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//! The `ndarray-linalg` crate provides linear algebra functionalities for `ArrayBase`, the n-dimensional array data structure provided by [`ndarray`](https://github.com/rust-ndarray/ndarray). //! //! `ndarray-linalg` leverages [LAPACK](http://www.netlib.org/lapack/)'s routines using the bindings provided by [blas-lapack-rs/lapack](https://github.com/blas-lapack-rs/lapack). //! //! Linear algebra methods //! ----------------------- //! - Decomposition methods: //! - [QR decomposition](qr/index.html) //! - [Cholesky/LU decomposition](cholesky/index.html) //! - [Eigenvalue decomposition](eig/index.html) //! - [Eigenvalue decomposition for Hermite matrices](eigh/index.html) //! - [**S**ingular **V**alue **D**ecomposition](svd/index.html) //! - Solution of linear systems: //! - [General matrices](solve/index.html) //! - [Triangular matrices](triangular/index.html) //! - [Hermitian/real symmetric matrices](solveh/index.html) //! - [Tridiagonal matrices](tridiagonal/index.html) //! - [Inverse matrix computation](solve/trait.Inverse.html) //! //! 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](qr/trait.QRSquare.html), if you hold an immutable reference (i.e. `&self`) to the matrix you want to decompose; //! - [QRSquareInplace](qr/trait.QRSquareInplace.html), if you hold a mutable reference (i.e. `&mut self`) to the matrix you want to decompose; //! - [QRSquareInto](qr/trait.QRSquareInto.html), 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 //! ----------- //! - [Assertions for array](index.html#macros) //! - [Random matrix generators](generate/index.html) //! - [Scalar trait](types/trait.Scalar.html) #![allow( clippy::module_inception, clippy::many_single_char_names, clippy::type_complexity, clippy::ptr_arg )] #[macro_use] extern crate ndarray; pub mod assert; pub mod cholesky; pub mod convert; pub mod diagonal; pub mod eig; pub mod eigh; pub mod error; pub mod generate; pub mod inner; pub mod krylov; pub mod layout; pub mod least_squares; pub mod lobpcg; pub mod norm; pub mod operator; pub mod opnorm; pub mod qr; pub mod solve; pub mod solveh; pub mod svd; pub mod svddc; pub mod trace; pub mod triangular; pub mod tridiagonal; pub mod types; 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::lobpcg::{TruncatedEig, TruncatedOrder, TruncatedSvd}; 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::*;