,PRIMAL PERTURBATION SIMPLEX ALGORITHMS FOR LINEAR PROGRAMMING

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In this paper, we propose two new perturbation simplex variants. Solving linear programming problems without introducing artificial variables, each of the two uses the dual pivot rule to achieve primal feasibility, and then the primal pivot rule to achieve optimality. The second algorithm, a modification of the first, is designed to handle highly degenerate problems more etficiently. Some interesting results conceing merit of the perturbation are established. Numerical results from preliminary tests are also reported.
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