An adaptive CGNR algorithm for solving large linear systems
The author analyses the Conjugate Gradient Normal Residual algorithm (CGNR) and proves that it is both residual minimizing and error reducing. Then, by using a polynomial preconditioning he constructs an adaptive version of the CGNR algorithm. Numerical tests and comparisons with GMRES and classical CGNR algorithms are also reported.
scientific article; zbMATH DE number 1723325