Mathematical Programming

, Volume 62, Issue 1, pp 261–275

Convergence of some algorithms for convex minimization

Authors

  • Rafael Correa
    • Departamento de MatematicasUniversidad de Chile
  • Claude Lemaréchal
    • Domaine de Voluceau-RocquencourtINRIA
Article

DOI: 10.1007/BF01585170

Cite this article as:
Correa, R. & Lemaréchal, C. Mathematical Programming (1993) 62: 261. doi:10.1007/BF01585170

Abstract

We present a simple and unified technique to establish convergence of various minimization methods. These contain the (conceptual) proximal point method, as well as implementable forms such as bundle algorithms, including the classical subgradient relaxation algorithm with divergent series.

AMS Subject Classification

65K0590C25

Key words

Nondifferentiable optimizationconvex programmingproximal point methodbundle algorithmsglobal convergence

Copyright information

© The Mathematical Programming Society, Inc. 1993