Domain reduction techniques for global NLP and MINLP optimization View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2017-07

AUTHORS

Yash Puranik, Nikolaos V. Sahinidis

ABSTRACT

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 mixed-integer 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. More... »

PAGES

338-376

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s10601-016-9267-5

DOI

http://dx.doi.org/10.1007/s10601-016-9267-5

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1008995915


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