About the Gradient Projection Algorithm for a Strongly Convex Function and a Proximally Smooth Set
Journal of convex analysis, Tome 24 (2017) no. 2, pp. 493-5
We consider the gradient projection algorithm for a strongly convex function with the Lipschitz continuous gradient and a proximally smooth (nonconvex in general) set in a real Hilbert space. We prove that the problem of minimization of such function on a proximally smooth set has unique solution if the constant of proximal smoothness of the set is sufficiently large. The considered algorithm converges with the rate of geometric progression.
Classification :
46C05, 52A07, 49J52, 46N10, 90C26, 26B25
Mots-clés : Hilbert space, strongly convex set of radius r, proximally smooth set with constant R, Lipschitz continuous gradient, gradient projection algorithm, continuous optimization
Mots-clés : Hilbert space, strongly convex set of radius r, proximally smooth set with constant R, Lipschitz continuous gradient, gradient projection algorithm, continuous optimization
@article{JCA_2017_24_2_JCA_2017_24_2_a7,
author = {M. V. Balashov},
title = {About the {Gradient} {Projection} {Algorithm} for a {Strongly} {Convex} {Function} and a {Proximally} {Smooth} {Set}},
journal = {Journal of convex analysis},
pages = {493--5},
year = {2017},
volume = {24},
number = {2},
url = {http://geodesic.mathdoc.fr/item/JCA_2017_24_2_JCA_2017_24_2_a7/}
}
TY - JOUR AU - M. V. Balashov TI - About the Gradient Projection Algorithm for a Strongly Convex Function and a Proximally Smooth Set JO - Journal of convex analysis PY - 2017 SP - 493 EP - 5 VL - 24 IS - 2 UR - http://geodesic.mathdoc.fr/item/JCA_2017_24_2_JCA_2017_24_2_a7/ ID - JCA_2017_24_2_JCA_2017_24_2_a7 ER -
M. V. Balashov. About the Gradient Projection Algorithm for a Strongly Convex Function and a Proximally Smooth Set. Journal of convex analysis, Tome 24 (2017) no. 2, pp. 493-5. http://geodesic.mathdoc.fr/item/JCA_2017_24_2_JCA_2017_24_2_a7/