nalgebra/src/linalg/full_piv_lu.rs
2019-03-23 14:33:47 +01:00

274 lines
8.6 KiB
Rust

#[cfg(feature = "serde-serialize")]
use serde::{Deserialize, Serialize};
use alga::general::Complex;
use crate::allocator::Allocator;
use crate::base::{DefaultAllocator, Matrix, MatrixMN, MatrixN};
use crate::constraint::{SameNumberOfRows, ShapeConstraint};
use crate::dimension::{Dim, DimMin, DimMinimum};
use crate::storage::{Storage, StorageMut};
use crate::linalg::lu;
use crate::linalg::PermutationSequence;
/// LU decomposition with full row and column pivoting.
#[cfg_attr(feature = "serde-serialize", derive(Serialize, Deserialize))]
#[cfg_attr(
feature = "serde-serialize",
serde(bound(
serialize = "DefaultAllocator: Allocator<N, R, C> +
Allocator<(usize, usize), DimMinimum<R, C>>,
MatrixMN<N, R, C>: Serialize,
PermutationSequence<DimMinimum<R, C>>: Serialize"
))
)]
#[cfg_attr(
feature = "serde-serialize",
serde(bound(
deserialize = "DefaultAllocator: Allocator<N, R, C> +
Allocator<(usize, usize), DimMinimum<R, C>>,
MatrixMN<N, R, C>: Deserialize<'de>,
PermutationSequence<DimMinimum<R, C>>: Deserialize<'de>"
))
)]
#[derive(Clone, Debug)]
pub struct FullPivLU<N: Complex, R: DimMin<C>, C: Dim>
where DefaultAllocator: Allocator<N, R, C> + Allocator<(usize, usize), DimMinimum<R, C>>
{
lu: MatrixMN<N, R, C>,
p: PermutationSequence<DimMinimum<R, C>>,
q: PermutationSequence<DimMinimum<R, C>>,
}
impl<N: Complex, R: DimMin<C>, C: Dim> Copy for FullPivLU<N, R, C>
where
DefaultAllocator: Allocator<N, R, C> + Allocator<(usize, usize), DimMinimum<R, C>>,
MatrixMN<N, R, C>: Copy,
PermutationSequence<DimMinimum<R, C>>: Copy,
{}
impl<N: Complex, R: DimMin<C>, C: Dim> FullPivLU<N, R, C>
where DefaultAllocator: Allocator<N, R, C> + Allocator<(usize, usize), DimMinimum<R, C>>
{
/// Computes the LU decomposition with full pivoting of `matrix`.
///
/// This effectively computes `P, L, U, Q` such that `P * matrix * Q = LU`.
pub fn new(mut matrix: MatrixMN<N, R, C>) -> Self {
let (nrows, ncols) = matrix.data.shape();
let min_nrows_ncols = nrows.min(ncols);
let mut p = PermutationSequence::identity_generic(min_nrows_ncols);
let mut q = PermutationSequence::identity_generic(min_nrows_ncols);
if min_nrows_ncols.value() == 0 {
return Self {
lu: matrix,
p: p,
q: q,
};
}
for i in 0..min_nrows_ncols.value() {
let piv = matrix.slice_range(i.., i..).icamax_full();
let row_piv = piv.0 + i;
let col_piv = piv.1 + i;
let diag = matrix[(row_piv, col_piv)];
if diag.is_zero() {
// The remaining of the matrix is zero.
break;
}
matrix.swap_columns(i, col_piv);
q.append_permutation(i, col_piv);
if row_piv != i {
p.append_permutation(i, row_piv);
matrix.columns_range_mut(..i).swap_rows(i, row_piv);
lu::gauss_step_swap(&mut matrix, diag, i, row_piv);
} else {
lu::gauss_step(&mut matrix, diag, i);
}
}
Self {
lu: matrix,
p: p,
q: q,
}
}
#[doc(hidden)]
pub fn lu_internal(&self) -> &MatrixMN<N, R, C> {
&self.lu
}
/// The lower triangular matrix of this decomposition.
#[inline]
pub fn l(&self) -> MatrixMN<N, R, DimMinimum<R, C>>
where DefaultAllocator: Allocator<N, R, DimMinimum<R, C>> {
let (nrows, ncols) = self.lu.data.shape();
let mut m = self.lu.columns_generic(0, nrows.min(ncols)).into_owned();
m.fill_upper_triangle(N::zero(), 1);
m.fill_diagonal(N::one());
m
}
/// The upper triangular matrix of this decomposition.
#[inline]
pub fn u(&self) -> MatrixMN<N, DimMinimum<R, C>, C>
where DefaultAllocator: Allocator<N, DimMinimum<R, C>, C> {
let (nrows, ncols) = self.lu.data.shape();
self.lu.rows_generic(0, nrows.min(ncols)).upper_triangle()
}
/// The row permutations of this decomposition.
#[inline]
pub fn p(&self) -> &PermutationSequence<DimMinimum<R, C>> {
&self.p
}
/// The column permutations of this decomposition.
#[inline]
pub fn q(&self) -> &PermutationSequence<DimMinimum<R, C>> {
&self.q
}
/// The two matrices of this decomposition and the row and column permutations: `(P, L, U, Q)`.
#[inline]
pub fn unpack(
self,
) -> (
PermutationSequence<DimMinimum<R, C>>,
MatrixMN<N, R, DimMinimum<R, C>>,
MatrixMN<N, DimMinimum<R, C>, C>,
PermutationSequence<DimMinimum<R, C>>,
)
where DefaultAllocator: Allocator<N, R, DimMinimum<R, C>> + Allocator<N, DimMinimum<R, C>, C>
{
// Use reallocation for either l or u.
let l = self.l();
let u = self.u();
let p = self.p;
let q = self.q;
(p, l, u, q)
}
}
impl<N: Complex, D: DimMin<D, Output = D>> FullPivLU<N, D, D>
where DefaultAllocator: Allocator<N, D, D> + Allocator<(usize, usize), D>
{
/// Solves the linear system `self * x = b`, where `x` is the unknown to be determined.
///
/// Returns `None` if the decomposed matrix is not invertible.
pub fn solve<R2: Dim, C2: Dim, S2>(
&self,
b: &Matrix<N, R2, C2, S2>,
) -> Option<MatrixMN<N, R2, C2>>
where
S2: StorageMut<N, R2, C2>,
ShapeConstraint: SameNumberOfRows<R2, D>,
DefaultAllocator: Allocator<N, R2, C2>,
{
let mut res = b.clone_owned();
if self.solve_mut(&mut res) {
Some(res)
} else {
None
}
}
/// Solves the linear system `self * x = b`, where `x` is the unknown to be determined.
///
/// If the decomposed matrix is not invertible, this returns `false` and its input `b` may
/// be overwritten with garbage.
pub fn solve_mut<R2: Dim, C2: Dim, S2>(&self, b: &mut Matrix<N, R2, C2, S2>) -> bool
where
S2: StorageMut<N, R2, C2>,
ShapeConstraint: SameNumberOfRows<R2, D>,
{
assert_eq!(
self.lu.nrows(),
b.nrows(),
"FullPivLU solve matrix dimension mismatch."
);
assert!(
self.lu.is_square(),
"FullPivLU solve: unable to solve a non-square system."
);
if self.is_invertible() {
self.p.permute_rows(b);
let _ = self.lu.solve_lower_triangular_with_diag_mut(b, N::one());
let _ = self.lu.solve_upper_triangular_mut(b);
self.q.inv_permute_rows(b);
true
} else {
false
}
}
/// Computes the inverse of the decomposed matrix.
///
/// Returns `None` if the decomposed matrix is not invertible.
pub fn try_inverse(&self) -> Option<MatrixN<N, D>> {
assert!(
self.lu.is_square(),
"FullPivLU inverse: unable to compute the inverse of a non-square matrix."
);
let (nrows, ncols) = self.lu.data.shape();
let mut res = MatrixN::identity_generic(nrows, ncols);
if self.solve_mut(&mut res) {
Some(res)
} else {
None
}
}
/// Indicates if the decomposed matrix is invertible.
pub fn is_invertible(&self) -> bool {
assert!(
self.lu.is_square(),
"FullPivLU: unable to test the invertibility of a non-square matrix."
);
let dim = self.lu.nrows();
!self.lu[(dim - 1, dim - 1)].is_zero()
}
/// Computes the determinant of the decomposed matrix.
pub fn determinant(&self) -> N {
assert!(
self.lu.is_square(),
"FullPivLU determinant: unable to compute the determinant of a non-square matrix."
);
let dim = self.lu.nrows();
let mut res = self.lu[(dim - 1, dim - 1)];
if !res.is_zero() {
for i in 0..dim - 1 {
res *= unsafe { *self.lu.get_unchecked((i, i)) };
}
res * self.p.determinant() * self.q.determinant()
} else {
N::zero()
}
}
}
impl<N: Complex, R: DimMin<C>, C: Dim, S: Storage<N, R, C>> Matrix<N, R, C, S>
where DefaultAllocator: Allocator<N, R, C> + Allocator<(usize, usize), DimMinimum<R, C>>
{
/// Computes the LU decomposition with full pivoting of `matrix`.
///
/// This effectively computes `P, L, U, Q` such that `P * matrix * Q = LU`.
pub fn full_piv_lu(self) -> FullPivLU<N, R, C> {
FullPivLU::new(self.into_owned())
}
}