Subgradient method: Difference between revisions

Revision as of 21:31, 8 February 2018

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'''Subgradient methods''' are [[iterative method]]s for solving [[convex optimization|convex minimization]] problems. Originally developed by [[Naum Z. Shor]] and others in the 1960s and 1970s, subgradient methods are convergent when applied even to a non-differentiable objective function. When the objective function is differentiable, subgradientsub-gradient methods for unconstrained problems use the same search direction as the method of [[gradient descent|steepest descent]].

Subgradient methods are slower than Newton's method when applied to minimize twice continuously differentiable convex functions. However, Newton's method fails to converge on problems that have non-differentiable kinks.