Title :
On Marginal Subgradients of Convex Functions
Author_Institution :
Dept. of Math. & Comput. Sci., Northern Michigan Univ., Marquette, MI, USA
Abstract :
For a lower semicontinuous and proper convex function f with nonempty minimizer set and a point x in its domain, a marginal subgradient of f at x is a vector in ∂ f(x) with the smallest norm. We denote the norm of the marginal subgradient of f at x by g(x), it is known that the infimum of g(x) over a level set of f is nondecreasing from a lower level set to a higher level set. In this paper we study another aspect of the marginal subgradients, namely the monotonicity of the infimum of g(x) over an equidistance contour from the minimizer set. The results are applied to the study of some growth properties of the marginal subgradients.
Keywords :
convex programming; gradient methods; set theory; convex function; equidistance contour; lower level set; marginal subgradient; nonempty minimizer set; Computer science; Joining processes; Level set; Mathematics;
Conference_Titel :
Computational Science and Optimization (CSO), 2010 Third International Joint Conference on
Conference_Location :
Huangshan, Anhui
Print_ISBN :
978-1-4244-6812-6
Electronic_ISBN :
978-1-4244-6813-3
DOI :
10.1109/CSO.2010.248