Entropy-like proximal algorithms based on a second-order homogeneous distance function for quasi-convex programming

Shaohua Pan, Jein Shan Chen

Research output: Contribution to journalArticle

9 Citations (Scopus)


We consider two classes of proximal-like algorithms for minimizing a proper lower semicontinuous quasi-convex function f(x) subject to non-negative constraints x ≥ 0. The algorithms are based on an entropy-like second-order homogeneous distance function. Under the assumption that the global minimizer set is nonempty and bounded, we prove the full convergence of the sequence generated by the algorithms, and furthermore, obtain two important convergence results through imposing certain conditions on the proximal parameters. One is that the sequence generated will converge to a stationary point if the proximal parameters are bounded and the problem is continuously differentiable, and the other is that the sequence generated will converge to a solution of the problem if the proximal parameters approach to zero. Numerical experiments are done for a class of quasi-convex optimization problems where the function f(x) is a composition of a quadratic convex function from Rn to R and a continuously differentiable increasing function from R to R, and computational results indicate that these algorithms are very promising in finding a global optimal solution to these quasi-convex problems.

Original languageEnglish
Pages (from-to)555-575
Number of pages21
JournalJournal of Global Optimization
Issue number4
Publication statusPublished - 2007 Dec 1



  • Entropy-like distance
  • Proximal-like method
  • Quasi-convex programming

ASJC Scopus subject areas

  • Computer Science Applications
  • Management Science and Operations Research
  • Control and Optimization
  • Applied Mathematics

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