dc.creatorLi, Jin
dc.creatorTan, Yuejin
dc.creatorLiao, Liangcai
dc.date2007-03-09T19:23:39Z
dc.date2010-09-07T16:40:15Z
dc.date2011-03-10T14:33:29Z
dc.date2007-03-09T19:23:39Z
dc.date2010-09-07T16:40:15Z
dc.date2011-03-10T14:33:29Z
dc.date2007-03-09T19:23:39Z
dc.identifierhttp://bibdigital.epn.edu.ec/handle/15000/9322
dc.descriptionAn efficient optimization algorithm based on reduced sequential quadratic programming (rSQP) and automatic differentiation (AD) is presented in this paper. With the characteristics of sparseness, relatively low degrees of freedom and equality constraints utilized, the nonlinear programming problem was solved by improved rSQP solver. In the solving process, AD technology was used to obtain accurate gradient information. The numerical results show that the combined algorithm, which is suitable for large-scale process optimization problems, can calculate more efficiently than rSQP itself.
dc.languageeng
dc.rightsopenAccess
dc.rightshttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectALGORITMOS
dc.subjectOPTIMIZACIÓN
dc.subjectALGORITHMS
dc.subjectOPTIMIZATION
dc.titleA New Algorithm Based on rSQP and AD
dc.typeArtículos de revistas


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