Fix nalgebra-sparse.
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148b164aaa
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@ -3,7 +3,7 @@ use crate::ops::serial::spsolve_csc_lower_triangular;
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use crate::ops::Op;
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use crate::pattern::SparsityPattern;
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use core::{iter, mem};
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use nalgebra::{DMatrix, DMatrixSlice, DMatrixSliceMut, RealField, Scalar};
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use nalgebra::{DMatrix, DMatrixSlice, DMatrixSliceMut, RealField};
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use std::fmt::{Display, Formatter};
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/// A symbolic sparse Cholesky factorization of a CSC matrix.
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@ -209,15 +209,16 @@ impl<T: RealField> CscCholesky<T> {
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let irow = *self.m_pattern.minor_indices().get_unchecked(p);
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if irow >= k {
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*self.work_x.get_unchecked_mut(irow) = *values.get_unchecked(p);
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*self.work_x.get_unchecked_mut(irow) = values.get_unchecked(p).clone();
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}
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}
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for &j in self.u_pattern.lane(k) {
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let factor = -*self
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let factor = -self
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.l_factor
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.values()
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.get_unchecked(*self.work_c.get_unchecked(j));
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.get_unchecked(*self.work_c.get_unchecked(j))
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.clone();
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*self.work_c.get_unchecked_mut(j) += 1;
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if j < k {
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@ -225,27 +226,27 @@ impl<T: RealField> CscCholesky<T> {
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let col_j_entries = col_j.row_indices().iter().zip(col_j.values());
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for (&z, val) in col_j_entries {
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if z >= k {
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*self.work_x.get_unchecked_mut(z) += val.clone() * factor;
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*self.work_x.get_unchecked_mut(z) += val.clone() * factor.clone();
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}
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}
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}
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}
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let diag = *self.work_x.get_unchecked(k);
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let diag = self.work_x.get_unchecked(k).clone();
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if diag > T::zero() {
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let denom = diag.sqrt();
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{
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let (offsets, _, values) = self.l_factor.csc_data_mut();
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*values.get_unchecked_mut(*offsets.get_unchecked(k)) = denom;
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*values.get_unchecked_mut(*offsets.get_unchecked(k)) = denom.clone();
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}
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let mut col_k = self.l_factor.col_mut(k);
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let (col_k_rows, col_k_values) = col_k.rows_and_values_mut();
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let col_k_entries = col_k_rows.iter().zip(col_k_values);
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for (&p, val) in col_k_entries {
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*val = *self.work_x.get_unchecked(p) / denom;
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*val = self.work_x.get_unchecked(p).clone() / denom.clone();
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*self.work_x.get_unchecked_mut(p) = T::zero();
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}
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} else {
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@ -165,13 +165,13 @@ fn spsolve_csc_lower_triangular_no_transpose<T: RealField>(
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// a severe penalty)
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let diag_csc_index = l_col_k.row_indices().iter().position(|&i| i == k);
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if let Some(diag_csc_index) = diag_csc_index {
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let l_kk = l_col_k.values()[diag_csc_index];
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let l_kk = l_col_k.values()[diag_csc_index].clone();
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if l_kk != T::zero() {
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// Update entry associated with diagonal
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x_col_j[k] /= l_kk;
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// Copy value after updating (so we don't run into the borrow checker)
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let x_kj = x_col_j[k];
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let x_kj = x_col_j[k].clone();
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let row_indices = &l_col_k.row_indices()[(diag_csc_index + 1)..];
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let l_values = &l_col_k.values()[(diag_csc_index + 1)..];
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@ -179,7 +179,7 @@ fn spsolve_csc_lower_triangular_no_transpose<T: RealField>(
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// Note: The remaining entries are below the diagonal
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for (&i, l_ik) in row_indices.iter().zip(l_values) {
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let x_ij = &mut x_col_j[i];
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*x_ij -= l_ik.clone() * x_kj;
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*x_ij -= l_ik.clone() * x_kj.clone();
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}
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x_col_j[k] = x_kj;
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@ -223,22 +223,22 @@ fn spsolve_csc_lower_triangular_transpose<T: RealField>(
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// TODO: Can use exponential search here to quickly skip entries
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let diag_csc_index = l_col_i.row_indices().iter().position(|&k| i == k);
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if let Some(diag_csc_index) = diag_csc_index {
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let l_ii = l_col_i.values()[diag_csc_index];
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let l_ii = l_col_i.values()[diag_csc_index].clone();
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if l_ii != T::zero() {
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// // Update entry associated with diagonal
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// x_col_j[k] /= a_kk;
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// Copy value after updating (so we don't run into the borrow checker)
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let mut x_ii = x_col_j[i];
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let mut x_ii = x_col_j[i].clone();
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let row_indices = &l_col_i.row_indices()[(diag_csc_index + 1)..];
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let a_values = &l_col_i.values()[(diag_csc_index + 1)..];
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// Note: The remaining entries are below the diagonal
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for (&k, &l_ki) in row_indices.iter().zip(a_values) {
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let x_kj = x_col_j[k];
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x_ii -= l_ki * x_kj;
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for (k, l_ki) in row_indices.iter().zip(a_values) {
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let x_kj = x_col_j[*k].clone();
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x_ii -= l_ki.clone() * x_kj;
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}
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x_col_j[i] = x_ii / l_ii;
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