Integrated experimental design and nonlinear optimization to handle computationally expensive models under resource constraints

عنوان Integrated experimental design and nonlinear optimization to handle computationally expensive models under resource constraints
نویسنده Pinter, Janos D., Horvath, Z.
تاریخ انتشار: 2013-09
محل انتشار - Springer Science+Business Media
موضوع Nonlinear optimization under resource constraints, Metamodel, Experimental design, Latin hypercube design (LHD), Regularly spaced sampling LHD strategies, RSS software implementation, LGO solver suite for nonlinear optimization, MathOptimizer Professional (LGO linked to Mathematica), Illustrative numerical results
نوع دوره ای
زبان انگلیسی
دیجیتال بله
نسخه خطی خیر
کتابخانه: دانشگاه اوزیغین
شناسه دارایی کتابخانه 1573-2916
شماره ثبت 21b24880-59c9-487f-b1bd-2675edddb2f3
محل کتابخانه Industrial Engineering
تاریخ 2013-09
یادداشت‌ها Due to copyright restrictions, the access to the full text of this article is only available via subscription.
متن نمونه In many real-world applications of optimization, the underlying descriptive system model is defined by computationally expensive functions: simulation modules, numerical models and other “black box” model components are typical examples. In such cases, the model development and optimization team often has to rely on optimization carried out under severe resource constraints. To address this important issue, recently a Regularly Spaced Sampling (RSS) module has been added to the Lipschitz Global Optimizer (LGO) solver suite. RSS generates non-collapsing space filling designs, and produces corresponding solution estimates: this information is passed along to LGO for refinement within the given resource (function evaluation and/or runtime) limitations. Obviously, the quality of the solution obtained will essentially depend both on model instance difficulty and on the admissible computational effort. In spite of this general caveat, our results based on solving a selection of non-trivial global optimization test problems suggest that even a moderate amount of well-placed sampling effort enhanced by limited optimization can lead at least to reasonable or even to high quality results. Our numerical tests also indicate that LGO’s overall efficiency is often increased by using RSS as a presolver, both in resource-constrained and in completed LGO runs.
DOI 10.1007/s10898-012-9882-7
Cilt 57
مشاهده در منبع دانشگاه اوزیغین دانشگاه اوزیغین - موتور جستجوی نسخه های خطی عثمانی
دانشگاه اوزیغین - موتور جستجوی نسخه های خطی عثمانی دانشگاه اوزیغین

Integrated experimental design and nonlinear optimization to handle computationally expensive models under resource constraints

نویسنده Pinter, Janos D., Horvath, Z.
تاریخ انتشار 2013-09
محل انتشار - Springer Science+Business Media
موضوع Nonlinear optimization under resource constraints, Metamodel, Experimental design, Latin hypercube design (LHD), Regularly spaced sampling LHD strategies, RSS software implementation, LGO solver suite for nonlinear optimization, MathOptimizer Professional (LGO linked to Mathematica), Illustrative numerical results
نوع دوره ای
زبان انگلیسی
دیجیتال بله
نسخه خطی خیر
کتابخانه دانشگاه اوزیغین
شناسه دارایی کتابخانه 1573-2916
شماره ثبت 21b24880-59c9-487f-b1bd-2675edddb2f3
محل کتابخانه Industrial Engineering
تاریخ 2013-09
یادداشت‌ها Due to copyright restrictions, the access to the full text of this article is only available via subscription.
متن نمونه In many real-world applications of optimization, the underlying descriptive system model is defined by computationally expensive functions: simulation modules, numerical models and other “black box” model components are typical examples. In such cases, the model development and optimization team often has to rely on optimization carried out under severe resource constraints. To address this important issue, recently a Regularly Spaced Sampling (RSS) module has been added to the Lipschitz Global Optimizer (LGO) solver suite. RSS generates non-collapsing space filling designs, and produces corresponding solution estimates: this information is passed along to LGO for refinement within the given resource (function evaluation and/or runtime) limitations. Obviously, the quality of the solution obtained will essentially depend both on model instance difficulty and on the admissible computational effort. In spite of this general caveat, our results based on solving a selection of non-trivial global optimization test problems suggest that even a moderate amount of well-placed sampling effort enhanced by limited optimization can lead at least to reasonable or even to high quality results. Our numerical tests also indicate that LGO’s overall efficiency is often increased by using RSS as a presolver, both in resource-constrained and in completed LGO runs.
DOI 10.1007/s10898-012-9882-7
Cilt 57
دانشگاه اوزیغین - موتور جستجوی نسخه های خطی عثمانی
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