Expand description
The ndarray-linalg crate provides linear algebra functionalities for ArrayBase, the n-dimensional array data structure provided by ndarray.
ndarray-linalg leverages LAPACK’s routines using the bindings provided by blas-lapack-rs/lapack.
§Linear algebra methods
- Decomposition methods:
- Solution of linear systems:
- Inverse matrix computation
§Naming Convention
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.
self).
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.
§Utilities
Re-exports§
pub use crate::lobpcg::TruncatedEig;pub use crate::lobpcg::TruncatedOrder;pub use crate::lobpcg::TruncatedSvd;pub use crate::assert::*;pub use crate::cholesky::*;pub use crate::convert::*;pub use crate::diagonal::*;pub use crate::eig::*;pub use crate::eigh::*;pub use crate::generate::*;pub use crate::inner::*;pub use crate::layout::*;pub use crate::least_squares::*;pub use crate::norm::*;pub use crate::operator::*;pub use crate::opnorm::*;pub use crate::qr::*;pub use crate::solve::*;pub use crate::solveh::*;pub use crate::svd::*;pub use crate::svddc::*;pub use crate::trace::*;pub use crate::triangular::*;pub use crate::tridiagonal::*;pub use crate::types::*;
Modules§
- assert
- Assertions for array
- cholesky
- Cholesky decomposition of Hermitian (or real symmetric) positive definite matrices
- convert
- utilities for convert array
- diagonal
- Vector as a Diagonal matrix
- eig
- Eigenvalue decomposition for non-symmetric square matrices
- eigh
- Eigendecomposition for Hermitian matrices.
- error
- Define Errors
- generate
- Generator functions for matrices
- inner
- krylov
- Krylov subspace methods
- layout
- Convert ndarray into LAPACK-compatible matrix format
- least_
squares - Least Squares
- lobpcg
- norm
- Norm of vectors
- operator
- Linear operator algebra
- opnorm
- Operator norm
- qr
- QR decomposition
- solve
- Solve systems of linear equations and invert matrices
- solveh
- Solve Hermitian (or real symmetric) linear problems and invert Hermitian (or real symmetric) matrices
- svd
- Singular-value decomposition (SVD)
- svddc
- Singular-value decomposition (SVD) by divide-and-conquer (?gesdd)
- trace
- Trace calculation
- triangular
- Methods for triangular matrices
- tridiagonal
- Vectors as a Tridiagonal matrix & Methods for tridiagonal matrices
- types
- Basic types and their methods for linear algebra