Subgradient method: Difference between revisions

Revision as of 03:19, 7 March 2007

Revision as of 11:58, 7 March 2007

Line 1:

'''Subgradient methods''' are [[algorithm|algorithms]] for solving [[convex optimization]] problems. that can be used with a non-differentiable objective function originallyOriginally developed by 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 [[gradient descent|steepest descent]].

Although subgradient methods can be much slower than [[interior-point methods]] and [[Newton's method in optimization|Newton's method]] in practice, they can be immediately applied to a far wider variety of problems and require much less memory. Moreover, by combining the subgradient method with primal or dual decomposition techniques, it is sometimes possible to develop a simple distributed algorithm for a problem.