Predictor-corrector interior-point algorithm for linearly constrained convex programming

来源期刊：中南大学学报(英文版)2001年第3期

论文作者：LIANG Xi-ming

文章页码：208 - 212

Key words：linearly constrained convex programming; predictor-corrector interior-point algorithm; numerical experiment

Abstract: Active set method and gradient projection method are currently the main approaches for linearly constrained convex programming. Interior-point method is one of the most effective choices for linear programming. In the paper a predictor-corrector interior-point algorithm for linearly constrained convex programming under the predictor-corrector motivation was proposed. In each iteration, the algorithm first performs a predic-tor-step to reduce the duality gap and then a corrector-step to keep the points close to the central trajectory.Computations in the algorithmonly require that the initial iterate be nonnegative while feasibility or strict feasibility is not required. It is proved that the algorithm is equivalent to a level-1 perturbed composite Newton method. Numerical experiments on twenty-six standard test problems are made. The results show that the proposed algorithm is stable and robust.