A Constant-Potential Infeasible-Start Interior-Point Algorithm with Computational Experiments and Applications

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We present a constant-potential infeasible-start interior-point (INFCP)) algorithm for linear programming (LP)) problems with a worst-case iteration complexity analysis as well as some computational results.The performance of the INFCP algorithm is compared to those of practical interior-point algorithms. New features of the algorithm include a heuristic method for computing a “good” starting point and a procedure for solving the augmented system arising from stochastic programming with simple recourse. We also present an application to large scale planning problems under uncertainty.

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