nalgebra/nalgebra-lapack/src/eigen.rs

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#[cfg(feature = "serde-serialize")]
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use serde::{Deserialize, Serialize};
use num::Zero;
use num_complex::Complex;
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use simba::scalar::RealField;
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use crate::ComplexHelper;
use na::allocator::Allocator;
use na::dimension::{Dim, U1};
use na::storage::Storage;
use na::{DefaultAllocator, Matrix, MatrixN, Scalar, VectorN};
use lapack;
/// Eigendecomposition of a real square matrix with real eigenvalues.
#[cfg_attr(feature = "serde-serialize", derive(Serialize, Deserialize))]
#[cfg_attr(
feature = "serde-serialize",
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serde(
bound(serialize = "DefaultAllocator: Allocator<N, D, D> + Allocator<N, D>,
VectorN<N, D>: Serialize,
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MatrixN<N, D>: Serialize")
)
)]
#[cfg_attr(
feature = "serde-serialize",
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serde(
bound(deserialize = "DefaultAllocator: Allocator<N, D, D> + Allocator<N, D>,
VectorN<N, D>: Serialize,
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MatrixN<N, D>: Deserialize<'de>")
)
)]
#[derive(Clone, Debug)]
pub struct Eigen<N: Scalar, D: Dim>
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where
DefaultAllocator: Allocator<N, D> + Allocator<N, D, D>,
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{
/// The eigenvalues of the decomposed matrix.
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pub eigenvalues: VectorN<N, D>,
/// The (right) eigenvectors of the decomposed matrix.
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pub eigenvectors: Option<MatrixN<N, D>>,
/// The left eigenvectors of the decomposed matrix.
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pub left_eigenvectors: Option<MatrixN<N, D>>,
}
impl<N: Scalar + Copy, D: Dim> Copy for Eigen<N, D>
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where
DefaultAllocator: Allocator<N, D> + Allocator<N, D, D>,
VectorN<N, D>: Copy,
MatrixN<N, D>: Copy,
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{
}
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impl<N: EigenScalar + RealField, D: Dim> Eigen<N, D>
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where
DefaultAllocator: Allocator<N, D, D> + Allocator<N, D>,
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{
/// Computes the eigenvalues and eigenvectors of the square matrix `m`.
///
/// If `eigenvectors` is `false` then, the eigenvectors are not computed explicitly.
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pub fn new(
mut m: MatrixN<N, D>,
left_eigenvectors: bool,
eigenvectors: bool,
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) -> Option<Eigen<N, D>> {
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assert!(
m.is_square(),
"Unable to compute the eigenvalue decomposition of a non-square matrix."
);
let ljob = if left_eigenvectors { b'V' } else { b'N' };
let rjob = if eigenvectors { b'V' } else { b'N' };
let (nrows, ncols) = m.data.shape();
let n = nrows.value();
let lda = n as i32;
let mut wr = unsafe { Matrix::new_uninitialized_generic(nrows, U1) };
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// TODO: Tap into the workspace.
let mut wi = unsafe { Matrix::new_uninitialized_generic(nrows, U1) };
let mut info = 0;
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let mut placeholder1 = [N::zero()];
let mut placeholder2 = [N::zero()];
let lwork = N::xgeev_work_size(
ljob,
rjob,
n as i32,
m.as_mut_slice(),
lda,
wr.as_mut_slice(),
wi.as_mut_slice(),
&mut placeholder1,
n as i32,
&mut placeholder2,
n as i32,
&mut info,
);
lapack_check!(info);
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let mut work = unsafe { crate::uninitialized_vec(lwork as usize) };
match (left_eigenvectors, eigenvectors) {
(true, true) => {
let mut vl = unsafe { Matrix::new_uninitialized_generic(nrows, ncols) };
let mut vr = unsafe { Matrix::new_uninitialized_generic(nrows, ncols) };
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N::xgeev(
ljob,
rjob,
n as i32,
m.as_mut_slice(),
lda,
wr.as_mut_slice(),
wi.as_mut_slice(),
&mut vl.as_mut_slice(),
n as i32,
&mut vr.as_mut_slice(),
n as i32,
&mut work,
lwork,
&mut info,
);
lapack_check!(info);
if wi.iter().all(|e| e.is_zero()) {
return Some(Self {
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eigenvalues: wr,
left_eigenvectors: Some(vl),
eigenvectors: Some(vr),
});
}
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}
(true, false) => {
let mut vl = unsafe { Matrix::new_uninitialized_generic(nrows, ncols) };
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N::xgeev(
ljob,
rjob,
n as i32,
m.as_mut_slice(),
lda,
wr.as_mut_slice(),
wi.as_mut_slice(),
&mut vl.as_mut_slice(),
n as i32,
&mut placeholder2,
1 as i32,
&mut work,
lwork,
&mut info,
);
lapack_check!(info);
if wi.iter().all(|e| e.is_zero()) {
return Some(Self {
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eigenvalues: wr,
left_eigenvectors: Some(vl),
eigenvectors: None,
});
}
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}
(false, true) => {
let mut vr = unsafe { Matrix::new_uninitialized_generic(nrows, ncols) };
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N::xgeev(
ljob,
rjob,
n as i32,
m.as_mut_slice(),
lda,
wr.as_mut_slice(),
wi.as_mut_slice(),
&mut placeholder1,
1 as i32,
&mut vr.as_mut_slice(),
n as i32,
&mut work,
lwork,
&mut info,
);
lapack_check!(info);
if wi.iter().all(|e| e.is_zero()) {
return Some(Self {
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eigenvalues: wr,
left_eigenvectors: None,
eigenvectors: Some(vr),
});
}
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}
(false, false) => {
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N::xgeev(
ljob,
rjob,
n as i32,
m.as_mut_slice(),
lda,
wr.as_mut_slice(),
wi.as_mut_slice(),
&mut placeholder1,
1 as i32,
&mut placeholder2,
1 as i32,
&mut work,
lwork,
&mut info,
);
lapack_check!(info);
if wi.iter().all(|e| e.is_zero()) {
return Some(Self {
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eigenvalues: wr,
left_eigenvectors: None,
eigenvectors: None,
});
}
}
}
None
}
/// The complex eigenvalues of the given matrix.
///
/// Panics if the eigenvalue computation does not converge.
pub fn complex_eigenvalues(mut m: MatrixN<N, D>) -> VectorN<Complex<N>, D>
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where
DefaultAllocator: Allocator<Complex<N>, D>,
{
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assert!(
m.is_square(),
"Unable to compute the eigenvalue decomposition of a non-square matrix."
);
let nrows = m.data.shape().0;
let n = nrows.value();
let lda = n as i32;
let mut wr = unsafe { Matrix::new_uninitialized_generic(nrows, U1) };
let mut wi = unsafe { Matrix::new_uninitialized_generic(nrows, U1) };
let mut info = 0;
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let mut placeholder1 = [N::zero()];
let mut placeholder2 = [N::zero()];
let lwork = N::xgeev_work_size(
b'N',
b'N',
n as i32,
m.as_mut_slice(),
lda,
wr.as_mut_slice(),
wi.as_mut_slice(),
&mut placeholder1,
n as i32,
&mut placeholder2,
n as i32,
&mut info,
);
lapack_panic!(info);
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let mut work = unsafe { crate::uninitialized_vec(lwork as usize) };
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N::xgeev(
b'N',
b'N',
n as i32,
m.as_mut_slice(),
lda,
wr.as_mut_slice(),
wi.as_mut_slice(),
&mut placeholder1,
1 as i32,
&mut placeholder2,
1 as i32,
&mut work,
lwork,
&mut info,
);
lapack_panic!(info);
let mut res = unsafe { Matrix::new_uninitialized_generic(nrows, U1) };
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for i in 0..res.len() {
res[i] = Complex::new(wr[i], wi[i]);
}
res
}
/// The determinant of the decomposed matrix.
#[inline]
pub fn determinant(&self) -> N {
let mut det = N::one();
for e in self.eigenvalues.iter() {
det *= *e;
}
det
}
}
/*
*
* Lapack functions dispatch.
*
*/
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/// Trait implemented by scalar type for which Lapack function exist to compute the
/// eigendecomposition.
pub trait EigenScalar: Scalar {
#[allow(missing_docs)]
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fn xgeev(
jobvl: u8,
jobvr: u8,
n: i32,
a: &mut [Self],
lda: i32,
wr: &mut [Self],
wi: &mut [Self],
vl: &mut [Self],
ldvl: i32,
vr: &mut [Self],
ldvr: i32,
work: &mut [Self],
lwork: i32,
info: &mut i32,
);
#[allow(missing_docs)]
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fn xgeev_work_size(
jobvl: u8,
jobvr: u8,
n: i32,
a: &mut [Self],
lda: i32,
wr: &mut [Self],
wi: &mut [Self],
vl: &mut [Self],
ldvl: i32,
vr: &mut [Self],
ldvr: i32,
info: &mut i32,
) -> i32;
}
macro_rules! real_eigensystem_scalar_impl (
($N: ty, $xgeev: path) => (
impl EigenScalar for $N {
#[inline]
fn xgeev(jobvl: u8, jobvr: u8, n: i32, a: &mut [Self], lda: i32,
wr: &mut [Self], wi: &mut [Self],
vl: &mut [Self], ldvl: i32, vr: &mut [Self], ldvr: i32,
work: &mut [Self], lwork: i32, info: &mut i32) {
unsafe { $xgeev(jobvl, jobvr, n, a, lda, wr, wi, vl, ldvl, vr, ldvr, work, lwork, info) }
}
#[inline]
fn xgeev_work_size(jobvl: u8, jobvr: u8, n: i32, a: &mut [Self], lda: i32,
wr: &mut [Self], wi: &mut [Self], vl: &mut [Self], ldvl: i32,
vr: &mut [Self], ldvr: i32, info: &mut i32) -> i32 {
let mut work = [ Zero::zero() ];
let lwork = -1 as i32;
unsafe { $xgeev(jobvl, jobvr, n, a, lda, wr, wi, vl, ldvl, vr, ldvr, &mut work, lwork, info) };
ComplexHelper::real_part(work[0]) as i32
}
}
)
);
real_eigensystem_scalar_impl!(f32, lapack::sgeev);
real_eigensystem_scalar_impl!(f64, lapack::dgeev);
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//// TODO: decomposition of complex matrix and matrices with complex eigenvalues.
// eigensystem_complex_impl!(f32, lapack::cgeev);
// eigensystem_complex_impl!(f64, lapack::zgeev);