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An Effective Line Search for the Subgradient Method

Author

Listed:
  • C. Beltran

    (University of Geneva)

  • F. J. Heredia

    (Polytechnical University of Catalonia)

Abstract

One of the main drawbacks of the subgradient method is the tuning process to determine the sequence of steplengths. In this paper, the radar subgradient method, a heuristic method designed to compute a tuning-free subgradient steplength, is geometrically motivated and algebraically deduced. The unit commitment problem, which arises in the electrical engineering field, is used to compare the performance of the subgradient method with the new radar subgradient method.

Suggested Citation

  • C. Beltran & F. J. Heredia, 2005. "An Effective Line Search for the Subgradient Method," Journal of Optimization Theory and Applications, Springer, vol. 125(1), pages 1-18, April.
    • Handle: RePEc:spr:joptap:v:125:y:2005:i:1:d:10.1007_s10957-004-1708-4
    DOI: 10.1007/s10957-004-1708-4
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    Citations

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    Cited by:

    1. Monique Guignard, 2020. "Strong RLT1 bounds from decomposable Lagrangean relaxation for some quadratic 0–1 optimization problems with linear constraints," Annals of Operations Research, Springer, vol. 286(1), pages 173-200, March.
    2. A. M. Bagirov & L. Jin & N. Karmitsa & A. Al Nuaimat & N. Sultanova, 2013. "Subgradient Method for Nonconvex Nonsmooth Optimization," Journal of Optimization Theory and Applications, Springer, vol. 157(2), pages 416-435, May.
    3. Beltran-Royo, C., 2007. "A conjugate Rosen's gradient projection method with global line search for piecewise linear concave optimization," European Journal of Operational Research, Elsevier, vol. 182(2), pages 536-551, October.
    4. C. Beltran-Royo, 2009. "The radar method: an effective line search for piecewise linear concave functions," Annals of Operations Research, Springer, vol. 166(1), pages 299-312, February.

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