Optimization pp 207-231 | Cite as

Convex Programming

  • Kenneth Lange
Part of the Springer Texts in Statistics book series (STS)

Abstract

Our final chapter provides a concrete introduction to convex programming, interior point methods, and duality, three of the deepest and most pervasive themes of modern optimization theory. It takes considerable mathematical maturity to appreciate the subtlety of these topics, and it is impossible to do them justice in a short essay. Our philosophy here is to build on previous material on convexity and the MM algorithm. Exposing these connections reinforces old concepts while teaching new ones. The survey article [46] is especially recommended to readers wanting a more thorough introduction to the topics treated here.

Keywords

Support Vector Machine Equality Constraint Dual Problem Inequality Constraint Surrogate Function 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media New York 2004

Authors and Affiliations

  • Kenneth Lange
    • 1
  1. 1.Department of Biomathematics and Human GeneticsUCLA School of MedicineLos AngelesUSA

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