A Subgradient method based on gradient sampling for solving convex optimization problems

Yaohua Hu, Chee Khian Sim, Xiaoqi Yang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

12 Citations (Scopus)


Based on the gradient sampling technique, we present a subgradient algorithm to solve the nondifferentiable convex optimization problem with an extended real-valued objective function. A feature of our algorithm is the approximation of subgradient at a point via random sampling of (relative) gradients at nearby points, and then taking convex combinations of these (relative) gradients. We prove that our algorithm converges to an optimal solution with probability 1. Numerical results demonstrate that our algorithm performs favorably compared with existing subgradient algorithms on applications considered.
Original languageEnglish
Pages (from-to)1559-1584
Number of pages26
JournalNumerical Functional Analysis and Optimization
Issue number12
Publication statusPublished - 2 Dec 2015


  • Convex optimization
  • Gradient sampling technique
  • Projection
  • Subgradient method

ASJC Scopus subject areas

  • Analysis
  • Signal Processing
  • Computer Science Applications
  • Control and Optimization

Cite this