Sensitivity analysis in multi-parametric strictly convex quadratic optimization

B. Kheirfam

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Displaying similar documents to “Sensitivity analysis in multi-parametric strictly convex quadratic optimization”

PDI&PDE-constrained optimization problems with curvilinear functional quotients as objective vectors.

Pitea, Ariana, Udrişte, Constantin, Mititelu, Ştefan (2009)

Balkan Journal of Geometry and its Applications (BJGA)

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An optimality system for finite average Markov decision chains under risk-aversion

Alfredo Alanís-Durán, Rolando Cavazos-Cadena (2012)

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This work concerns controlled Markov chains with finite state space and compact action sets. The decision maker is risk-averse with constant risk-sensitivity, and the performance of a control policy is measured by the long-run average cost criterion. Under standard continuity-compactness conditions, it is shown that the (possibly non-constant) optimal value function is characterized by a system of optimality equations which allows to obtain an optimal stationary policy. Also, it is shown...

Variational principles for set-valued mappings with applications to multiobjective optimization

Truong Bao, Boris Mordukhovich (2007)

Control and Cybernetics

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Chance constrained problems: penalty reformulation and performance of sample approximation technique

Martin Branda (2012)

Kybernetika

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We explore reformulation of nonlinear stochastic programs with several joint chance constraints by stochastic programs with suitably chosen penalty-type objectives. We show that the two problems are asymptotically equivalent. Simpler cases with one chance constraint and particular penalty functions were studied in [6,11]. The obtained problems with penalties and with a fixed set of feasible solutions are simpler to solve and analyze then the chance constrained programs. We discuss solving...

Computing minimum norm solution of a specific constrained convex nonlinear problem

Saeed Ketabchi, Hossein Moosaei (2012)

Kybernetika

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The characterization of the solution set of a convex constrained problem is a well-known attempt. In this paper, we focus on the minimum norm solution of a specific constrained convex nonlinear problem and reformulate this problem as an unconstrained minimization problem by using the alternative theorem.The objective function of this problem is piecewise quadratic, convex, and once differentiable. To minimize this function, we will provide a new Newton-type method with global convergence...