Stochastic subgradient descent method for large-scale robust chance-constrained support vector machines

Ximing Wang, Neng Fan, Panos M. Pardalos

Research output: Contribution to journalArticle

3 Scopus citations

Abstract

Robust chance-constrained Support Vector Machines (SVM) with second-order moment information can be reformulated into equivalent and tractable Semidefinite Programming (SDP) and Second Order Cone Programming (SOCP) models. However, practical applications involve processing large-scale data sets. For the reformulated SDP and SOCP models, existed solvers by primal-dual interior method do not have enough computational efficiency. This paper studies the stochastic subgradient descent method and algorithms to solve robust chance-constrained SVM on large-scale data sets. Numerical experiments are performed to show the efficiency of the proposed approaches. The result of this paper breaks the computational limitation and expands the application of robust chance-constrained SVM.

Original languageEnglish (US)
Pages (from-to)1-12
Number of pages12
JournalOptimization Letters
DOIs
Publication statusAccepted/In press - Mar 15 2016

    Fingerprint

Keywords

  • Large-scale data
  • Primal-dual interior method
  • Robust chance constraints
  • Stochastic subgradient descent method
  • Support vector machines

ASJC Scopus subject areas

  • Control and Optimization

Cite this