ndarray-linalg crate provides linear algebra functionalities for
ArrayBase, the n-dimensional array data structure provided by
- Decomposition methods:
- Solution of linear systems:
- Inverse matrix computation
Each routine is usually exposed as a trait, implemented by the relevant types.
For each routine there might be multiple “variants”: different traits corresponding to the different ownership possibilities of the array you intend to work on.
For example, if you are interested in the QR decomposition of a square matrix, you can use:
- QRSquare, if you hold an immutable reference (i.e.
&self) to the matrix you want to decompose;
- QRSquareInplace, if you hold a mutable reference (i.e.
&mut self) to the matrix you want to decompose;
- QRSquareInto, if you can pass the matrix you want to decompose by value (e.g.
Depending on the algorithm, each variant might require more or less copy operations of the underlying data.
Details are provided in the description of each routine.
Assertions for array
Cholesky decomposition of Hermitian (or real symmetric) positive definite matrices
utilities for convert array
Vector as a Diagonal matrix
Eigenvalue decomposition for non-symmetric square matrices
Eigenvalue decomposition for Hermite matrices
Generator functions for matrices
Krylov subspace methods
Convert ndarray into LAPACK-compatible matrix format
Norm of vectors
Linear operator algebra
Solve systems of linear equations and invert matrices
Solve Hermitian (or real symmetric) linear problems and invert Hermitian (or real symmetric) matrices
Singular-value decomposition (SVD)
Singular-value decomposition (SVD) by divide-and-conquer (?gesdd)
Methods for triangular matrices
Vectors as a Tridiagonal matrix & Methods for tridiagonal matrices
Basic types and their methods for linear algebra
Truncated eigenproblem solver
Truncated singular value decomposition
Find largest or smallest eigenvalues