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//! Cholesky decomposition of Hermitian (or real symmetric) positive definite matrices //! //! See the [Wikipedia page about Cholesky //! decomposition](https://en.wikipedia.org/wiki/Cholesky_decomposition) for //! more information. //! //! # Example //! //! Calculate `L` in the Cholesky decomposition `A = L * L^H`, where `A` is a //! Hermitian (or real symmetric) positive definite matrix: //! //! ``` //! #[macro_use] //! extern crate ndarray; //! extern crate ndarray_linalg; //! //! use ndarray::prelude::*; //! use ndarray_linalg::{CholeskyInto, UPLO}; //! # fn main() { //! //! let a: Array2<f64> = array![ //! [ 4., 12., -16.], //! [ 12., 37., -43.], //! [-16., -43., 98.] //! ]; //! let lower = a.cholesky_into(UPLO::Lower).unwrap(); //! assert!(lower.all_close(&array![ //! [ 2., 0., 0.], //! [ 6., 1., 0.], //! [-8., 5., 3.] //! ], 1e-9)); //! # } //! ``` use ndarray::*; use super::convert::*; use super::error::*; use super::layout::*; use super::triangular::IntoTriangular; use super::types::*; pub use lapack_traits::UPLO; /// Cholesky decomposition of Hermitian (or real symmetric) positive definite matrix reference pub trait Cholesky { type Output; /// Computes the Cholesky decomposition of the Hermitian (or real /// symmetric) positive definite matrix. /// /// If the argument is `UPLO::Upper`, then computes the decomposition `A = /// U^H * U` using the upper triangular portion of `A` and returns `U`. /// Otherwise, if the argument is `UPLO::Lower`, computes the decomposition /// `A = L * L^H` using the lower triangular portion of `A` and returns /// `L`. fn cholesky(&self, UPLO) -> Result<Self::Output>; } /// Cholesky decomposition of Hermitian (or real symmetric) positive definite matrix pub trait CholeskyInto: Sized { /// Computes the Cholesky decomposition of the Hermitian (or real /// symmetric) positive definite matrix. /// /// If the argument is `UPLO::Upper`, then computes the decomposition `A = /// U^H * U` using the upper triangular portion of `A` and returns `U`. /// Otherwise, if the argument is `UPLO::Lower`, computes the decomposition /// `A = L * L^H` using the lower triangular portion of `A` and returns /// `L`. fn cholesky_into(self, UPLO) -> Result<Self>; } /// Cholesky decomposition of Hermitian (or real symmetric) positive definite mutable reference of matrix pub trait CholeskyMut { /// Computes the Cholesky decomposition of the Hermitian (or real /// symmetric) positive definite matrix, storing the result in `self` and /// returning it. /// /// If the argument is `UPLO::Upper`, then computes the decomposition `A = /// U^H * U` using the upper triangular portion of `A` and returns `U`. /// Otherwise, if the argument is `UPLO::Lower`, computes the decomposition /// `A = L * L^H` using the lower triangular portion of `A` and returns /// `L`. fn cholesky_mut(&mut self, UPLO) -> Result<&mut Self>; } impl<A, S> CholeskyInto for ArrayBase<S, Ix2> where A: Scalar, S: DataMut<Elem = A>, { fn cholesky_into(mut self, uplo: UPLO) -> Result<Self> { unsafe { A::cholesky(self.square_layout()?, uplo, self.as_allocated_mut()?)? }; Ok(self.into_triangular(uplo)) } } impl<A, S> CholeskyMut for ArrayBase<S, Ix2> where A: Scalar, S: DataMut<Elem = A>, { fn cholesky_mut(&mut self, uplo: UPLO) -> Result<&mut Self> { unsafe { A::cholesky(self.square_layout()?, uplo, self.as_allocated_mut()?)? }; Ok(self.into_triangular(uplo)) } } impl<A, S> Cholesky for ArrayBase<S, Ix2> where A: Scalar, S: Data<Elem = A>, { type Output = Array2<A>; fn cholesky(&self, uplo: UPLO) -> Result<Self::Output> { let mut a = replicate(self); unsafe { A::cholesky(a.square_layout()?, uplo, a.as_allocated_mut()?)? }; Ok(a.into_triangular(uplo)) } }