Sequential penalty quadratic programming filter methods for nonlinear programming |
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| Affiliation: | 1. Dipartimento di Matematica e Applicazioni, Università degli Studi di Napoli “Federico II”, Via Cintia, I-80126 Napoli, Italy;2. C.N.R. National Research Council of Italy, Institute for Computational Application “Mauro Picone”, Via P. Castellino, 111, 80131 Napoli, Italy;1. School of Mathematics and Computer Science, Longyan University, Longyan, 364012, PR China;2. Center for Discrete Mathematics, Fuzhou University, Fuzhou, Fujian, 350003, PR China;1. Department of Chemical Engineering, Babeș-Bolyai University, Arany János Street 11, Cluj-Napoca, Romania;2. Institute of Chemical and Process Engineering, University of Pannonia, Egyetem Street 10, Veszprém, Hungary;1. College of Science, Harbin Engineering University, Harbin 150001, PR China;2. College of Automation, Harbin Engineering University, Harbin 150001, PR China;3. School of Science, Harbin Institute of Technology, Harbin 150001, PR China;4. College of Computer Science and Technology, Harbin Engineering University, Harbin 150001, PR China;1. School of Mathematics and Statistics, Yunnan University, Kunming, Yunnan, 650091, PR China;2. Beijing Computational Science Research Center, Beijing, 100094, PR China;3. School of Mathematics and Computer Science, Guizhou Normal University, Guiyang, Guizhou, 550001, PR China |
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| Abstract: | Filter approaches, initially proposed by Fletcher and Leyffer in 2002, are recently attached importance to. If the objective function value or the constraint violation is reduced, this step is accepted by a filter, which is the basic idea of the filter. In this paper, the filter approach is employed in a sequential penalty quadratic programming (SlQP) algorithm which is similar to that of Yuan's. In every trial step, the step length is controlled by a trust region radius. In this work, our purpose is not to reduce the objective function and constraint violation. We reduce the degree of constraint violation and some function, and the function is closely related to the objective function. This algorithm requires neither Lagrangian multipliers nor the strong decrease condition. Meanwhile, in our SlQP filter there is no requirement of large penalty parameter. This method produces K-T points for the original problem. |
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