A CONSTANT-POTENTIAL INFEASIBLE-START INTERIOR-POINT ALGORITHM WITH COMPUTATIONAL EXPERIMENTS AND APPLICATIONS

We present a constant-potential infeasible-start interior-point (INFCP ) algorithm for linear programming (LP) problems with a worst-case ite ration complexity analysis as well as some computational results. The performance of the INFCP algorithm is compared to those of practical i nterior-point algorithms. New features of the algorithm include a heur istic method for computing a ''good'' starting point and a procedure f or solving the augmented system arising from stochastic programming wi th simple recourse. We also present an application to large scale plan ning problems under uncertainty.