Mathematical Programming

, Volume 62, Issue 1, pp 261–275

Convergence of some algorithms for convex minimization


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

DOI: 10.1007/BF01585170

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


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


Key words

Nondifferentiable optimizationconvex programmingproximal point methodbundle algorithmsglobal convergence

Copyright information

© The Mathematical Programming Society, Inc. 1993