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

Sichong Guan and Shu-Cherng Fang

Mathematical Methods of Operations Research, 1998, vol. 48, issue 3, 287-316

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. Copyright Springer-Verlag Berlin Heidelberg 1998

Keywords: Key words: Linear programming; stochastic programming; Kalman filter; infeasible-interior-point method (search for similar items in EconPapers)
Date: 1998
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Persistent link: https://EconPapers.repec.org/RePEc:spr:mathme:v:48:y:1998:i:3:p:287-316

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DOI: 10.1007/s001860050029

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