An Interior Point Parameterized Central Path Following Algorithm for Linearly Constrained Convex Programming

Liangshao Hou, Xun Qian, Li Zhi Liao, Jie Sun

Research output: Contribution to journalArticlepeer-review

Abstract

An interior point algorithm is proposed for linearly constrained convex programming following a parameterized central path, which is a generalization of the central path and requires weaker convergence conditions. The convergence and polynomial-time complexity of the proposed algorithm are proved under the assumption that the Hessian of the objective function is locally Lipschitz continuous. In addition, an initialization strategy is proposed and some numerical results are provided to show the efficiency and attractiveness of the proposed algorithm.
Original languageEnglish (US)
JournalJournal of Scientific Computing
Volume90
Issue number3
DOIs
StatePublished - Feb 8 2022

Bibliographical note

KAUST Repository Item: Exported on 2022-04-26
Acknowledgements: The authors would like to thank the Associate Editor and one anonymous referee for their constructive comments and suggestions on the earlier version of this paper.

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Theoretical Computer Science
  • Software
  • General Engineering

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