Linear convergence analysis for general proximal point algorithms involving (H, η) − monotonicity frameworks

Downloads

DOI:

https://doi.org/10.4067/S0719-06462011000300010

Abstract

General framework for the generalized proximal point algorithm, based on the notion of (H, η) − monotonicity, is developed. The linear convergence analysis for the generalized proximal point algorithm to the context of solving a class of nonlinear variational inclusions is examined, The obtained results generalize and unify a wide range of problems to the context of achieving the linear convergence for proximal point algorithms.

Keywords

General cocoerciveness , Variational inclusions , Maximal monotone mapping , (H, η) − monotone mapping , Generalized proximal point algorithm , Generalized resolvent operator
  • Ram U. Verma Texas A&M University at Kingsville, Department of Mathematics, Kingsville, Texas 78363, USA.
  • Pages: 185–196
  • Date Published: 2011-10-01
  • Vol. 13 No. 3 (2011): CUBO, A Mathematical Journal

Downloads

Download data is not yet available.

Published

2011-10-01

How to Cite

[1]
R. U. Verma, “Linear convergence analysis for general proximal point algorithms involving (H, η) − monotonicity frameworks”, CUBO, vol. 13, no. 3, pp. 185–196, Oct. 2011.

Issue

Vol. 13 No. 3 (2011): CUBO, A Mathematical Journal

Section

Articles