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The Log—Quadratic Proximal Methodology in Convex Optimization Algorithms and Variational Inequalities

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Book cover Equilibrium Problems and Variational Models

Part of the book series: Nonconvex Optimization and Its Applications ((NOIA,volume 68))

Abstract

The logarithmic-quadratic proximal map recently introduced by the authors allows for deriving several efficient algorithms in convex optimization and variational inequalities. This brief survey outlines the power and usefulness of the resulting logarithmic-quadratic methodology.

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Author information

Authors and Affiliations

  1. Departement de Mathematiques, Universite Lyon I, Lyon, France

    Alfred Auslender

  2. School of Mathematical Sciences, Tel-Aviv University, Ramat-Aviv, 69978, Israel

    Marc Teboulle

Authors
  1. Alfred Auslender
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  2. Marc Teboulle
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Editor information

Editors and Affiliations

  1. Department of Mathematics, University of Catania, 95125, Catania, Italy

    Patrizia Daniele  & Antonino Maugeri  & 

  2. Department of Mathematics, University of Pisa, 56127, Pisa, Italy

    Franco Giannessi

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© 2003 Kluwer Academic Publishers

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Auslender, A., Teboulle, M. (2003). The Log—Quadratic Proximal Methodology in Convex Optimization Algorithms and Variational Inequalities. In: Daniele, P., Giannessi, F., Maugeri, A. (eds) Equilibrium Problems and Variational Models. Nonconvex Optimization and Its Applications, vol 68. Springer, Boston, MA. https://doi.org/10.1007/978-1-4613-0239-1_2

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  • DOI: https://doi.org/10.1007/978-1-4613-0239-1_2

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-1-4613-7955-3

  • Online ISBN: 978-1-4613-0239-1

  • eBook Packages: Springer Book Archive

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