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The SOR-like iteration method for solving the system of absolute value equations of finding a vector x such that A x - | x | - b = 0 with ν = ‖ A - 1 ‖ 2 < 1 is investigated. The convergence conditions of the SOR-like iteration method proposed by Ke and Ma (Appl. Math. Comput., 311:195–202, 2017) are revisited and a new proof is given, which exhibits some insights in determining the convergent region and the optimal iteration parameter. Along this line, the optimal parameter which minimizes ‖ T ν (ω) ‖ 2 with T ν (ω) = | 1 - ω | ω 2 ν | 1 - ω | | 1 - ω | + ω 2 ν and the approximate optimal parameter which minimizes an upper bound of ‖ T ν (ω) ‖ 2 are explored. The optimal and approximate optimal parameters are iteration-independent, and the bigger value of ν is, the smaller convergent region of the iteration parameter ω is. Numerical results are presented to demonstrate that the SOR-like iteration method with the optimal parameter is superior to that with the approximate optimal parameter proposed by Guo et al. (Appl. Math. Lett., 97:107–113, 2019). [ABSTRACT FROM AUTHOR]

Based on the variant of the deteriorated positive-definite and skew-Hermitian splitting (VDPSS) preconditioner developed by Zhang and Gu (BIT Numer. Math. 56:587-604, 2016), a generalized VDPSS (GVDPSS) preconditioner is established in this paper by replacing the parameter α in (2,2)-block of the VDPSS preconditioner by another parameter β. This preconditioner can also be viewed as a generalized form of the VDPSS preconditioner and the new relaxed HSS (NRHSS) preconditioner which has been exhibited by Salkuyeh and Masoudi (Numer. Algorithms, 2016). The convergence properties of the GVDPSS iteration method are derived. Meanwhile, the distribution of eigenvalues and the forms of the eigenvectors of the preconditioned matrix are analyzed in detail. We also study the upper bounds on the degree of the minimum polynomial of the preconditioned matrix. Numerical experiments are implemented to illustrate the effectiveness of the GVDPSS preconditioner and verify that the GVDPSS preconditioned generalized minimal residual method is superior to the DPSS, relaxed DPSS, SIMPLE-like, NRHSS, and VDPSS preconditioned ones for solving saddle point problems in terms of the iterations and computational times. [ABSTRACT FROM AUTHOR]

In this paper a particular partition on blocks of generalized ( h, r)-circulant matrices is determined. We obtain a characterization of generalized ( h, r)-circulant matrices and get some results on the values of the permanent and also on the determination of the eigenvalues of r-circulant matrices. At last, a lower bound for the permanent of these matrices is achieved. [ABSTRACT FROM AUTHOR]

Recently, Axelsson and Salkuyeh in (BIT Numerical Mathematics, 59 (2019) 321–342) proposed the transformed matrix iteration (TMIT) method for solving a certain two-by-two block matrices with square blocks. However, they did not present any formula for the optimal parameter of the method which minimizes the spectral radius of the iteration matrix. In this work, we give an upper bound for the spectral radius of the iteration matrix of the method and then compute the parameter which minimizes this upper bound. Numerical results are presented to demonstrate the theoretical results. [ABSTRACT FROM AUTHOR]