Second order convexity and a modified objective function method in mathematical programming
Tadeusz Antczak (2007)
Control and Cybernetics
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Displaying similar documents to “Random perturbation of the projected variable metric method for nonsmooth nonconvex optimization problems with linear constraints”
Second order convexity and a modified objective function method in mathematical programming
Nonessential objective functions in linear multiobjective optimization problems
Agnieszka Malinowska (2006)
Control and Cybernetics
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A comparative study on optimization methods for the constrained nonlinear programming problems.
Yeniay, Ozgur (2005)
Mathematical Problems in Engineering
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Composite semi-infinite optimization
Darinka Dentcheva, Andrzej Ruszczyński (2007)
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The corridor method: a dynamic programming inspired metaheuristic
Moshe Sniedovich, Stefan Voß (2006)
Control and Cybernetics
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Interactive multi-objective optimization for simulated moving bed processes
Jussi Hakanen, Yoshiaki Kawajiri, Kaisa Miettinen, Lorenz Biegler (2007)
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A Global Stochastic Optimization Method for Large Scale Problems
W. El Alem, A. El Hami, R. Ellaia (2010)
Mathematical Modelling of Natural Phenomena
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In this paper, a new hybrid simulated annealing algorithm for constrained global optimization is proposed. We have developed a stochastic algorithm called ASAPSPSA that uses Adaptive Simulated Annealing algorithm (ASA). ASA is a series of modifications to the basic simulated annealing algorithm (SA) that gives the region containing the global solution of an objective function. In addition, Simultaneous Perturbation Stochastic Approximation...
Open problems in nonlinear conjugate gradient algorithms for unconstrained optimization.
Andrei, Neculai (2011)
Bulletin of the Malaysian Mathematical Sciences Society. Second Series
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Sufficient Second Order Optimality Conditions for C^1 Multiobjective Optimization Problems
Gadhi, N. (2003)
Serdica Mathematical Journal
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2000 Mathematics Subject Classification: Primary 90C29; Secondary 90C30. In this work, we use the notion of Approximate Hessian introduced by Jeyakumar and Luc [19], and a special scalarization to establish sufficient optimality conditions for constrained multiobjective optimization problems. Throughout this paper, the data are assumed to be of class C^1, but not necessarily of class C^(1.1).