Abstract
A generalized subgradient method is considered which uses the subgradients at previous iterations as well as the subgradient at current point. This method is a direct generalization of the usual subgradient method. We provide two sets of convergence conditions of the generalized subgradient method. Our results provide a larger class of sequences which converge to a minimum point and more freedom of adjustment to accelerate the speed of convergence.
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Most of this research was performed when the first author was visiting Decision and Information Systems Department, College of Business, Arizona State University.
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Kim, S., Ahn, H. Convergence of a generalized subgradient method for nondifferentiable convex optimization. Mathematical Programming 50, 75–80 (1991). https://doi.org/10.1007/BF01594925
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DOI: https://doi.org/10.1007/BF01594925