cholesky algorithm
1Cholesky decomposition — In linear algebra, the Cholesky decomposition or Cholesky triangle is a decomposition of a Hermitian, positive definite matrix into the product of a lower triangular matrix and its conjugate transpose. It was discovered by André Louis Cholesky… …
2Minimum degree algorithm — In numerical analysis the minimum degree algorithm is an algorithm used to permute the rows and columns of a symmetric sparse matrix before applying the Cholesky decomposition, to reduce the number of non zeros in the Cholesky factor. This… …
3Symbolic Cholesky decomposition — In the mathematical subfield of numerical analysis the symbolic Cholesky decomposition is an algorithm used to determine the non zero pattern for the L factors of a symmetric sparse matrix when applying the Cholesky decomposition or… …
4Incomplete Cholesky factorization — In numerical analysis, a field within mathematics, an incomplete Cholesky factorization of a symmetric positive definite matrix is a sparse approximation of the Cholesky factorization. Incomplete Cholesky factorization are often used as a… …
5Gauss–Newton algorithm — The Gauss–Newton algorithm is a method used to solve non linear least squares problems. It can be seen as a modification of Newton s method for finding a minimum of a function. Unlike Newton s method, the Gauss–Newton algorithm can only be used… …
6Skyline matrix — A skyline matrix, or a variable band matrix, is a form of a sparse matrix storage format for a square, banded (and typically symmetric) matrix that reduces the storage requirement of a matrix more than banded storage. In banded storage, all… …
7List of numerical analysis topics — This is a list of numerical analysis topics, by Wikipedia page. Contents 1 General 2 Error 3 Elementary and special functions 4 Numerical linear algebra …
8LU decomposition — In linear algebra, LU decomposition (also called LU factorization) is a matrix decomposition which writes a matrix as the product of a lower triangular matrix and an upper triangular matrix. The product sometimes includes a permutation matrix as… …
9Mehrotra predictor-corrector method — Mehrotra s predictor corrector method in optimization is an implementation of interior point methods. It was proposed in 1989 by Sanjay Mehrotra. [cite journal|last=Mehrotra|first=S.|title=On the implementation of a primal–dual interior point… …
10Mehrotra predictor–corrector method — Mehrotra s predictor–corrector method in optimization is an implementation of interior point methods. It was proposed in 1989 by Sanjay Mehrotra.[1] The method is based on the fact that at each iteration of an interior point algorithm it is… …
