Convergence rate estimates for the gradient differential inclusion
Osman Guler (gulermath.umbc.edu)
Abstract: Let f be a lower semi-continuous convex function in a Euclidean space, finite or infinite dimensional. The gradient differential inclusion is a generalized differential equation which requires that -x'(t) be in the subgradient of f at x. It is the continuous versions of the gradient descent method for minimizing f in case f is differentiable, and the proximal point method in the general case. There is a remarkable similarity between the behavior of the inclusion and its discrete counterparts as well in the methods used in both cases. As a simple consequence of our previous results on the proximal point method, we prove global convergence rate estimates for the optimality gap f(x(t))-min f=O(1/t) or o(1/t).
Keywords: steepest descent, proximal-point method, convergence rate, non-linear semigroups.
Category 1: Convex and Nonsmooth Optimization (Convex Optimization )
Category 2: Convex and Nonsmooth Optimization (Nonsmooth Optimization )
Citation: Department of Mathematics and Statistics, University of Maryland, Baltimore County, Baltimore, MD 21250. November, 2003.
Entry Submitted: 12/02/2003
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