finished cleaning

src/linalg/cholesky.rs

@@ -8,7 +8,6 @@ use crate::base::{DefaultAllocator, Matrix, MatrixMN, MatrixN, SquareMatrix, Vec
 use crate::constraint::{SameNumberOfRows, ShapeConstraint};
 use crate::dimension::{Dim, DimAdd, DimSum, DimDiff, DimSub, Dynamic, U1};
 use crate::storage::{Storage, StorageMut};
-use crate::base::allocator::Reallocator;
 
 /// The Cholesky decomposition of a symmetric-definite-positive matrix.
 #[cfg_attr(feature = "serde-serialize", derive(Serialize, Deserialize))]
@@ -156,7 +155,7 @@ where
         DefaultAllocator: Allocator<N, R2, U1>,
         ShapeConstraint: SameNumberOfRows<R2, D>,
     {
-        rank_one_update(&mut self.chol, x, sigma)
+        Self::xx_rank_one_update(&mut self.chol, x, sigma)
     }
 
     /// Updates the decomposition such that we get the decomposition of a matrix with the given column `col` in the `j`th position.
@@ -170,7 +169,7 @@ where
         D: DimAdd<U1>,
         R2: Dim,
         S2: Storage<N, R2, U1>,
-        DefaultAllocator: Reallocator<N, D, D, D, DimSum<D, U1>> + Reallocator<N, D, DimSum<D, U1>, DimSum<D, U1>, DimSum<D, U1>>,
+        DefaultAllocator: Allocator<N, DimSum<D, U1>, DimSum<D, U1>>,
         ShapeConstraint: SameNumberOfRows<R2, DimSum<D, U1>>,
     {
         // for an explanation of the formulas, see https://en.wikipedia.org/wiki/Cholesky_decomposition#Updating_the_decomposition
@@ -178,8 +177,12 @@ where
         assert_eq!(n, self.chol.nrows() + 1, "The new column must have the size of the factored matrix plus one.");
         assert!(j < n, "j needs to be within the bound of the new matrix.");
 
-        // TODO what is the fastest way to produce the new matrix ?
+        // loads the data into a new matrix with an additional jth row/column
-        let mut chol= self.chol.clone().insert_column(j, N::zero()).insert_row(j, N::zero());
+        let mut chol = unsafe { Matrix::new_uninitialized_generic(self.chol.data.shape().0.add(U1), self.chol.data.shape().1.add(U1)) };
+        chol.slice_range_mut(..j, ..j).copy_from(&self.chol.slice_range(..j, ..j));
+        chol.slice_range_mut(..j, j+1..).copy_from(&self.chol.slice_range(..j, j..));
+        chol.slice_range_mut(j+1.., ..j).copy_from(&self.chol.slice_range(j.., ..j));
+        chol.slice_range_mut(j+1.., j+1..).copy_from(&self.chol.slice_range(j.., j..));
 
         // update the jth row
         let top_left_corner = self.chol.slice_range(..j, ..j);
@@ -200,7 +203,7 @@ where
 
         // update the bottom right corner
         let mut bottom_right_corner = chol.slice_range_mut(j+1.., j+1..);
-        rank_one_update(&mut bottom_right_corner, &new_colj, -N::real(N::one()));
+        Self::xx_rank_one_update(&mut bottom_right_corner, &new_colj, -N::real(N::one()));
 
         Cholesky { chol }
     }
@@ -208,53 +211,43 @@ where
     /// Updates the decomposition such that we get the decomposition of the factored matrix with its `j`th column removed.
     /// Since the matrix is square, the `j`th row will also be removed.
     pub fn remove_column(
-        self,
+        &self,
         j: usize,
     ) -> Cholesky<N, DimDiff<D, U1>>
         where
             D: DimSub<U1>,
-            DefaultAllocator: Reallocator<N, D, D, D, DimDiff<D, U1>> + Reallocator<N, D, DimDiff<D, U1>, DimDiff<D, U1>, DimDiff<D, U1>>,
+            DefaultAllocator: Allocator<N, DimDiff<D, U1>, DimDiff<D, U1>>
     {
         let n = self.chol.nrows();
         assert!(n > 0, "The matrix needs at least one column.");
         assert!(j < n, "j needs to be within the bound of the matrix.");
 
-        // TODO what is the fastest way to produce the new matrix ?
+        // loads the data into a new matrix except for the jth row/column
-        let mut chol= self.chol.clone().remove_column(j).remove_row(j);
+        let mut chol = unsafe { Matrix::new_uninitialized_generic(self.chol.data.shape().0.sub(U1), self.chol.data.shape().1.sub(U1)) };
+        chol.slice_range_mut(..j, ..j).copy_from(&self.chol.slice_range(..j, ..j));
+        chol.slice_range_mut(..j, j..).copy_from(&self.chol.slice_range(..j, j+1..));
+        chol.slice_range_mut(j.., ..j).copy_from(&self.chol.slice_range(j+1.., ..j));
+        chol.slice_range_mut(j.., j..).copy_from(&self.chol.slice_range(j+1.., j+1..));
 
         // 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);
-        rank_one_update(&mut bottom_right_corner, &old_colj, N::real(N::one()));
+        Self::xx_rank_one_update(&mut bottom_right_corner, &old_colj, N::real(N::one()));
 
         Cholesky { chol }
     }
-}
-
-impl<N: ComplexField, D: DimSub<Dynamic>, S: Storage<N, D, D>> SquareMatrix<N, D, S>
-where
-    DefaultAllocator: Allocator<N, D, D>,
-{
-    /// Attempts to compute the Cholesky decomposition of this matrix.
-    ///
-    /// Returns `None` if the input matrix is not definite-positive. The input matrix is assumed
-    /// to be symmetric and only the lower-triangular part is read.
-    pub fn cholesky(self) -> Option<Cholesky<N, D>> {
-        Cholesky::new(self.into_owned())
-    }
-}
 
     /// 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()`.
     ///
     /// 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 rank_one_update<N, D, S, Rx, Sx>(chol : &mut Matrix<N, D, D, S>, 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: &Vector<N, Rx, Sx>, sigma: N::RealField)
         where
-        N: ComplexField,
+            //N: ComplexField,
-        D: Dim,
+            Dm: Dim,
             Rx: Dim,
-        S: StorageMut<N, D, D>,
+            Sm: StorageMut<N, Dm, Dm>,
             Sx: Storage<N, Rx, U1>,
             DefaultAllocator: Allocator<N, Rx, U1>,
     {
@@ -292,3 +285,17 @@ fn rank_one_update<N, D, S, Rx, Sx>(chol : &mut Matrix<N, D, D, S>, x: &Vector<N
             }
         }
     }
+}
+
+impl<N: ComplexField, D: DimSub<Dynamic>, S: Storage<N, D, D>> SquareMatrix<N, D, S>
+where
+    DefaultAllocator: Allocator<N, D, D>,
+{
+    /// Attempts to compute the Cholesky decomposition of this matrix.
+    ///
+    /// Returns `None` if the input matrix is not definite-positive. The input matrix is assumed
+    /// to be symmetric and only the lower-triangular part is read.
+    pub fn cholesky(self) -> Option<Cholesky<N, D>> {
+        Cholesky::new(self.into_owned())
+    }
+}

tests/linalg/cholesky.rs

@@ -100,7 +100,7 @@ macro_rules! gen_tests(
                 }
 
                 fn cholesky_insert_column(n: usize) -> bool {
-                    let n = n.max(1).min(50);
+                    let n = n.max(1).min(10);
                     let j = random::<usize>() % n;
                     let m_updated = RandomSDP::new(Dynamic::new(n), || random::<$scalar>().0).unwrap();
 
@@ -112,15 +112,11 @@ macro_rules! gen_tests(
                     let chol = m.clone().cholesky().unwrap().insert_column(j, &col);
                     let m_chol_updated = chol.l() * chol.l().adjoint();
 
-                    println!("n={} j={}", n, j);
-                    println!("chol updated:{}", m_chol_updated);
-                    println!("m updated:{}", m_updated);
-
                     relative_eq!(m_updated, m_chol_updated, epsilon = 1.0e-7)
                 }
 
                 fn cholesky_remove_column(n: usize) -> bool {
-                    let n = n.max(1).min(5);
+                    let n = n.max(1).min(10);
                     let j = random::<usize>() % n;
                     let m = RandomSDP::new(Dynamic::new(n), || random::<$scalar>().0).unwrap();