2021
DOI: 10.22436/jmcs.026.04.01
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Iterative methods for solving absolute value equations

Abstract: We suggest and analyze some iterative methods called Jacobi, Gauss-Seidel, SOR (successive over-relaxation), and modified Picard methods for solving absolute value equations Ax − |x| = b, where A is an M-matrix, b ∈ R n is a real vector, and x ∈ R n is unknown. Furthermore, we discuss the convergence of the suggested methods under suitable assumptions and represent their performance through our numerical results. Results are very encouraging and may stimulate further research in this direction.

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Cited by 16 publications
(7 citation statements)
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“…Theorem 3. If either of the following conditions satisfy then GAVE (1) has exactly one solution for each b: (i) [19] σ max (|B|) < σ min (A); (ii) [24] 2 2 I n is a positive definite matrix.…”
Section: Lemma 1 [7]mentioning
confidence: 99%
“…Theorem 3. If either of the following conditions satisfy then GAVE (1) has exactly one solution for each b: (i) [19] σ max (|B|) < σ min (A); (ii) [24] 2 2 I n is a positive definite matrix.…”
Section: Lemma 1 [7]mentioning
confidence: 99%
“…Elzaki transform (ET) is an integral transform [22] in this context. Several scholars [23][24][25][26][27][28][29][30] have looked at some essential solution approaches for real-world issues, as well as numerical simulations obtained using the novel integral transformation.…”
Section: Introductionmentioning
confidence: 99%
“…Chen et al [16] showed the SOR-like strategy with optimal parameters and examined some novel convergence situations different from [15]. Zamani and Hladík [17] offered a new concave minimization approach for AVE (1), which addresses the deficiency of the system proposed in [18] and others (see [19][20][21][22][23]).…”
Section: Introductionmentioning
confidence: 99%