A potential reduction algorithm is proposed for optimization of a convex function sub - ject to linear constraints. At each step of the algorithm, a system of linear equations is solved to get a serch direction and the Armijo's rule is used to determine a stepsize. It is proved that the algorithm is globally convergent. Computational results are reported.
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