Rigorous proof of termination of SMO algorithm for support vector machines

Norikazu Takahashi, Tetsuo Nishi

Research output: Contribution to journalArticlepeer-review

38 Citations (Scopus)


Sequential minimal optimization (SMO) algorithm is one of the simplest decomposition methods for learning of support vector machines (SVMs). Keerthi and Gilbert have recently studied the convergence property of SMO algorithm and given a proof that SMO algorithm always stops within a finite number of iterations. In this letter, we point out the incompleteness of their proof and give a more rigorous proof.

Original languageEnglish
Pages (from-to)774-776
Number of pages3
JournalIEEE Transactions on Neural Networks
Issue number3
Publication statusPublished - May 2005
Externally publishedYes


  • Convergence
  • Sequential minimal optimization (SMO) algorithm
  • Support vector machines (SVMs)
  • Termination

ASJC Scopus subject areas

  • Software
  • Computer Science Applications
  • Computer Networks and Communications
  • Artificial Intelligence


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