The system of generalized set-valued equilibrium problems.
Peng, Jian-Wen (2006)
Journal of Inequalities and Applications [electronic only]
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Peng, Jian-Wen (2006)
Journal of Inequalities and Applications [electronic only]
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Peng, Jian-Wen, Zheng, Xiao-Ping (2007)
Journal of Inequalities and Applications [electronic only]
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Wang, Liang-Cheng, Ma, Xiu-Fen, Liu, Li-Hong (2009)
JIPAM. Journal of Inequalities in Pure & Applied Mathematics [electronic only]
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Jean-Charles Régin, Michel Rueher (2005)
RAIRO - Operations Research - Recherche Opérationnelle
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This paper introduces a new method to prune the domains of the variables in constrained optimization problems where the objective function is defined by a sum , and where the integer variables are subject to difference constraints of the form . An important application area where such problems occur is deterministic scheduling with the mean flow time as optimality criteria. This new constraint is also more general than a sum constraint defined on a set of ordered variables. Classical...
Chatfield, J.A. (1978)
International Journal of Mathematics and Mathematical Sciences
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Cheung, Wing-Sum, Zhao, Chang-Jian (2007)
JIPAM. Journal of Inequalities in Pure & Applied Mathematics [electronic only]
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Demetrovics, Janos, Thi, Vu Duc, Giang, Nguyen Long (2013)
Serdica Journal of Computing
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There are limitations in recent research undertaken on attribute reduction in incomplete decision systems. In this paper, we propose a distance-based method for attribute reduction in an incomplete decision system. In addition, we prove theoretically that our method is more effective than some other methods.
Peng, Jianwen, Yang, Xinmin (2005)
International Journal of Mathematics and Mathematical Sciences
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Ujević, Nenad (2005)
International Journal of Mathematics and Mathematical Sciences
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Ivanka Tr. Angelova, Lubin G. Vulkov (2007)
Kragujevac Journal of Mathematics
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Zhongxue, Lü, Hongzheng, Xie (2002)
International Journal of Mathematics and Mathematical Sciences
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Ujević, Nenad (2004)
The New York Journal of Mathematics [electronic only]
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Marek Smietanski (2008)
Open Mathematics
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An algorithm for univariate optimization using a linear lower bounding function is extended to a nonsmooth case by using the generalized gradient instead of the derivative. A convergence theorem is proved under the condition of semismoothness. This approach gives a globally superlinear convergence of algorithm, which is a generalized Newton-type method.