Results 141 to 150 of about 979 (183)

Optimality and Duality in Nonsmooth Conic Vector Optimization

Journal of Optimization Theory and Applications, 2019
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Optimality conditions in nonsmooth optimization

Bulletin of the Australian Mathematical Society, 1994
This ``article'' is in fact the abstract of the author's Ph. D. thesis, the main thrust of which is to establish necessary and sufficient optimality conditions for minimization and maximization problems posed on abstract spaces. More particularly, the aim is to extend well-known optimality conditions for finite-dimensional problems, to the infinite ...
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Nonsmooth Optimization Techniques on Riemannian Manifolds

Journal of Optimization Theory and Applications, 2012
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S. Hosseini, M. R. Pouryayevali
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Nonsmooth optimization in optimal control

Proceedings of the 28th IEEE Conference on Decision and Control, 2003
The aim of this study is to apply nonsmooth optimization to optimal control problems. The author describes the main ideas of K.C. Kiwiel's (1985) generalized cutting plane method and to the bundle method due to C. Lemarechal (1977) for generating a descent direction. He presents an optimal control problem which is a model of an elastic deflected string,
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Nonsmooth convex optimization

2007
Dottorato di Ricerca in Ricerca Operativa, Ciclo XX, a.a.
Gorgone, Enrico, Grandinetti, Lucio, Gaudioso, Manlio, Monaco, Maria Flavia   +3 more
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Subgradient Method for Nonconvex Nonsmooth Optimization

Journal of Optimization Theory and Applications, 2012
Based on the notion of quasisecants introduced by \textit{A. M. Bagirov} and \textit{A. N. Ganjehlou} [Optim. Methods Softw. 25, No. 1, 3--18 (2010; Zbl 1202.65072)], the authors develop a version of the subgradient method for solving nonconvex nonsmooth optimization problems. Quasisecants are subgradients computed in some neighborhood of a point.
Adil M. Bagirov, L. Jin, Napsu Karmitsa, A. Al Nuaimat, Napsu Sultanova   +4 more
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Nonsmooth Convex Optimization

2020
In this chapter, we study an extension of the projected subgradient method for minimization of convex and nonsmooth functions, under the presence of computational errors. The problem is described by an objective function and a set of feasible points. For this algorithm, each iteration consists of two steps.
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Computational nonsmooth optimization

Mathematical Programming, 1997
Qi, Liqun, Ruszczynski, Andrzej, Womersley, Robert   +2 more
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Nonsmooth Optimization Algorithms

1996
Quasidifferentiable and codifferentiable optimization algorithms are based on gradientlike, descent, iterative techniques whereas gradient information is replaced by the setvalued quasidifferential or the codifferential. Then the steepest descent finding subproblems are appropriately replaced by quadratic programming subproblems with a polyhedral ...
Vladimir F. Dem’yanov, Georgios E. Stavroulakis, Ludmila N. Polyakova, Panagiotis D. Panagiotopoulos   +3 more
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Nonsmooth Optimization Methods

1999
From the previous chapters we know that after the discretization, elliptic and parabolic hemivariational inequalities can be transformed into substationary point type problems for locally Lipschitz superpotentials and as such will be solved. There is a class of mathematical programming methods especially developed for this type of problems.
Jaroslav Haslinger, Markku Miettinen, Panagiotis D. Panagiotopoulos   +2 more
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