An entropy-like proximal algorithm and the exponential multiplier method for convex symmetric cone programming

Jein Shan Chen*, Shaohua Pan

*此作品的通信作者

研究成果: 雜誌貢獻期刊論文同行評審

6 引文 斯高帕斯(Scopus)

摘要

We introduce an entropy-like proximal algorithm for the problem of minimizing a closed proper convex function subject to symmetric cone constraints. The algorithm is based on a distance-like function that is an extension of the Kullback- Leiber relative entropy to the setting of symmetric cones. Like the proximal algorithms for convex programming with nonnegative orthant cone constraints, we show that, under some mild assumptions, the sequence generated by the proposed algorithm is bounded and every accumulation point is a solution of the considered problem. In addition, we also present a dual application of the proposed algorithm to the symmetric cone linear program, leading to a multiplier method which is shown to possess similar properties as the exponential multiplier method (Tseng and Bertsekas in Math. Program. 60:1-19, 1993) holds.

原文英語
頁(從 - 到)477-499
頁數23
期刊Computational Optimization and Applications
47
發行號3
DOIs
出版狀態已發佈 - 2010 十一月

ASJC Scopus subject areas

  • 控制和優化
  • 計算數學
  • 應用數學

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