A primal-dual perspective of online learning algorithms

Shai Shalev-Shwartz, Yoram Singer

Research output: Contribution to journalArticlepeer-review

73 Scopus citations


We describe a novel framework for the design and analysis of online learning algorithms based on the notion of duality in constrained optimization. We cast a sub-family of universal online bounds as an optimization problem. Using the weak duality theorem we reduce the process of online learning to the task of incrementally increasing the dual objective function. The amount by which the dual increases serves as a new and natural notion of progress for analyzing online learning algorithms. We are thus able to tie the primal objective value and the number of prediction mistakes using the increase in the dual.

Original languageEnglish (US)
Pages (from-to)115-142
Number of pages28
JournalMachine Learning
Issue number2-3
StatePublished - Dec 2007
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Software
  • Artificial Intelligence


  • Duality
  • Mistake bounds
  • Online learning
  • Regret bounds


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