energy function over amino acid sequences. In this paper, we introduce the CPD problem and some of the main approaches that have been used to solve it. We then show how this problem directly reduces to Cost Function Network (CFN) and 0/1LP optimization problems. We construct different real CPD instances to evaluate CFN and 0/1LP algorithms as implemented in the toulbar2 and cplex solvers. We observe that CFN algorithms bring important speedups compared to the CPD platform osprey but also to cplex.

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Section: Existing Approaches For the Cpdmentioning

confidence: 99%

Section: Integer Linear Programming Modelmentioning

confidence: 99%

Computational Protein Design as a Cost Function Network Optimization Problem

Allouche¹,

Traoré

André

et al. 2012

Lecture Notes in Computer Science

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show abstract

“…However finding such ϕ appears more difficult: e.g. the most efficient dual block-coordinate ascent solvers [20,12,8,33] only have convergence guarantees (see [20,33]) allowing to find a sub-optimal solution, satisfying arc consistency. An optimal dual solution need not be arc consistent, but it can be reparametrized without loss of optimality to enforce arc consistency [49].…”

Section: Expressing O *mentioning

confidence: 99%

Maximum Persistency via Iterative Relaxed Inference in Graphical Models

Shekhovtsov¹,

Swoboda²,

Savchynskyy³

2018

IEEE Trans. Pattern Anal. Mach. Intell.

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Abstract-We consider the NP-hard problem of MAP-inference for undirected discrete graphical models. We propose a polynomial time and practically efficient algorithm for finding a part of its optimal solution. Specifically, our algorithm marks some labels of the considered graphical model either as (i) optimal, meaning that they belong to all optimal solutions of the inference problem; (ii) non-optimal if they provably do not belong to any solution. With access to an exact solver of a linear programming relaxation to the MAP-inference problem, our algorithm marks the maximal possible (in a specified sense) number of labels. We also present a version of the algorithm, which has access to a suboptimal dual solver only and still can ensure the (non-)optimality for the marked labels, although the overall number of the marked labels may decrease. We propose an efficient implementation, which runs in time comparable to a single run of a suboptimal dual solver. Our method is well-scalable and shows state-of-the-art results on computational benchmarks from machine learning and computer vision.

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“…Adapting weights for improving propagation has also been applied in the context of Valued Constraint Satisfaction Problems [6]. In that work, a linear programming model is proposed for computing Optimal Soft Arc Consistency, but Lagrangian relaxations are not used.…”

Section: Related Workmentioning

confidence: 99%

Improved Constraint Propagation via Lagrangian Decomposition

Bergman

Ciré

Hoeve

2015

Lecture Notes in Computer Science

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Always cite the published version, so the author(s) will receive recognition through services that track citation counts, e.g. Scopus. If you need to cite the page number of the author manuscript from TSpace because you cannot access the published version, then cite the TSpace version in addition to the published version using the permanent URI (handle) found on the record page.This article was made openly accessible by U of T Faculty. Please tell us how this access benefits you. Your story matters. Improved Constraint Propagation viaLagrangian Decomposition Abstract. Constraint propagation is inherently restricted to the local information that is available to each propagator. We propose to improve the communication between constraints by introducing Lagrangian penalty costs between pairs of constraints, based on the Lagrangian decomposition scheme. The role of these penalties is to force variable assignments in each of the constraints to correspond to one another. We apply this approach to constraints that can be represented by decision diagrams, and show that propagating Lagrangian cost information can help improve the overall bound computation as well as the solution time.

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Soft arc consistency revisited

Cited by 117 publications

References 33 publications

Computational Protein Design as a Cost Function Network Optimization Problem

Computational Protein Design as a Cost Function Network Optimization Problem

Maximum Persistency via Iterative Relaxed Inference in Graphical Models

Improved Constraint Propagation via Lagrangian Decomposition

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