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In numerical linear algebra, the QR algorithm or QR iteration is an eigenvalue algorithm: that is, a procedure to calculate the eigenvalues and eigenvectors of a matrix.The QR algorithm was developed in the late 1950s by John G. F. Francis and by Vera N. Kublanovskaya, working independently.
An iterative method with a given iteration matrix is called convergent if the following holds lim k → ∞ C k = 0. {\displaystyle \lim _{k\rightarrow \infty }C^{k}=0.} An important theorem states that for a given iterative method and its iteration matrix C {\displaystyle C} it is convergent if and only if its spectral radius ρ ( C ...
In numerical linear algebra, the Jacobi method (a.k.a. the Jacobi iteration method) is an iterative algorithm for determining the solutions of a strictly diagonally dominant system of linear equations. Each diagonal element is solved for, and an approximate value is plugged in. The process is then iterated until it converges.
Such methods can be preferable when the matrix is so big that storing and manipulating it would cost a lot of memory and computing time, even with the use of methods for sparse matrices. Many iterative methods allow for a matrix-free implementation, including: the power method, the Lanczos algorithm, [2]
Spectral radius () of the iteration matrix for the SOR method .The plot shows the dependence on the spectral radius of the Jacobi iteration matrix := ().. The choice of relaxation factor ω is not necessarily easy, and depends upon the properties of the coefficient matrix.
In mathematics, the Jacobi method for complex Hermitian matrices is a generalization of the Jacobi iteration method. The Jacobi iteration method is also explained in "Introduction to Linear Algebra" by Strang (1993).
In mathematics, an H-matrix is a matrix whose comparison matrix is an M-matrix.It is useful in iterative methods.. Definition: Let A = (a ij) be a n × n complex matrix. Then comparison matrix M(A) of complex matrix A is defined as M(A) = α ij where α ij = −|A ij | for all i ≠ j, 1 ≤ i,j ≤ n and α ij = |A ij | for all i = j, 1 ≤ i,j ≤ n.
The Lanczos algorithm is most often brought up in the context of finding the eigenvalues and eigenvectors of a matrix, but whereas an ordinary diagonalization of a matrix would make eigenvectors and eigenvalues apparent from inspection, the same is not true for the tridiagonalization performed by the Lanczos algorithm; nontrivial additional steps are needed to compute even a single eigenvalue ...