Journal of Optimization Theory and Applications

, Volume 135, Issue 3, pp 429–443 | Cite as

Approximate Duality

  • C. Scovel
  • D. Hush
  • I. Steinwart


We extend the Lagrangian duality theory for convex optimization problems to incorporate approximate solutions. In particular, we generalize well-known relationships between minimizers of a convex optimization problem, maximizers of its Lagrangian dual, saddle points of the Lagrangian, Kuhn–Tucker vectors, and Kuhn–Tucker conditions to incorporate approximate versions. As an application, we show how the theory can be used for convex quadratic programming and then apply the results to support vector machines from learning theory.


Lagrangian duality Approximations Saddle points Kuhn–Tucker conditions Support vector machines 


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Copyright information

© Springer Science+Business Media, LLC 2007

Authors and Affiliations

  1. 1.Modeling, Algorithms, and Informatics GroupLos Alamos National LaboratoryLos AlamosUSA

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