Adaptive filtering strategy for numerical constraint satisfaction problems

Araya, Ignacio; Soto, Ricardo; Crawford, Broderick

doi:10.1016/j.eswa.2015.06.030

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Implementation Of Domain Reduction Techniques1

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“…Based on insights from Stergiou [181] that propagation events often occur in close clusters, Araya et al [16] propose an adaptive strategy. Thus, if a constraint propagation mechanism succeeds in carrying out domain reduction at a given node, it should be exploited repetitively in other nodes that are geometrically close to the node until the method fails.…”

Section: Implementation Of Domain Reduction Techniquesmentioning

confidence: 99%

Domain reduction techniques for global NLP and MINLP optimization

Puranik

Sahinidis

2017

Constraints

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Optimization solvers routinely utilize presolve techniques, including model simplification, reformulation and domain reduction techniques. Domain reduction techniques are especially important in speeding up convergence to the global optimum for challenging nonconvex nonlinear programming (NLP) and mixedinteger nonlinear programming (MINLP) optimization problems. In this work, we survey the various techniques used for domain reduction of NLP and MINLP optimization problems. We also present a computational analysis of the impact of these techniques on the performance of various widely available global solvers on a collection of 1740 test problems.

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Section: Implementation Of Domain Reduction Techniquesmentioning

confidence: 99%