A proximal-like algorithm for a class of nonconvex programming

Jein Shan Chen*, Shaohua Pan


研究成果: 雜誌貢獻期刊論文同行評審

8 引文 斯高帕斯(Scopus)


In this paper, we study a proximal-like algorithm for minimizing a closed proper function f(x) subject to x30, based on the iterative scheme: xk ε argmin{f(x) + μkd(x, xk-1)}, where d( , ) is an entropy-like distance function. The algorithm is well-defined under the assumption that the problem has a nonempty and bounded solution set. If, in addition, f is a differentiable quasi-convex function (or f is a differentiable function which is homogeneous with respect to a solution), we show that the sequence generated by the algorithm is convergent (or bounded), and furthermore, it converges to a solution of the problem (or every accumulation point is a solution of the problem) when the parameter μk approaches to zero. Preliminary numerical results are also reported, which further verify the theoretical results obtained.

頁(從 - 到)319-333
期刊Pacific Journal of Optimization
出版狀態已發佈 - 2008 5月

ASJC Scopus subject areas

  • 控制和優化
  • 計算數學
  • 應用數學


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