found: Work cat.: Barahona, F. The volume algorithm : producing primal solutions with a subgradient method, 1998.
found: Metaheuristic optimization via memory and evolution, 2005:p. 446 (The subgradient method is a generalization of the gradient method to nondifferentiable concave/convex functions)
found: Nedić, A. Subgradient methods for convex minimization, 2002:p. 10 (Subgradient methods are the principal methods used in convex nondifferentiable minimization. ... they have been extensively studied, and in general two major classes of subgradient methods have been developed: descent-based methods and nondescent methods. The descent-based subgradient methods are based on the principal idea of the function descent, which lies in the framework of gradient-type minimization. Nondescent subgradient methods are based on the idea of the distance decrease (distance from the set of minima), and their implementation is simpler than that of descent-based methods)
found: Wikipedia, Dec. 22, 2009(Subgradient method -- Subgradient methods are iterative methods for solving convex optimization problems. Originally developed by Naum Z. Shor and others in the 1960s and 1970s, subgradient methods can be used with a non-differentiable objective function. When the objective function is differentiable, subgradient methods for unconstrained problems use the same search direction as the method of steepest descent)
found: Decision sciences, Fall 2004:p. 620 (The subgradient method is an adaptation of the gradient method in which subgradients replace gradients)
notfound: Wolfram MathWorld, via WWW, Dec. 22, 2009;Math glossary, math terms, via WWW, Dec. 22, 2009;Encyclopedic dictionary of mathematics, 1987;James, R.C. Mathematics dictionary, 1992;Penguin dictionary of mathematics, 1989
