Mathematical Programming

, Volume 32, Issue 2, pp 199–223

Applications of the method of partial inverses to convex programming: Decomposition

  • Jonathan E. Spingarn

DOI: 10.1007/BF01586091

Cite this article as:
Spingarn, J.E. Mathematical Programming (1985) 32: 199. doi:10.1007/BF01586091


A primal–dual decomposition method is presented to solve the separable convex programming problem. Convergence to a solution and Lagrange multiplier vector occurs from an arbitrary starting point. The method is equivalent to the proximal point algorithm applied to a certain maximal monotone multifunction. In the nonseparable case, it specializes to a known method, the proximal method of multipliers. Conditions are provided which guarantee linear convergence.

Key words

Monotone Multifunction Separable Convex Programming Proximal Point Algorithm Decomposition Algorithm Resource Allocation Large-Scale Programming 

Copyright information

© The Mathematical Programming Society, Inc. 1985

Authors and Affiliations

  • Jonathan E. Spingarn
    • 1
  1. 1.School of MathematicsGeorgia Institute of TechnologyAtlantaUSA

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