A class of interior proximal-like algorithms for convex second-order cone programming

Shaohua Pan*, Jein Shan Chen


研究成果: 雜誌貢獻期刊論文同行評審

8 引文 斯高帕斯(Scopus)


We propose a class of interior proximal-like algorithms for the second-order cone program, which is to minimize a closed proper convex function subject to general second-order cone constraints. The class of methods uses a distance measure generated by a twice continuously differentiable strictly convex function on (0, +00), and includes as a special case the entropy-like proximal algorithm [Eggermont, Linear Algebra Appl., 130 (1990), pp. 25-42], which was originally proposed for minimizing a convex function subject to nonnegative constraints. Particularly, we consider an approximate version of these methods, allowing the inexact solution of subproblems. Like the entropy-like proximal algorithm for convex programming with nonnegative constraints, we, under some mild assumptions, establish the global convergence expressed in terms of the objective values for the proposed algorithm, and we show that the sequence generated is bounded, and every accumulation point is a solution of the considered problem. Preliminary numerical results are reported for two approximate entropy-like proximal algorithms, and numerical comparisons are also made with the merit function approach [Chen and Tseng, Math. Program., 104 (2005), pp. 293-327], which verify the effectiveness of the proposed method.

頁(從 - 到)883-910
期刊SIAM Journal on Optimization
出版狀態已發佈 - 2008 6月

ASJC Scopus subject areas

  • 軟體
  • 理論電腦科學


深入研究「A class of interior proximal-like algorithms for convex second-order cone programming」主題。共同形成了獨特的指紋。