Abstract
This note presents not only a surrogate subgradient method, but also a framework of surrogate subgradient methods. Furthermore, the framework can be used not only for separable problems, but also for coupled subproblems. The note delineates such a framework and shows that the algorithm can converges for a larger stepsize.
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Communicated by W.B. Gong.
The author thanks Professor Ching-An Lin from the Department of Electrical and Control Engineering of National Chiao Tung University, Hsinchu, Taiwan for valuable discussions.
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Chang, T.S. Comments on “Surrogate Gradient Algorithm for Lagrangian Relaxation”. J Optim Theory Appl 137, 691–697 (2008). https://doi.org/10.1007/s10957-007-9349-z
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DOI: https://doi.org/10.1007/s10957-007-9349-z