Strict convex regularizations, proximal points and augmented lagrangians
Strict convex regularizations, proximal points and augmented lagrangians
Proximal Point Methods (PPM) can be traced to the pioneer works of Moreau [16], Martinet [14, 15] and Rockafellar [19, 20] who used as regularization function the square of the Euclidean norm. In this work, we study PPM in the context of optimization and we derive a class of such methods which contains Rockafellar's result. We also present a less stringent criterion to the acceptance of an approximate solution to the subproblems that arise in the inner loops of PPM. Moreover, we introduce a new...
Strict minimizers of order in nonsmooth optimization problems
In the paper, some sufficient optimality conditions for strict minima of order in constrained nonlinear mathematical programming problems involving (locally Lipschitz) -convex functions of order are presented. Furthermore, the concept of strict local minimizer of order is also used to state various duality results in the sense of Mond-Weir and in the sense of Wolfe for such nondifferentiable optimization problems.
Strong average optimality criterion for continuous-time Markov decision processes
This paper deals with continuous-time Markov decision processes with the unbounded transition rates under the strong average cost criterion. The state and action spaces are Borel spaces, and the costs are allowed to be unbounded from above and from below. Under mild conditions, we first prove that the finite-horizon optimal value function is a solution to the optimality equation for the case of uncountable state spaces and unbounded transition rates, and that there exists an optimal deterministic...
Strong convergence of a new iterative method for infinite family of generalized equilibrium and fixed-point problems of nonexpansive mappings in Hilbert spaces.
Strong convergence theorems for countable Lipschitzian mappings and its applications in equilibrium and optimization problems.
Strongly proper optimums and maximal optimization in multiobjective programming.
Strongly quasiconvex quadratic functions.
Strong-weak Stackelberg Problems in Finite Dimensional Spaces
We are concerned with two-level optimization problems called strongweak Stackelberg problems, generalizing the class of Stackelberg problems in the strong and weak sense. In order to handle the fact that the considered two-level optimization problems may fail to have a solution under mild assumptions, we consider a regularization involving ε-approximate optimal solutions in the lower level problems. We prove the existence of optimal solutions for such regularized problems and present some approximation...
Structure maximale pour la somme des carrés d'une contingence aux marges fixées ; une solution algorithmique programmée
Structure of efficient sets for strictly quasi convex objectives.
Structures algébriques généralisées des problèmes de cheminement dans les graphes
Student absenteeism in engineering colleges: Evaluation of alternatives using AHP.
Study of the stability of the queueing network of Lu-Kumar-Bramson by the fluid limit method. (Étude par la méthode des limites fluides de la stabilité du réseau de file d'attente de Lu-Kumar-Bramson.)
Studying graph - stability to apprehend relative hardness of constructive-non-constructive approximation
Su un particolare problema di allocazione di servizi
Sub differentiability and trustworthiness in the light of a new variational principle of Borwein and Preiss
Subgradient optimization and large scale programming : an application to optimum multicommodity network synthesis with security constraints
Subgraph isomorphism in planar graphs and related problems.
Sufficient condition for Pareto optimization in Banach spaces