A global-filtering algorithm for linear programming problems with stochastic elements

A global-filtering algorithm for linear programming problems with stochastic elements

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Article ID: iaor2000528
Country: Germany
Volume: 48
Issue: 3
Start Page Number: 287
End Page Number: 316
Publication Date: Jan 1998
Journal: Mathematical Methods of Operations Research (Heidelberg)
Authors: ,
Keywords: programming: linear
Abstract:

In this paper, an interior-point based global filtering algorithm is proposed to solve linear programming problems with the right-hand-side and cost vectors being stochastic. Previous results on the limiting properties of the Kalman filtering process have been extended to handle some non-stationary situations. A global Kalman filter, across all iterations of the interior-point method, is designed to filter out noises while improving the objective value and reducing the primal and dual infeasibilities. Under appropriate assumptions, the proposed algorithm is shown to be globally convergent to an optimal solution of the underlying ‘true value’ system.

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