Convergence of a generalized subgradient method for nondifferentiable convex optimization

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. The authors provide two sets of convergence conditions of the generalized subgradient method. The present results provide a larger class of sequences which converge to a minimum point and more freedom of adjustment to accelerate the speed of convergence.