Titlebook: New Trends in Mathematical Programming; Homage to Steven Vaj Franco Giannessi,Sándor Komlósi,Tamás Rapcsák Book 1998 Springer Science+Busin

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,On Primal—Dual Path—Following Algorithms for Semidefinite Programming,y. Most of these methods are extensions of linear programming algorithms. The primal-dual central path following method for linear programming by Jansen et al. [6] has recently been extended to semidefinite programming by Jiang [7], utilizing the Nesterov-Todd direction and introducing a new distanc

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Geometrical solution of weighted Fermat problem about triangles,numbers . find in plane a point . minimizing the sum of its distances to ., ., ., multiplied by . respectively. It is shown by simple geometrical methods that, if . satisfy the triangle inequalities and further conditions also involving the angles of the triangle ., then there is one and only one mi

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Programming Under Probabilistic Constraint with Discrete Random Variable,ht-hand side values are random variables, are: the simple recourse model, the probabilistic constrained model and the combination of the two. In this paper we present algorithmic solution to the second and third models under the assumption that the random variables have a discrete joint distribution

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