Editing List of algorithms (section)

====Linear algebra====
{{further|Numerical linear algebra}}
* Krylov methods (for large sparse matrix problems; third most-important numerical method class of the 20th century as ranked by SISC; after fast-fourier and fast-multipole)
* [[Eigenvalue algorithm]]s
** [[Arnoldi iteration]]
** [[Inverse iteration]]
** [[Jacobi eigenvalue algorithm|Jacobi method]]
** [[Lanczos iteration]]
** [[Power iteration]]
** [[QR algorithm]]
** [[Rayleigh quotient iteration]]
* [[Gram–Schmidt process]]: orthogonalizes a set of vectors
* [[Matrix multiplication algorithm]]s
** [[Cannon's algorithm]]: a [[distributed algorithm]] for [[matrix multiplication]] especially suitable for computers laid out in an N × N mesh
** [[Coppersmith–Winograd algorithm]]: square [[matrix multiplication]]
** [[Freivalds' algorithm]]: a randomized algorithm used to verify matrix multiplication
** [[Strassen algorithm]]: faster [[matrix multiplication]]
{{anchor|Solving systems of linear equations}}
* Solving [[system of linear equations|systems of linear equations]]
** [[Biconjugate gradient method]]: solves systems of linear equations
** [[Conjugate gradient]]: an algorithm for the numerical solution of particular systems of linear equations
** [[Gaussian elimination]]
** [[Gauss–Jordan elimination]]: solves systems of linear equations
** [[Gauss–Seidel method]]: solves systems of linear equations iteratively
** [[Levinson recursion]]: solves equation involving a [[Toeplitz matrix]]
** [[Stone's method]]: also known as the strongly implicit procedure or SIP, is an algorithm for solving a sparse linear system of equations
** [[Successive over-relaxation]] (SOR): method used to speed up convergence of the [[Gauss–Seidel method]]
** [[Tridiagonal matrix algorithm]] (Thomas algorithm): solves systems of tridiagonal equations
* [[Sparse matrix]] algorithms
** [[Cuthill–McKee algorithm]]: reduce the [[bandwidth (matrix theory)|bandwidth]] of a [[symmetric sparse matrix]]
** [[Minimum degree algorithm]]: permute the rows and columns of a [[symmetric sparse matrix]] before applying the [[Cholesky decomposition]]
** [[Symbolic Cholesky decomposition]]: Efficient way of storing [[sparse matrix]]