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Random sampling-based automatic parameter tuning for nonlinear programming solvers / Weifeng Chen in Industrial & engineering chemistry research, Vol. 50 N° 7 (Avril 2011) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 50 N° 7 (Avril 2011) . - pp. 3907–3918 Titre : Random sampling-based automatic parameter tuning for nonlinear programming solvers Type de document : texte imprimé Auteurs : Weifeng Chen, Auteur ; Zhijiang Shao, Auteur ; Kexin Wang, Auteur Année de publication : 2011 Article en page(s) : pp. 3907–3918 Note générale : Chimie industrielle Langues : Anglais (eng) Mots-clés : Nonlinear  programming  solver Résumé : Nonlinear programming solvers play important roles in process systems engineering. The performance of a nonlinear programming solver is influenced significantly by values of the solver parameters. Hence, tuning these parameters can enhance the performance of the nonlinear programming solver, especially for hard problems and time-critical applications like real time optimization and nonlinear model predictive control. Random sampling (RS) algorithm is utilized to tune the nonlinear programming solver for solving hard problems. By introducing an iterated search technique, heuristic rules and advanced termination criteria, an enhanced random sampling algorithm is developed to determine parameter configuration that work significantly better than the default. These random sampling-based methods can handle all kinds of parameters (e.g., categorical, integer, and continuous) of the nonlinear programming solver. Numerical results with parameter configurations from the proposed random sampling-based methods show remarkable performance improvement compared with the defaults. Future works toward the development and improvement of the automatic parameter-tuning tool for the nonlinear programming solvers are also outlined. DEWEY : 660 ISSN : 0888-5885 En ligne : http://pubs.acs.org/doi/abs/10.1021/ie100826y [article] Random sampling-based automatic parameter tuning for nonlinear programming solvers [texte imprimé] / Weifeng Chen, Auteur ; Zhijiang Shao, Auteur ; Kexin Wang, Auteur . - 2011 . - pp. 3907–3918. Chimie industrielle Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 50 N° 7 (Avril 2011) . - pp. 3907–3918 Mots-clés : Nonlinear  programming  solver Résumé : Nonlinear programming solvers play important roles in process systems engineering. The performance of a nonlinear programming solver is influenced significantly by values of the solver parameters. Hence, tuning these parameters can enhance the performance of the nonlinear programming solver, especially for hard problems and time-critical applications like real time optimization and nonlinear model predictive control. Random sampling (RS) algorithm is utilized to tune the nonlinear programming solver for solving hard problems. By introducing an iterated search technique, heuristic rules and advanced termination criteria, an enhanced random sampling algorithm is developed to determine parameter configuration that work significantly better than the default. These random sampling-based methods can handle all kinds of parameters (e.g., categorical, integer, and continuous) of the nonlinear programming solver. Numerical results with parameter configurations from the proposed random sampling-based methods show remarkable performance improvement compared with the defaults. Future works toward the development and improvement of the automatic parameter-tuning tool for the nonlinear programming solvers are also outlined. DEWEY : 660 ISSN : 0888-5885 En ligne : http://pubs.acs.org/doi/abs/10.1021/ie100826y