About: Tridiagonal matrix algorithm is a research topic. Over the lifetime, 1070 publications have been published within this topic receiving 21084 citations.
TL;DR: This paper considers elimination methods to solve dense linear systems, in particular a variant of Gaussian elimination due to Huard, and shows that Huard’s elimination method is as stable as Gauss-Jordan elimination with the appropriate pivoting strategy.
Abstract: This paper considers elimination methods to solve dense linear systems, in particular a variant of Gaussian elimination due to Huard [13]. This variant reduces the system to an equivalent diagonal system just like Gauss-Jordan elimination, but does not require more floating-point operations than Gaussian elimination. To preserve stability, a pivoting strategy using column interchanges, proposed by Hoffmann [10], is incorporated in the original algorithm. An error analysis is given showing that Huard’s elimination method is as stable as Gauss-Jordan elimination with the appropriate pivoting strategy. This result is proven in a similar way as the proof of stability for Gauss-Jordan elimination given in [4]. Numerical experiments are reported which verify the theoretical error analysis of the Gauss-Huard algorithm.
TL;DR: The relativistic J-matrix as discussed by the authors is an extension of the one-dimensional Jmatrix method of scattering, which is a combination of vector, scalar, and pseudo-scalar components.
Abstract: We make a relativistic extension of the one-dimensional J-matrix method of scattering. The relativistic potential matrix is a combination of vector, scalar, and pseudo-scalar components. These are non-singular short-range potential functions (not necessarily analytic) such that they are well represented by their matrix elements in a finite subset of a square integrable basis set that supports a tridiagonal symmetric matrix representation for the free Dirac operator. Transmission and reflection coefficients are calculated for different potential coupling modes. This is the first of a two-paper sequence where we develop the theory in this part then follow it with applications in the second.
TL;DR: The eigenvalues of a tridiagonal matrix with a special structure are derived and a conjecture about the eigen values is proved, which has applications in biogeography theory.
TL;DR: This paper introduces a simple algorithm for the entries of the inverse of any tridiagonal nonsingular matrix, reduced as well as unreduced, through the linear recurrence relations satisfied by such determinants of their principal submatrices.