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

Name: Integrated experimental design and nonlinear optimization to handle computationally expensive models under resource constraints
Author: Pinter, Janos D., Horvath, Z.

İsim	Integrated experimental design and nonlinear optimization to handle computationally expensive models under resource constraints
Yazar	Pinter, Janos D., Horvath, Z.
Basım Tarihi:	2013-09
Basım Yeri	- Springer Science+Business Media
Konu	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
Tür	Süreli Yayın
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	1573-2916
Kayıt Numarası	21b24880-59c9-487f-b1bd-2675edddb2f3
Lokasyon	Industrial Engineering
Tarih	2013-09
Notlar	Due to copyright restrictions, the access to the full text of this article is only available via subscription.
Örnek Metin	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

Kaynağa git Özyeğin Üniversitesi

Aramaya Dön

Özyeğin Üniversitesi

Kaynağa git

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

Yazar Pinter, Janos D., Horvath, Z.

Basım Tarihi 2013-09

Basım Yeri - Springer Science+Business Media

Konu 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

Tür Süreli Yayın

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 1573-2916

Kayıt Numarası 21b24880-59c9-487f-b1bd-2675edddb2f3

Lokasyon Industrial Engineering

Tarih 2013-09

Notlar Due to copyright restrictions, the access to the full text of this article is only available via subscription.

Örnek Metin 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