A Numerically stable reduced-gradient type algorithm for solving large-scale linearly constrained minimization problems

Abstract In this paper we present a reduced-gradient type algorithm for solving large-scale linearly constrained minimization problems. During each iteration of the algorithm linear systems are solved using a preconditioned conjugate-gradient scheme. The preconditioning scheme uses orthogonal transformations, thus providing numerical stability. The total storage used by the algorithm may be predicted before beginning the calculations. We present some numerical experiments which confirm the reliability of the algorithm.

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