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Modified Lagrangian Methods for Separable Optimization Problems

Author

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  • Abdelouahed Hamdi
  • Aiman A. Mukheimer

Abstract

We propose a convergence analysis of a new decomposition method to solve structured optimization problems. The proposed scheme is based on a class of modified Lagrangians combined with the allocation of resources decomposition algorithm. Under mild assumptions, we show that the method generates convergent primal‐dual sequences.

Suggested Citation

  • Abdelouahed Hamdi & Aiman A. Mukheimer, 2012. "Modified Lagrangian Methods for Separable Optimization Problems," Abstract and Applied Analysis, John Wiley & Sons, vol. 2012(1).
    • Handle: RePEc:wly:jnlaaa:v:2012:y:2012:i:1:n:471854
    DOI: 10.1155/2012/471854
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    References listed on IDEAS

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    3. R. T. Rockafellar, 1976. "Augmented Lagrangians and Applications of the Proximal Point Algorithm in Convex Programming," Mathematics of Operations Research, INFORMS, vol. 1(2), pages 97-116, May.
    4. Alfred Auslender & Marc Teboulle & Sami Ben-Tiba, 1999. "Interior Proximal and Multiplier Methods Based on Second Order Homogeneous Kernels," Mathematics of Operations Research, INFORMS, vol. 24(3), pages 645-668, August.
    5. Alfredo N. Iusem & Marc Teboulle, 1995. "Convergence Rate Analysis of Nonquadratic Proximal Methods for Convex and Linear Programming," Mathematics of Operations Research, INFORMS, vol. 20(3), pages 657-677, August.
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    Cited by:

    1. Gai-Ge Wang & Lihong Guo & Amir Hossein Gandomi & Amir Hossein Alavi & Hong Duan, 2013. "Simulated Annealing‐Based Krill Herd Algorithm for Global Optimization," Abstract and Applied Analysis, John Wiley & Sons, vol. 2013(1).

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