Clean up CscCholesky

nalgebra-sparse/src/factorization/cholesky.rs

@@ -4,10 +4,9 @@
 use crate::pattern::SparsityPattern;
 use crate::csc::CscMatrix;
 use core::{mem, iter};
-use nalgebra::{U1, VectorN, Dynamic, Scalar, RealField};
+use nalgebra::{Scalar, RealField};
-use num_traits::Zero;
 use std::sync::Arc;
-use std::ops::Add;
+use std::fmt::{Display, Formatter};
 
 pub struct CscSymbolicCholesky {
     // Pattern of the original matrix that was decomposed
@@ -21,37 +20,11 @@ impl CscSymbolicCholesky {
     pub fn factor(pattern: &Arc<SparsityPattern>) -> Self {
         assert_eq!(pattern.major_dim(), pattern.minor_dim(),
             "Major and minor dimensions must be the same (square matrix).");
-
+        let (l_pattern, u_pattern) = nonzero_pattern(&*pattern);
-        // TODO: Temporary stopgap solution to make things work until we can refactor
-        #[derive(Copy, Clone, PartialEq, Eq, Debug)]
-        struct DummyVal;
-        impl Zero for DummyVal {
-            fn zero() -> Self {
-                DummyVal
-            }
-
-            fn is_zero(&self) -> bool {
-                true
-            }
-        }
-
-        impl Add<DummyVal> for DummyVal {
-            type Output = Self;
-
-            fn add(self, rhs: DummyVal) -> Self::Output {
-                rhs
-            }
-        }
-
-        let dummy_vals = vec![DummyVal; pattern.nnz()];
-        let dummy_csc = CscMatrix::try_from_pattern_and_values(Arc::clone(pattern), dummy_vals)
-            .unwrap();
-        let (l, u) = nonzero_pattern(&dummy_csc);
-        // TODO: Don't clone unnecessarily
         Self {
             m_pattern: Arc::clone(pattern),
-            l_pattern: l.pattern().as_ref().clone(),
+            l_pattern,
-            u_pattern: u.pattern().as_ref().clone()
+            u_pattern,
         }
     }
 
@@ -70,10 +43,20 @@ pub struct CscCholesky<T> {
 }
 
 #[derive(Debug, PartialEq, Eq, Clone)]
+#[non_exhaustive]
 pub enum CholeskyError {
-
+    /// The matrix is not positive definite.
+    NotPositiveDefinite,
 }
 
+impl Display for CholeskyError {
+    fn fmt(&self, f: &mut Formatter<'_>) -> std::fmt::Result {
+        write!(f, "Matrix is not positive definite")
+    }
+}
+
+impl std::error::Error for CholeskyError {}
+
 impl<T: RealField> CscCholesky<T> {
 
     pub fn factor(matrix: &CscMatrix<T>) -> Result<Self, CholeskyError> {
@@ -181,9 +164,7 @@ impl<T: RealField> CscCholesky<T> {
                         *self.work_x.get_unchecked_mut(p) = T::zero();
                     }
                 } else {
-                    // self.ok = false;
+                    return Err(CholeskyError::NotPositiveDefinite);
-                    // TODO: Return indefinite error (i.e. encountered non-positive diagonal
-                    unimplemented!()
                 }
             }
         }
@@ -196,8 +177,8 @@ impl<T: RealField> CscCholesky<T> {
 
 
 
-fn reach<T>(
+fn reach(
-    m: &CscMatrix<T>,
+    pattern: &SparsityPattern,
     j: usize,
     max_j: usize,
     tree: &[usize],
@@ -211,7 +192,7 @@ fn reach<T>(
     let mut tmp = Vec::new();
     let mut res = Vec::new();
 
-    for &irow in m.col(j).row_indices() {
+    for &irow in pattern.lane(j) {
         let mut curr = irow;
         while curr != usize::max_value() && curr <= max_j && !marks[curr] {
             marks[curr] = true;
@@ -223,57 +204,45 @@ fn reach<T>(
         mem::swap(&mut tmp, &mut res);
     }
 
-    // TODO: Is this right?
     res.sort_unstable();
 
     out.append(&mut res);
 }
 
-fn nonzero_pattern<T: Scalar + Zero>(
+fn nonzero_pattern(m: &SparsityPattern) -> (SparsityPattern, SparsityPattern) {
-    m: &CscMatrix<T>
-) -> (CscMatrix<T>, CscMatrix<T>) {
-    // TODO: In order to stay as faithful as possible to the original implementation,
-    // we here return full matrices, whereas we actually only need to construct sparsity patterns
-
     let etree = elimination_tree(m);
-    let (nrows, ncols) = (m.nrows(), m.ncols());
+    // Note: We assume CSC, therefore rows == minor and cols == major
+    let (nrows, ncols) = (m.minor_dim(), m.major_dim());
     let mut rows = Vec::with_capacity(m.nnz());
-    // TODO: Use a Vec here instead
+    let mut col_offsets = Vec::with_capacity(ncols + 1);
-    let mut cols = unsafe { VectorN::new_uninitialized_generic(Dynamic::new(nrows), U1) };
     let mut marks = Vec::new();
 
     // NOTE: the following will actually compute the non-zero pattern of
     // the transpose of l.
+    col_offsets.push(0);
     for i in 0..nrows {
-        cols[i] = rows.len();
         reach(m, i, i, &etree, &mut marks, &mut rows);
+        col_offsets.push(rows.len());
     }
 
-    // TODO: Get rid of this in particular
+    let u_pattern =  SparsityPattern::try_from_offsets_and_indices(nrows, ncols, col_offsets, rows)
-    let mut vals = Vec::with_capacity(rows.len());
+        .unwrap();
-    unsafe {
-        vals.set_len(rows.len());
-    }
-    vals.shrink_to_fit();
 
-    // TODO: Remove this unnecessary conversion by using Vec throughout
+    // TODO: Avoid this transpose?
-    let mut cols: Vec<_> = cols.iter().cloned().collect();
+    let l_pattern = u_pattern.transpose();
-    cols.push(rows.len());
 
-    let u = CscMatrix::try_from_csc_data(nrows, ncols, cols, rows, vals).unwrap();
+    (l_pattern, u_pattern)
-    // TODO: Avoid this transpose
-    let l = u.transpose();
-
-    (l, u)
 }
 
-fn elimination_tree<T>(m: &CscMatrix<T>) -> Vec<usize> {
+fn elimination_tree(pattern: &SparsityPattern) -> Vec<usize> {
-    let nrows = m.nrows();
+    // Note: The pattern is assumed to of a CSC matrix, so the number of rows is
+    // given by the minor dimension
+    let nrows = pattern.minor_dim();
     let mut forest: Vec<_> = iter::repeat(usize::max_value()).take(nrows).collect();
     let mut ancestor: Vec<_> = iter::repeat(usize::max_value()).take(nrows).collect();
 
     for k in 0..nrows {
-        for &irow in m.col(k).row_indices() {
+        for &irow in pattern.lane(k) {
             let mut i = irow;
 
             while i < k {