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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.
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.
In numerical linear algebra, the Arnoldi iteration is an eigenvalue algorithm and an important example of an iterative method.Arnoldi finds an approximation to the eigenvalues and eigenvectors of general (possibly non-Hermitian) matrices by constructing an orthonormal basis of the Krylov subspace, which makes it particularly useful when dealing with large sparse matrices.
Each Jacobi iteration with R J pq generates a transformed matrix, T J, with T J p,q = 0. The rotation matrix R J p , q is defined as a product of two complex unitary rotation matrices.
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 ...
4. The algorithm is written using matrix notation (1 based arrays instead of 0 based). 5. When implementing the algorithm, the part specified using matrix notation must be performed simultaneously. 6. This implementation does not correctly account for the case in which one dimension is an independent subspace.
Newton's method requires the Jacobian matrix of all partial derivatives of a multivariate function when used to search for zeros or the Hessian matrix when used for finding extrema. Quasi-Newton methods, on the other hand, can be used when the Jacobian matrices or Hessian matrices are unavailable or are impractical to compute at every iteration.