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Fast solvers for tridiagonal Toeplitz linear systems

Zhongyun Liu, Shan Li, Yin Yi, Yulin Zhang

2020Computational and Applied Mathematics15 citationsDOIOpen Access PDF

Abstract

Let A be a tridiagonal Toeplitz matrix denoted by $$A = {\text {Tritoep}} (\beta , \alpha , \gamma )$$ . The matrix A is said to be: strictly diagonally dominant if $$|\alpha |>|\beta |+|\gamma |$$ , weakly diagonally dominant if $$|\alpha |\ge |\beta |+|\gamma |$$ , subdiagonally dominant if $$|\beta |\ge |\alpha |+|\gamma |$$ , and superdiagonally dominant if $$|\gamma |\ge |\alpha |+|\beta |$$ . In this paper, we consider the solution of a tridiagonal Toeplitz system $$A\mathbf {x}= \mathbf {b}$$ , where A is subdiagonally dominant, superdiagonally dominant, or weakly diagonally dominant, respectively. We first consider the case of A being subdiagonally dominant. We transform A into a block $$2\times 2$$ matrix by an elementary transformation and then solve such a linear system using the block LU factorization. Compared with the LU factorization method with pivoting, our algorithm takes less flops, and needs less memory storage and data transmission. In particular, our algorithm outperforms the LU factorization method with pivoting in terms of computing efficiency. Then, we deal with superdiagonally dominant and weakly diagonally dominant cases, respectively. Numerical experiments are finally given to illustrate the effectiveness of our algorithms.

Topics & Concepts

Tridiagonal matrixToeplitz matrixMathematicsFactorizationDiagonally dominant matrixFLOPSDiagonalCombinatoricsMatrix (chemical analysis)Block (permutation group theory)Pure mathematicsAlgorithmParallel computingPhysicsComputer scienceGeometryQuantum mechanicsInvertible matrixEigenvalues and eigenvectorsComposite materialMaterials scienceMatrix Theory and AlgorithmsHolomorphic and Operator TheoryAdvanced Topics in Algebra