106 research outputs found

    Linear programming with matrix variables

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    AbstractLinear programming is formulated with the vector variable replaced by a matrix variable, with the inner product defined using trace of a matrix. The theorems of Motzkin, Farkas (both homogeneous and inhomogeneous forms), and linear programming duality thus extend to matrix variables. Duality theorems for linear programming over complex spaces, and over quaternion spaces, follow as special cases

    Asymptotic Expansions for Stationary Distributions of Perturbed Semi-Markov Processes

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    New algorithms for computing of asymptotic expansions for stationary distributions of nonlinearly perturbed semi-Markov processes are presented. The algorithms are based on special techniques of sequential phase space reduction, which can be applied to processes with asymptotically coupled and uncoupled finite phase spaces.Comment: 83 page

    Overview of the JET results in support to ITER

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    Invex functions and constrained local minima

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    Optimal control and invexity

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    A generalization of Lagrange multipliers: Corrigendum

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    A duality theorem for a nondifferentiable nonlinear fractional programming problem

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    Lagrangean conditions and quasiduality

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