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Approximate Duality

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Abstract

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.

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Authors and Affiliations

  1. Modeling, Algorithms, and Informatics Group, Los Alamos National Laboratory, Los Alamos, NM, USA

    C. Scovel, D. Hush & I. Steinwart

Authors
  1. C. Scovel
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  2. D. Hush
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  3. I. Steinwart
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Corresponding author

Correspondence to C. Scovel.

Additional information

Communicated by M. Fukushima.

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Scovel, C., Hush, D. & Steinwart, I. Approximate Duality. J Optim Theory Appl 135, 429–443 (2007). https://doi.org/10.1007/s10957-007-9281-2

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