Quasi-Newton bundle-type methods for nondifferentiable convex optimization

Robert Mifflin, Defeng Sun, Liqun Qi

Research output: Journal article publicationJournal articleAcademic researchpeer-review

37 Citations (Scopus)


In this paper we provide implementable methods for solving nondifferentiable convex optimization problems. A typical method minimizes an approximate Moreau-Yosida regularization using a quasi-Newton technique with inexact function and gradient values which are generated by a finite inner bundle algorithm. For a BFGS bundle-type method global and superlinear convergence results for the outer iteration sequence are obtained.
Original languageEnglish
Pages (from-to)583-603
Number of pages21
JournalSIAM Journal on Optimization
Issue number2
Publication statusPublished - 1 Jan 1998
Externally publishedYes


  • Bundle method
  • Moreau-Yosida regularization
  • Quasi-Newton method
  • Superlinear convergence

ASJC Scopus subject areas

  • Software
  • Theoretical Computer Science

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