pub struct CscMatrix<T> { /* private fields */ }
Expand description
A CSC representation of a sparse matrix.
The Compressed Sparse Column (CSC) format is well-suited as a general-purpose storage format for many sparse matrix applications.
§Usage
use nalgebra_sparse::coo::CooMatrix;
use nalgebra_sparse::csc::CscMatrix;
use nalgebra::{DMatrix, Matrix3x4};
use matrixcompare::assert_matrix_eq;
// The sparsity patterns of CSC matrices are immutable. This means that you cannot dynamically
// change the sparsity pattern of the matrix after it has been constructed. The easiest
// way to construct a CSC matrix is to first incrementally construct a COO matrix,
// and then convert it to CSC.
let mut coo = CooMatrix::<f64>::new(3, 3);
coo.push(2, 0, 1.0);
let csc = CscMatrix::from(&coo);
// Alternatively, a CSC matrix can be constructed directly from raw CSC data.
// Here, we construct a 3x4 matrix
let col_offsets = vec![0, 1, 3, 4, 5];
let row_indices = vec![0, 0, 2, 2, 0];
let values = vec![1.0, 2.0, 3.0, 4.0, 5.0];
// The dense representation of the CSC data, for comparison
let dense = Matrix3x4::new(1.0, 2.0, 0.0, 5.0,
0.0, 0.0, 0.0, 0.0,
0.0, 3.0, 4.0, 0.0);
// The constructor validates the raw CSC data and returns an error if it is invalid.
let csc = CscMatrix::try_from_csc_data(3, 4, col_offsets, row_indices, values)
.expect("CSC data must conform to format specifications");
assert_matrix_eq!(csc, dense);
// A third approach is to construct a CSC matrix from a pattern and values. Sometimes this is
// useful if the sparsity pattern is constructed separately from the values of the matrix.
let (pattern, values) = csc.into_pattern_and_values();
let csc = CscMatrix::try_from_pattern_and_values(pattern, values)
.expect("The pattern and values must be compatible");
// Once we have constructed our matrix, we can use it for arithmetic operations together with
// other CSC matrices and dense matrices/vectors.
let x = csc;
let xTx = x.transpose() * &x;
let z = DMatrix::from_fn(4, 8, |i, j| (i as f64) * (j as f64));
let w = 3.0 * xTx * z;
// Although the sparsity pattern of a CSC matrix cannot be changed, its values can.
// Here are two different ways to scale all values by a constant:
let mut x = x;
x *= 5.0;
x.values_mut().iter_mut().for_each(|x_i| *x_i *= 5.0);
§Format
An m x n
sparse matrix with nnz
non-zeros in CSC format is represented by the
following three arrays:
col_offsets
, an array of integers with lengthn + 1
.row_indices
, an array of integers with lengthnnz
.values
, an array of values with lengthnnz
.
The relationship between the arrays is described below.
- Each consecutive pair of entries
col_offsets[j] .. col_offsets[j + 1]
corresponds to an offset range inrow_indices
that holds the row indices in columnj
. - For an entry represented by the index
idx
,row_indices[idx]
stores its column index andvalues[idx]
stores its value.
The following invariants must be upheld and are enforced by the data structure:
col_offsets[0] == 0
col_offsets[m] == nnz
col_offsets
is monotonically increasing.0 <= row_indices[idx] < m
for allidx < nnz
.- The row indices associated with each column are monotonically increasing (see below).
The CSC format is a standard sparse matrix format (see Wikipedia article). The format
represents the matrix in a column-by-column fashion. The entries associated with column j
are
determined as follows:
let range = col_offsets[j] .. col_offsets[j + 1];
let col_j_rows = &row_indices[range.clone()];
let col_j_vals = &values[range];
// For each pair (i, v) in (col_j_rows, col_j_vals), we obtain a corresponding entry
// (i, j, v) in the matrix.
assert_eq!(col_j_rows.len(), col_j_vals.len());
In the above example, for each column j
, the row indices col_j_cols
must appear in
monotonically increasing order. In other words, they must be sorted. This criterion is not
standard among all sparse matrix libraries, but we enforce this property as it is a crucial
assumption for both correctness and performance for many algorithms.
Note that the CSR and CSC formats are essentially identical, except that CSC stores the matrix column-by-column instead of row-by-row like CSR.
Implementations§
Source§impl<T> CscMatrix<T>
impl<T> CscMatrix<T>
Sourcepub fn identity(n: usize) -> Self
pub fn identity(n: usize) -> Self
Constructs a CSC representation of the (square) n x n
identity matrix.
Sourcepub fn zeros(nrows: usize, ncols: usize) -> Self
pub fn zeros(nrows: usize, ncols: usize) -> Self
Create a zero CSC matrix with no explicitly stored entries.
Sourcepub fn try_from_csc_data(
num_rows: usize,
num_cols: usize,
col_offsets: Vec<usize>,
row_indices: Vec<usize>,
values: Vec<T>,
) -> Result<Self, SparseFormatError>
pub fn try_from_csc_data( num_rows: usize, num_cols: usize, col_offsets: Vec<usize>, row_indices: Vec<usize>, values: Vec<T>, ) -> Result<Self, SparseFormatError>
Try to construct a CSC matrix from raw CSC data.
It is assumed that each column contains unique and sorted row indices that are in bounds with respect to the number of rows in the matrix. If this is not the case, an error is returned to indicate the failure.
An error is returned if the data given does not conform to the CSC storage format.
See the documentation for CscMatrix
for more information.
Sourcepub fn try_from_unsorted_csc_data(
num_rows: usize,
num_cols: usize,
col_offsets: Vec<usize>,
row_indices: Vec<usize>,
values: Vec<T>,
) -> Result<Self, SparseFormatError>where
T: Scalar,
pub fn try_from_unsorted_csc_data(
num_rows: usize,
num_cols: usize,
col_offsets: Vec<usize>,
row_indices: Vec<usize>,
values: Vec<T>,
) -> Result<Self, SparseFormatError>where
T: Scalar,
Try to construct a CSC matrix from raw CSC data with unsorted row indices.
It is assumed that each column contains unique row indices that are in bounds with respect to the number of rows in the matrix. If this is not the case, an error is returned to indicate the failure.
An error is returned if the data given does not conform to the CSC storage format
with the exception of having unsorted row indices and values.
See the documentation for CscMatrix
for more information.
Sourcepub fn try_from_pattern_and_values(
pattern: SparsityPattern,
values: Vec<T>,
) -> Result<Self, SparseFormatError>
pub fn try_from_pattern_and_values( pattern: SparsityPattern, values: Vec<T>, ) -> Result<Self, SparseFormatError>
Try to construct a CSC matrix from a sparsity pattern and associated non-zero values.
Returns an error if the number of values does not match the number of minor indices in the pattern.
Sourcepub fn nnz(&self) -> usize
pub fn nnz(&self) -> usize
The number of non-zeros in the matrix.
Note that this corresponds to the number of explicitly stored entries, not the actual number of algebraically zero entries in the matrix. Explicitly stored entries can still be zero. Corresponds to the number of entries in the sparsity pattern.
Sourcepub fn col_offsets(&self) -> &[usize]
pub fn col_offsets(&self) -> &[usize]
The column offsets defining part of the CSC format.
Sourcepub fn row_indices(&self) -> &[usize]
pub fn row_indices(&self) -> &[usize]
The row indices defining part of the CSC format.
Sourcepub fn values_mut(&mut self) -> &mut [T]
pub fn values_mut(&mut self) -> &mut [T]
Mutable access to the non-zero values.
Sourcepub fn triplet_iter(&self) -> CscTripletIter<'_, T> ⓘ
pub fn triplet_iter(&self) -> CscTripletIter<'_, T> ⓘ
An iterator over non-zero triplets (i, j, v).
The iteration happens in column-major fashion, meaning that j increases monotonically, and i increases monotonically within each row.
§Examples
let col_offsets = vec![0, 2, 3, 4];
let row_indices = vec![0, 2, 1, 0];
let values = vec![1, 3, 2, 4];
let mut csc = CscMatrix::try_from_csc_data(4, 3, col_offsets, row_indices, values)
.unwrap();
let triplets: Vec<_> = csc.triplet_iter().map(|(i, j, v)| (i, j, *v)).collect();
assert_eq!(triplets, vec![(0, 0, 1), (2, 0, 3), (1, 1, 2), (0, 2, 4)]);
Sourcepub fn triplet_iter_mut(&mut self) -> CscTripletIterMut<'_, T> ⓘ
pub fn triplet_iter_mut(&mut self) -> CscTripletIterMut<'_, T> ⓘ
A mutable iterator over non-zero triplets (i, j, v).
Iteration happens in the same order as for triplet_iter.
§Examples
let col_offsets = vec![0, 2, 3, 4];
let row_indices = vec![0, 2, 1, 0];
let values = vec![1, 3, 2, 4];
// Using the same data as in the `triplet_iter` example
let mut csc = CscMatrix::try_from_csc_data(4, 3, col_offsets, row_indices, values)
.unwrap();
// Zero out lower-triangular terms
csc.triplet_iter_mut()
.filter(|(i, j, _)| j < i)
.for_each(|(_, _, v)| *v = 0);
let triplets: Vec<_> = csc.triplet_iter().map(|(i, j, v)| (i, j, *v)).collect();
assert_eq!(triplets, vec![(0, 0, 1), (2, 0, 0), (1, 1, 2), (0, 2, 4)]);
Sourcepub fn get_col(&self, index: usize) -> Option<CscCol<'_, T>>
pub fn get_col(&self, index: usize) -> Option<CscCol<'_, T>>
Return the column at the given column index, or None
if out of bounds.
Sourcepub fn get_col_mut(&mut self, index: usize) -> Option<CscColMut<'_, T>>
pub fn get_col_mut(&mut self, index: usize) -> Option<CscColMut<'_, T>>
Mutable column access for the given column index, or None
if out of bounds.
Sourcepub fn col_iter(&self) -> CscColIter<'_, T> ⓘ
pub fn col_iter(&self) -> CscColIter<'_, T> ⓘ
An iterator over columns in the matrix.
Sourcepub fn col_iter_mut(&mut self) -> CscColIterMut<'_, T> ⓘ
pub fn col_iter_mut(&mut self) -> CscColIterMut<'_, T> ⓘ
A mutable iterator over columns in the matrix.
Sourcepub fn disassemble(self) -> (Vec<usize>, Vec<usize>, Vec<T>)
pub fn disassemble(self) -> (Vec<usize>, Vec<usize>, Vec<T>)
Disassembles the CSC matrix into its underlying offset, index and value arrays.
If the matrix contains the sole reference to the sparsity pattern, then the data is returned as-is. Otherwise, the sparsity pattern is cloned.
§Examples
let col_offsets = vec![0, 2, 3, 4];
let row_indices = vec![0, 2, 1, 0];
let values = vec![1, 3, 2, 4];
let mut csc = CscMatrix::try_from_csc_data(
4,
3,
col_offsets.clone(),
row_indices.clone(),
values.clone())
.unwrap();
let (col_offsets2, row_indices2, values2) = csc.disassemble();
assert_eq!(col_offsets2, col_offsets);
assert_eq!(row_indices2, row_indices);
assert_eq!(values2, values);
Sourcepub fn into_pattern_and_values(self) -> (SparsityPattern, Vec<T>)
pub fn into_pattern_and_values(self) -> (SparsityPattern, Vec<T>)
Returns the sparsity pattern and values associated with this matrix.
Sourcepub fn pattern_and_values_mut(&mut self) -> (&SparsityPattern, &mut [T])
pub fn pattern_and_values_mut(&mut self) -> (&SparsityPattern, &mut [T])
Returns a reference to the sparsity pattern and a mutable reference to the values.
Sourcepub fn pattern(&self) -> &SparsityPattern
pub fn pattern(&self) -> &SparsityPattern
Returns a reference to the underlying sparsity pattern.
Sourcepub fn transpose_as_csr(self) -> CsrMatrix<T>
pub fn transpose_as_csr(self) -> CsrMatrix<T>
Reinterprets the CSC matrix as its transpose represented by a CSR matrix.
This operation does not touch the CSC data, and is effectively a no-op.
Sourcepub fn get_entry(
&self,
row_index: usize,
col_index: usize,
) -> Option<SparseEntry<'_, T>>
pub fn get_entry( &self, row_index: usize, col_index: usize, ) -> Option<SparseEntry<'_, T>>
Returns an entry for the given row/col indices, or None
if the indices are out of bounds.
Each call to this function incurs the cost of a binary search among the explicitly stored row entries for the given column.
Sourcepub fn get_entry_mut(
&mut self,
row_index: usize,
col_index: usize,
) -> Option<SparseEntryMut<'_, T>>
pub fn get_entry_mut( &mut self, row_index: usize, col_index: usize, ) -> Option<SparseEntryMut<'_, T>>
Returns a mutable entry for the given row/col indices, or None
if the indices are out
of bounds.
Each call to this function incurs the cost of a binary search among the explicitly stored row entries for the given column.
Sourcepub fn index_entry(
&self,
row_index: usize,
col_index: usize,
) -> SparseEntry<'_, T>
pub fn index_entry( &self, row_index: usize, col_index: usize, ) -> SparseEntry<'_, T>
Returns an entry for the given row/col indices.
Same as get_entry
, except that it directly panics upon encountering row/col indices
out of bounds.
§Panics
Panics if row_index
or col_index
is out of bounds.
Sourcepub fn index_entry_mut(
&mut self,
row_index: usize,
col_index: usize,
) -> SparseEntryMut<'_, T>
pub fn index_entry_mut( &mut self, row_index: usize, col_index: usize, ) -> SparseEntryMut<'_, T>
Returns a mutable entry for the given row/col indices.
Same as get_entry_mut
, except that it directly panics upon encountering row/col indices
out of bounds.
§Panics
Panics if row_index
or col_index
is out of bounds.
Sourcepub fn csc_data(&self) -> (&[usize], &[usize], &[T])
pub fn csc_data(&self) -> (&[usize], &[usize], &[T])
Returns a triplet of slices (col_offsets, row_indices, values)
that make up the CSC data.
Sourcepub fn csc_data_mut(&mut self) -> (&[usize], &[usize], &mut [T])
pub fn csc_data_mut(&mut self) -> (&[usize], &[usize], &mut [T])
Returns a triplet of slices (col_offsets, row_indices, values)
that make up the CSC data,
where the values
array is mutable.
Sourcepub fn filter<P>(&self, predicate: P) -> Self
pub fn filter<P>(&self, predicate: P) -> Self
Creates a sparse matrix that contains only the explicit entries decided by the given predicate.
Sourcepub fn upper_triangle(&self) -> Selfwhere
T: Clone,
pub fn upper_triangle(&self) -> Selfwhere
T: Clone,
Returns a new matrix representing the upper triangular part of this matrix.
The result includes the diagonal of the matrix.
Sourcepub fn lower_triangle(&self) -> Selfwhere
T: Clone,
pub fn lower_triangle(&self) -> Selfwhere
T: Clone,
Returns a new matrix representing the lower triangular part of this matrix.
The result includes the diagonal of the matrix.
Sourcepub fn diagonal_as_csc(&self) -> Selfwhere
T: Clone,
pub fn diagonal_as_csc(&self) -> Selfwhere
T: Clone,
Returns the diagonal of the matrix as a sparse matrix.
Trait Implementations§
Source§impl<'a, T> Add<&'a CscMatrix<T>> for CscMatrix<T>where
T: Scalar + ClosedAddAssign + ClosedSubAssign + ClosedMulAssign + Zero + One + Neg<Output = T>,
impl<'a, T> Add<&'a CscMatrix<T>> for CscMatrix<T>where
T: Scalar + ClosedAddAssign + ClosedSubAssign + ClosedMulAssign + Zero + One + Neg<Output = T>,
Source§impl<'a, T> Add<CscMatrix<T>> for &'a CscMatrix<T>where
T: Scalar + ClosedAddAssign + ClosedSubAssign + ClosedMulAssign + Zero + One + Neg<Output = T>,
impl<'a, T> Add<CscMatrix<T>> for &'a CscMatrix<T>where
T: Scalar + ClosedAddAssign + ClosedSubAssign + ClosedMulAssign + Zero + One + Neg<Output = T>,
Source§impl<'a, T> Add for &'a CscMatrix<T>where
T: Scalar + ClosedAddAssign + ClosedSubAssign + ClosedMulAssign + Zero + One + Neg<Output = T>,
impl<'a, T> Add for &'a CscMatrix<T>where
T: Scalar + ClosedAddAssign + ClosedSubAssign + ClosedMulAssign + Zero + One + Neg<Output = T>,
Source§impl<T> Add for CscMatrix<T>where
T: Scalar + ClosedAddAssign + ClosedSubAssign + ClosedMulAssign + Zero + One + Neg<Output = T>,
impl<T> Add for CscMatrix<T>where
T: Scalar + ClosedAddAssign + ClosedSubAssign + ClosedMulAssign + Zero + One + Neg<Output = T>,
Source§impl<'a, T> Div<&'a T> for &'a CscMatrix<T>where
T: ClosedDivAssign + Scalar + ClosedAddAssign + ClosedSubAssign + ClosedMulAssign + Zero + One + Neg<Output = T>,
impl<'a, T> Div<&'a T> for &'a CscMatrix<T>where
T: ClosedDivAssign + Scalar + ClosedAddAssign + ClosedSubAssign + ClosedMulAssign + Zero + One + Neg<Output = T>,
Source§impl<'a, T> Div<&T> for CscMatrix<T>where
T: ClosedDivAssign + Scalar + ClosedAddAssign + ClosedSubAssign + ClosedMulAssign + Zero + One + Neg<Output = T>,
impl<'a, T> Div<&T> for CscMatrix<T>where
T: ClosedDivAssign + Scalar + ClosedAddAssign + ClosedSubAssign + ClosedMulAssign + Zero + One + Neg<Output = T>,
Source§impl<'a, T> Div<T> for &'a CscMatrix<T>where
T: ClosedDivAssign + Scalar + ClosedAddAssign + ClosedSubAssign + ClosedMulAssign + Zero + One + Neg<Output = T>,
impl<'a, T> Div<T> for &'a CscMatrix<T>where
T: ClosedDivAssign + Scalar + ClosedAddAssign + ClosedSubAssign + ClosedMulAssign + Zero + One + Neg<Output = T>,
Source§impl<T> Div<T> for CscMatrix<T>where
T: ClosedDivAssign + Scalar + ClosedAddAssign + ClosedSubAssign + ClosedMulAssign + Zero + One + Neg<Output = T>,
impl<T> Div<T> for CscMatrix<T>where
T: ClosedDivAssign + Scalar + ClosedAddAssign + ClosedSubAssign + ClosedMulAssign + Zero + One + Neg<Output = T>,
Source§impl<'a, T> DivAssign<&'a T> for CscMatrix<T>
impl<'a, T> DivAssign<&'a T> for CscMatrix<T>
Source§fn div_assign(&mut self, scalar: &'a T)
fn div_assign(&mut self, scalar: &'a T)
/=
operation. Read moreSource§impl<T> DivAssign<T> for CscMatrix<T>
impl<T> DivAssign<T> for CscMatrix<T>
Source§fn div_assign(&mut self, scalar: T)
fn div_assign(&mut self, scalar: T)
/=
operation. Read moreSource§impl<'a, T> Mul<&'a CscMatrix<T>> for CscMatrix<T>where
T: Scalar + ClosedAddAssign + ClosedSubAssign + ClosedMulAssign + Zero + One + Neg<Output = T>,
impl<'a, T> Mul<&'a CscMatrix<T>> for CscMatrix<T>where
T: Scalar + ClosedAddAssign + ClosedSubAssign + ClosedMulAssign + Zero + One + Neg<Output = T>,
Source§impl<'a, T, R, C, S> Mul<&'a Matrix<T, R, C, S>> for &'a CscMatrix<T>where
T: Scalar + ClosedMulAssign + ClosedAddAssign + ClosedSubAssign + ClosedDivAssign + Neg + Zero + One,
R: Dim,
C: Dim,
S: RawStorage<T, R, C>,
DefaultAllocator: Allocator<Dyn, C>,
ShapeConstraint: DimEq<U1, <<DefaultAllocator as Allocator<Dyn, C>>::Buffer<T> as RawStorage<T, Dyn, C>>::RStride> + DimEq<C, Dyn> + DimEq<Dyn, <<DefaultAllocator as Allocator<Dyn, C>>::Buffer<T> as RawStorage<T, Dyn, C>>::CStride> + DimEq<U1, S::RStride> + DimEq<R, Dyn> + DimEq<Dyn, S::CStride>,
impl<'a, T, R, C, S> Mul<&'a Matrix<T, R, C, S>> for &'a CscMatrix<T>where
T: Scalar + ClosedMulAssign + ClosedAddAssign + ClosedSubAssign + ClosedDivAssign + Neg + Zero + One,
R: Dim,
C: Dim,
S: RawStorage<T, R, C>,
DefaultAllocator: Allocator<Dyn, C>,
ShapeConstraint: DimEq<U1, <<DefaultAllocator as Allocator<Dyn, C>>::Buffer<T> as RawStorage<T, Dyn, C>>::RStride> + DimEq<C, Dyn> + DimEq<Dyn, <<DefaultAllocator as Allocator<Dyn, C>>::Buffer<T> as RawStorage<T, Dyn, C>>::CStride> + DimEq<U1, S::RStride> + DimEq<R, Dyn> + DimEq<Dyn, S::CStride>,
Source§impl<'a, T, R, C, S> Mul<&'a Matrix<T, R, C, S>> for CscMatrix<T>where
T: Scalar + ClosedMulAssign + ClosedAddAssign + ClosedSubAssign + ClosedDivAssign + Neg + Zero + One,
R: Dim,
C: Dim,
S: RawStorage<T, R, C>,
DefaultAllocator: Allocator<Dyn, C>,
ShapeConstraint: DimEq<U1, <<DefaultAllocator as Allocator<Dyn, C>>::Buffer<T> as RawStorage<T, Dyn, C>>::RStride> + DimEq<C, Dyn> + DimEq<Dyn, <<DefaultAllocator as Allocator<Dyn, C>>::Buffer<T> as RawStorage<T, Dyn, C>>::CStride> + DimEq<U1, S::RStride> + DimEq<R, Dyn> + DimEq<Dyn, S::CStride>,
impl<'a, T, R, C, S> Mul<&'a Matrix<T, R, C, S>> for CscMatrix<T>where
T: Scalar + ClosedMulAssign + ClosedAddAssign + ClosedSubAssign + ClosedDivAssign + Neg + Zero + One,
R: Dim,
C: Dim,
S: RawStorage<T, R, C>,
DefaultAllocator: Allocator<Dyn, C>,
ShapeConstraint: DimEq<U1, <<DefaultAllocator as Allocator<Dyn, C>>::Buffer<T> as RawStorage<T, Dyn, C>>::RStride> + DimEq<C, Dyn> + DimEq<Dyn, <<DefaultAllocator as Allocator<Dyn, C>>::Buffer<T> as RawStorage<T, Dyn, C>>::CStride> + DimEq<U1, S::RStride> + DimEq<R, Dyn> + DimEq<Dyn, S::CStride>,
Source§impl<'a, T> Mul<&'a T> for &'a CscMatrix<T>where
T: Scalar + ClosedAddAssign + ClosedSubAssign + ClosedMulAssign + Zero + One + Neg<Output = T>,
impl<'a, T> Mul<&'a T> for &'a CscMatrix<T>where
T: Scalar + ClosedAddAssign + ClosedSubAssign + ClosedMulAssign + Zero + One + Neg<Output = T>,
Source§impl<'a, T> Mul<&'a T> for CscMatrix<T>where
T: Scalar + ClosedAddAssign + ClosedSubAssign + ClosedMulAssign + Zero + One + Neg<Output = T>,
impl<'a, T> Mul<&'a T> for CscMatrix<T>where
T: Scalar + ClosedAddAssign + ClosedSubAssign + ClosedMulAssign + Zero + One + Neg<Output = T>,
Source§impl<'a, T> Mul<CscMatrix<T>> for &'a CscMatrix<T>where
T: Scalar + ClosedAddAssign + ClosedSubAssign + ClosedMulAssign + Zero + One + Neg<Output = T>,
impl<'a, T> Mul<CscMatrix<T>> for &'a CscMatrix<T>where
T: Scalar + ClosedAddAssign + ClosedSubAssign + ClosedMulAssign + Zero + One + Neg<Output = T>,
Source§impl<'a, T, R, C, S> Mul<Matrix<T, R, C, S>> for &'a CscMatrix<T>where
T: Scalar + ClosedMulAssign + ClosedAddAssign + ClosedSubAssign + ClosedDivAssign + Neg + Zero + One,
R: Dim,
C: Dim,
S: RawStorage<T, R, C>,
DefaultAllocator: Allocator<Dyn, C>,
ShapeConstraint: DimEq<U1, <<DefaultAllocator as Allocator<Dyn, C>>::Buffer<T> as RawStorage<T, Dyn, C>>::RStride> + DimEq<C, Dyn> + DimEq<Dyn, <<DefaultAllocator as Allocator<Dyn, C>>::Buffer<T> as RawStorage<T, Dyn, C>>::CStride> + DimEq<U1, S::RStride> + DimEq<R, Dyn> + DimEq<Dyn, S::CStride>,
impl<'a, T, R, C, S> Mul<Matrix<T, R, C, S>> for &'a CscMatrix<T>where
T: Scalar + ClosedMulAssign + ClosedAddAssign + ClosedSubAssign + ClosedDivAssign + Neg + Zero + One,
R: Dim,
C: Dim,
S: RawStorage<T, R, C>,
DefaultAllocator: Allocator<Dyn, C>,
ShapeConstraint: DimEq<U1, <<DefaultAllocator as Allocator<Dyn, C>>::Buffer<T> as RawStorage<T, Dyn, C>>::RStride> + DimEq<C, Dyn> + DimEq<Dyn, <<DefaultAllocator as Allocator<Dyn, C>>::Buffer<T> as RawStorage<T, Dyn, C>>::CStride> + DimEq<U1, S::RStride> + DimEq<R, Dyn> + DimEq<Dyn, S::CStride>,
Source§impl<'a, T, R, C, S> Mul<Matrix<T, R, C, S>> for CscMatrix<T>where
T: Scalar + ClosedMulAssign + ClosedAddAssign + ClosedSubAssign + ClosedDivAssign + Neg + Zero + One,
R: Dim,
C: Dim,
S: RawStorage<T, R, C>,
DefaultAllocator: Allocator<Dyn, C>,
ShapeConstraint: DimEq<U1, <<DefaultAllocator as Allocator<Dyn, C>>::Buffer<T> as RawStorage<T, Dyn, C>>::RStride> + DimEq<C, Dyn> + DimEq<Dyn, <<DefaultAllocator as Allocator<Dyn, C>>::Buffer<T> as RawStorage<T, Dyn, C>>::CStride> + DimEq<U1, S::RStride> + DimEq<R, Dyn> + DimEq<Dyn, S::CStride>,
impl<'a, T, R, C, S> Mul<Matrix<T, R, C, S>> for CscMatrix<T>where
T: Scalar + ClosedMulAssign + ClosedAddAssign + ClosedSubAssign + ClosedDivAssign + Neg + Zero + One,
R: Dim,
C: Dim,
S: RawStorage<T, R, C>,
DefaultAllocator: Allocator<Dyn, C>,
ShapeConstraint: DimEq<U1, <<DefaultAllocator as Allocator<Dyn, C>>::Buffer<T> as RawStorage<T, Dyn, C>>::RStride> + DimEq<C, Dyn> + DimEq<Dyn, <<DefaultAllocator as Allocator<Dyn, C>>::Buffer<T> as RawStorage<T, Dyn, C>>::CStride> + DimEq<U1, S::RStride> + DimEq<R, Dyn> + DimEq<Dyn, S::CStride>,
Source§impl<'a, T> Mul<T> for &'a CscMatrix<T>where
T: Scalar + ClosedAddAssign + ClosedSubAssign + ClosedMulAssign + Zero + One + Neg<Output = T>,
impl<'a, T> Mul<T> for &'a CscMatrix<T>where
T: Scalar + ClosedAddAssign + ClosedSubAssign + ClosedMulAssign + Zero + One + Neg<Output = T>,
Source§impl<T> Mul<T> for CscMatrix<T>where
T: Scalar + ClosedAddAssign + ClosedSubAssign + ClosedMulAssign + Zero + One + Neg<Output = T>,
impl<T> Mul<T> for CscMatrix<T>where
T: Scalar + ClosedAddAssign + ClosedSubAssign + ClosedMulAssign + Zero + One + Neg<Output = T>,
Source§impl<'a, T> Mul for &'a CscMatrix<T>where
T: Scalar + ClosedAddAssign + ClosedSubAssign + ClosedMulAssign + Zero + One + Neg<Output = T>,
impl<'a, T> Mul for &'a CscMatrix<T>where
T: Scalar + ClosedAddAssign + ClosedSubAssign + ClosedMulAssign + Zero + One + Neg<Output = T>,
Source§impl<T> Mul for CscMatrix<T>where
T: Scalar + ClosedAddAssign + ClosedSubAssign + ClosedMulAssign + Zero + One + Neg<Output = T>,
impl<T> Mul for CscMatrix<T>where
T: Scalar + ClosedAddAssign + ClosedSubAssign + ClosedMulAssign + Zero + One + Neg<Output = T>,
Source§impl<'a, T> MulAssign<&'a T> for CscMatrix<T>
impl<'a, T> MulAssign<&'a T> for CscMatrix<T>
Source§fn mul_assign(&mut self, scalar: &'a T)
fn mul_assign(&mut self, scalar: &'a T)
*=
operation. Read moreSource§impl<T> MulAssign<T> for CscMatrix<T>
impl<T> MulAssign<T> for CscMatrix<T>
Source§fn mul_assign(&mut self, scalar: T)
fn mul_assign(&mut self, scalar: T)
*=
operation. Read moreSource§impl<T: Clone> SparseAccess<T> for CscMatrix<T>
impl<T: Clone> SparseAccess<T> for CscMatrix<T>
Source§impl<'a, T> Sub<&'a CscMatrix<T>> for CscMatrix<T>where
T: Scalar + ClosedAddAssign + ClosedSubAssign + ClosedMulAssign + Zero + One + Neg<Output = T>,
impl<'a, T> Sub<&'a CscMatrix<T>> for CscMatrix<T>where
T: Scalar + ClosedAddAssign + ClosedSubAssign + ClosedMulAssign + Zero + One + Neg<Output = T>,
Source§impl<'a, T> Sub<CscMatrix<T>> for &'a CscMatrix<T>where
T: Scalar + ClosedAddAssign + ClosedSubAssign + ClosedMulAssign + Zero + One + Neg<Output = T>,
impl<'a, T> Sub<CscMatrix<T>> for &'a CscMatrix<T>where
T: Scalar + ClosedAddAssign + ClosedSubAssign + ClosedMulAssign + Zero + One + Neg<Output = T>,
Source§impl<'a, T> Sub for &'a CscMatrix<T>where
T: Scalar + ClosedAddAssign + ClosedSubAssign + ClosedMulAssign + Zero + One + Neg<Output = T>,
impl<'a, T> Sub for &'a CscMatrix<T>where
T: Scalar + ClosedAddAssign + ClosedSubAssign + ClosedMulAssign + Zero + One + Neg<Output = T>,
Source§impl<T> Sub for CscMatrix<T>where
T: Scalar + ClosedAddAssign + ClosedSubAssign + ClosedMulAssign + Zero + One + Neg<Output = T>,
impl<T> Sub for CscMatrix<T>where
T: Scalar + ClosedAddAssign + ClosedSubAssign + ClosedMulAssign + Zero + One + Neg<Output = T>,
impl<T: Eq> Eq for CscMatrix<T>
impl<T: MatrixMarketScalar> MatrixMarketExport<T> for CscMatrix<T>
impl<T> StructuralPartialEq for CscMatrix<T>
Auto Trait Implementations§
impl<T> Freeze for CscMatrix<T>
impl<T> RefUnwindSafe for CscMatrix<T>where
T: RefUnwindSafe,
impl<T> Send for CscMatrix<T>where
T: Send,
impl<T> Sync for CscMatrix<T>where
T: Sync,
impl<T> Unpin for CscMatrix<T>where
T: Unpin,
impl<T> UnwindSafe for CscMatrix<T>where
T: UnwindSafe,
Blanket Implementations§
Source§impl<T> BorrowMut<T> for Twhere
T: ?Sized,
impl<T> BorrowMut<T> for Twhere
T: ?Sized,
Source§fn borrow_mut(&mut self) -> &mut T
fn borrow_mut(&mut self) -> &mut T
Source§impl<T> CloneToUninit for Twhere
T: Clone,
impl<T> CloneToUninit for Twhere
T: Clone,
Source§impl<SS, SP> SupersetOf<SS> for SPwhere
SS: SubsetOf<SP>,
impl<SS, SP> SupersetOf<SS> for SPwhere
SS: SubsetOf<SP>,
Source§fn to_subset(&self) -> Option<SS>
fn to_subset(&self) -> Option<SS>
self
from the equivalent element of its
superset. Read moreSource§fn is_in_subset(&self) -> bool
fn is_in_subset(&self) -> bool
self
is actually part of its subset T
(and can be converted to it).Source§fn to_subset_unchecked(&self) -> SS
fn to_subset_unchecked(&self) -> SS
self.to_subset
but without any property checks. Always succeeds.Source§fn from_subset(element: &SS) -> SP
fn from_subset(element: &SS) -> SP
self
to the equivalent element of its superset.