The global least squares minimal residual (Gl-LSMR) method is an efficient solver for linear systems with multiple right-hand sides. To accelerate the convergence of the Gl-LSMR method, we propose a block preconditioner for the global LSMR method which can be used for solving linear systems with a block partitioned coefficient matrix and multiple right-hand sides. Numerical examples and comparing the preconditioned Gl-LSMR method with the Gl-LSMR method validate the effectiveness of the preconditioner. Numerical results confirm that the Block Preconditioned Gl-LSMR (BPGLSMR) method has a better performance in reducing the number of iterations and CPU time.
Hasanpour, A., & Mojarrab, M. (2021). A block preconditioner for the Gl-LSMR algorithm. Journal of Mathematical Modeling, 9(3), 347-359. doi: 10.22124/jmm.2020.17687.1525
MLA
Afsaneh Hasanpour; Maryam Mojarrab. "A block preconditioner for the Gl-LSMR algorithm". Journal of Mathematical Modeling, 9, 3, 2021, 347-359. doi: 10.22124/jmm.2020.17687.1525
HARVARD
Hasanpour, A., Mojarrab, M. (2021). 'A block preconditioner for the Gl-LSMR algorithm', Journal of Mathematical Modeling, 9(3), pp. 347-359. doi: 10.22124/jmm.2020.17687.1525
VANCOUVER
Hasanpour, A., Mojarrab, M. A block preconditioner for the Gl-LSMR algorithm. Journal of Mathematical Modeling, 2021; 9(3): 347-359. doi: 10.22124/jmm.2020.17687.1525
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