Alexander J. Zaslavski
Abstract
We study the convergence of a proximal-like minimization algorithm using Bregman functions. We extend the convergence results by Censor and Zenios (1992) and by Chen and Teboulle (1993) by showing that the convergence of the algorithm is preserved in the presence of computational errors.
OpenAlex reports 3 citations for this work. Citation counts describe recorded attention and do not establish research quality.
A contribution statement is not available in the OpenAlex record.
Method details are not available in the OpenAlex metadata.
Findings are not separately available in the OpenAlex metadata.
Limitations are not available in the OpenAlex metadata.
Application details are not available in the OpenAlex metadata.
We study the convergence of a proximal-like minimization algorithm using Bregman functions. We extend the convergence results by Censor and Zenios (1992) and by Chen and Teboulle (1993) by showing that the convergence of the algorithm is preserved in the presence of computational errors.
Key concepts: Convergence (economics), Mathematics, Algorithm, Minification, Chen, Compact convergence, Mathematical optimization, Rate of convergence