自然科学版 英文版
自然科学版 英文版
自然科学版 英文版

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中南大学学报(英文版)

Journal of Central South University

Vol. 8    No. 3    September 2001

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Predictor-corrector interior-point algorithm for
linearly constrained convex programming
LIANG Xi-ming

College of Information Science&Engineering, Central South University , Changsha 410083, China

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.

 

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

中南大学学报(自然科学版)
  ISSN 1672-7207
CN 43-1426/N
ZDXZAC
中南大学学报(英文版)
  ISSN 2095-2899
CN 43-1516/TB
JCSTFT
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