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Fix Cholesky for no-std platforms.

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sebcrozet 2019-11-17 13:10:50 +01:00 committed by Sébastien Crozet
parent 59c6a98615
commit b96159aab3
2 changed files with 25 additions and 20 deletions
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43
src/linalg/cholesky.rs
View File

@ -1,6 +1,7 @@
#[cfg(feature = "serde-serialize")]
use serde::{Deserialize, Serialize};
use num::One;
use alga::general::ComplexField;
use crate::allocator::Allocator;
@ -155,23 +156,24 @@ where
DefaultAllocator: Allocator<N, R2, U1>,
ShapeConstraint: SameNumberOfRows<R2, D>,
{
Self::xx_rank_one_update(&mut self.chol, x, sigma)
Self::xx_rank_one_update(&mut self.chol, &mut x.clone_owned(), sigma)
}
/// Updates the decomposition such that we get the decomposition of a matrix with the given column `col` in the `j`th position.
/// Since the matrix is square, an identical row will be added in the `j`th row.
pub fn insert_column<R2, S2>(
self,
&self,
j: usize,
col: &Vector<N, R2, S2>,
col: Vector<N, R2, S2>,
) -> Cholesky<N, DimSum<D, U1>>
where
D: DimAdd<U1>,
R2: Dim,
S2: Storage<N, R2, U1>,
DefaultAllocator: Allocator<N, DimSum<D, U1>, DimSum<D, U1>>,
DefaultAllocator: Allocator<N, DimSum<D, U1>, DimSum<D, U1>> + Allocator<N, R2>,
ShapeConstraint: SameNumberOfRows<R2, DimSum<D, U1>>,
{
let mut col = col.into_owned();
// for an explanation of the formulas, see https://en.wikipedia.org/wiki/Cholesky_decomposition#Updating_the_decomposition
let n = col.nrows();
assert_eq!(n, self.chol.nrows() + 1, "The new column must have the size of the factored matrix plus one.");
@ -186,24 +188,26 @@ where
// update the jth row
let top_left_corner = self.chol.slice_range(..j, ..j);
let col_jminus = col.rows_range(..j);
let new_rowj_adjoint = top_left_corner.solve_lower_triangular(&col_jminus).expect("Cholesky::insert_column : Unable to solve lower triangular system!");
let col_j = col[j];
let (mut new_rowj_adjoint, mut new_colj) = col.rows_range_pair_mut(..j, j + 1..);
assert!(top_left_corner.solve_lower_triangular_mut(&mut new_rowj_adjoint), "Cholesky::insert_column : Unable to solve lower triangular system!");
new_rowj_adjoint.adjoint_to(&mut chol.slice_range_mut(j, ..j));
// update the center element
let center_element = N::sqrt(col[j] - N::from_real(new_rowj_adjoint.norm_squared()));
chol[(j,j)] = center_element;
let center_element = N::sqrt(col_j - N::from_real(new_rowj_adjoint.norm_squared()));
chol[(j, j)] = center_element;
// update the jth column
let bottom_left_corner = self.chol.slice_range(j.., ..j);
// new_colj = (col_jplus - bottom_left_corner * new_rowj.adjoint()) / center_element;
let mut new_colj = col.rows_range(j+1..).clone_owned();
new_colj.gemm(-N::one() / center_element, &bottom_left_corner, &new_rowj_adjoint, N::one() / center_element );
new_colj.gemm(-N::one() / center_element, &bottom_left_corner, &new_rowj_adjoint, N::one() / center_element);
chol.slice_range_mut(j+1.., j).copy_from(&new_colj);
// update the bottom right corner
let mut bottom_right_corner = chol.slice_range_mut(j+1.., j+1..);
Self::xx_rank_one_update(&mut bottom_right_corner, &new_colj, -N::real(N::one()));
Self::xx_rank_one_update(&mut bottom_right_corner, &mut new_colj, -N::RealField::one());
Cholesky { chol }
}
@ -216,7 +220,7 @@ where
) -> Cholesky<N, DimDiff<D, U1>>
where
D: DimSub<U1>,
DefaultAllocator: Allocator<N, DimDiff<D, U1>, DimDiff<D, U1>>
DefaultAllocator: Allocator<N, DimDiff<D, U1>, DimDiff<D, U1>> + Allocator<N, D>
{
let n = self.chol.nrows();
assert!(n > 0, "The matrix needs at least one column.");
@ -231,25 +235,25 @@ where
// updates the bottom right corner
let mut bottom_right_corner = chol.slice_range_mut(j.., j..);
let old_colj = self.chol.slice_range(j+1.., j);
Self::xx_rank_one_update(&mut bottom_right_corner, &old_colj, N::real(N::one()));
let mut workspace = self.chol.column(j).clone_owned();
let mut old_colj = workspace.rows_range_mut(j+1..);
Self::xx_rank_one_update(&mut bottom_right_corner, &mut old_colj, N::RealField::one());
Cholesky { chol }
}
/// Given the Cholesky decomposition of a matrix `M`, a scalar `sigma` and a vector `v`,
/// performs a rank one update such that we end up with the decomposition of `M + sigma * v*v.adjoint()`.
/// performs a rank one update such that we end up with the decomposition of `M + sigma * x*x.adjoint()`.
///
/// This helper method is calling for by `rank_one_update` but also `insert_column` and `remove_column`
/// where it is used on a square slice of the decomposition
fn xx_rank_one_update<Dm, Sm, Rx, Sx>(chol : &mut Matrix<N, Dm, Dm, Sm>, x: &Vector<N, Rx, Sx>, sigma: N::RealField)
fn xx_rank_one_update<Dm, Sm, Rx, Sx>(chol : &mut Matrix<N, Dm, Dm, Sm>, x: &mut Vector<N, Rx, Sx>, sigma: N::RealField)
where
//N: ComplexField,
Dm: Dim,
Rx: Dim,
Sm: StorageMut<N, Dm, Dm>,
Sx: Storage<N, Rx, U1>,
DefaultAllocator: Allocator<N, Rx, U1>,
Sx: StorageMut<N, Rx, U1>,
{
// heavily inspired by Eigen's `llt_rank_update_lower` implementation https://eigen.tuxfamily.org/dox/LLT_8h_source.html
let n = x.nrows();
@ -258,8 +262,9 @@ where
chol.nrows(),
"The input vector must be of the same size as the factorized matrix."
);
let mut x = x.clone_owned();
let mut beta = crate::one::<N::RealField>();
for j in 0..n {
// updates the diagonal
let diag = N::real(unsafe { *chol.get_unchecked((j, j)) });

2
tests/linalg/cholesky.rs
View File

@ -109,7 +109,7 @@ macro_rules! gen_tests(
let m = m_updated.clone().remove_column(j).remove_row(j);
// remove column from cholesky decomposition and rebuild m
let chol = m.clone().cholesky().unwrap().insert_column(j, &col);
let chol = m.clone().cholesky().unwrap().insert_column(j, col);
let m_chol_updated = chol.l() * chol.l().adjoint();
relative_eq!(m_updated, m_chol_updated, epsilon = 1.0e-7)