Struct FaerSparseMat 

pub struct FaerSparseMat<T: FaerScalar> { /* private fields */ }

Trait Implementations

impl<T: Scalar + FaerScalar> Add<&FaerSparseMat<T>> for FaerSparseMat<T>

type Output = FaerSparseMat<T>

The resulting type after applying the + operator.

fn add(self, rhs: &FaerSparseMat<T>) -> Self::Output

Performs the + operation.

impl<T: Clone + FaerScalar> Clone for FaerSparseMat<T>

fn clone(&self) -> FaerSparseMat<T>

Returns a duplicate of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl<T: Debug + FaerScalar> Debug for FaerSparseMat<T>

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl<T: FaerScalar> DefaultSolver for FaerSparseMat<T>

type LS = FaerSparseLU<T>

fn default_solver() -> Self::LS

impl<T: FaerScalar> LinearSolver<FaerSparseMat<T>> for FaerSparseLU<T>

fn set_linearisation<C: LinearOp<T = T, V = FaerVec<T>, M = FaerSparseMat<T>, C = FaerContext>>( &mut self, op: &C, )

Set the point at which the linear operator A is evaluated and factorise it.

fn solve_in_place(&self, x: &mut FaerVec<T>) -> Result<(), LaError>

fn set_sparsity<C: LinearOp<T = T, V = FaerVec<T>, M = FaerSparseMat<T>, C = FaerContext>>( &mut self, op: &C, )

Set the sparsity of the problem to be solved, any previous problem is discarded.

fn solve(&self, b: &M::V) -> Result<M::V, LaError>

Solve the problem Ax = b and return the solution x.

impl<T: FaerScalar> Matrix for FaerSparseMat<T>

type Sparsity = SymbolicSparseColMat<Own<usize>>

type SparsityRef<'a> = SymbolicSparseColMat<Ref<'a, usize>>

fn sparsity(&self) -> Option<Self::SparsityRef<'_>>

Return sparsity information, or None if the matrix is dense.

fn context(&self) -> &FaerContext

Get the context associated with this matrix (for device placement, memory management, etc.).

fn inner_mut(&mut self) -> &mut Self::Inner

Get a mutable reference to the inner representation of the matrix.

fn gather(&mut self, other: &Self, indices: &<Self::V as Vector>::Index)

Gather values from another matrix at specified indices into this matrix.

fn set_data_with_indices( &mut self, dst_indices: &<Self::V as Vector>::Index, src_indices: &<Self::V as Vector>::Index, data: &Self::V, )

Assign the values in the data vector to this matrix at the indices in dst_indices from the indices in src_indices.

fn add_column_to_vector(&self, j: IndexType, v: &mut Self::V)

Add a column of this matrix to a vector: v += self[:, j]

fn triplet_iter( &self, ) -> (impl Iterator<Item = (IndexType, IndexType)> + '_, impl Iterator<Item = Self::T> + '_)

Iterate over structural positions and values of the matrix.

fn try_from_triplets( nrows: IndexType, ncols: IndexType, indices: Vec<(IndexType, IndexType)>, values: Vec<Self::T>, ctx: Self::C, ) -> Result<Self, LaError>

Create a new matrix from structural indices and values.

fn gemv(&self, alpha: Self::T, x: &Self::V, beta: Self::T, y: &mut Self::V)

Perform a matrix-vector multiplication: y = alpha * self * x + beta * y

fn zeros(nrows: IndexType, ncols: IndexType, ctx: Self::C) -> Self

Create a new matrix of shape nrows x ncols filled with zeros.

fn copy_from(&mut self, other: &Self)

Copy the contents of other into this matrix.

fn from_diagonal(v: &FaerVec<T>) -> Self

Create a new diagonal matrix from a vector holding the diagonal elements.

fn partition_indices_by_zero_diagonal( &self, ) -> (<Self::V as Vector>::Index, <Self::V as Vector>::Index)

Partition the diagonal indices into two groups: those with zero diagonal elements and those with non-zero diagonal elements.

fn set_column(&mut self, j: IndexType, v: &Self::V)

Set the values of column j to be equal to the values in v.

fn scale_add_and_assign(&mut self, x: &Self, beta: Self::T, y: &Self)

Perform the assignment: self = x + beta * y where x and y are matrices and beta is a scalar.

fn new_from_sparsity( nrows: IndexType, ncols: IndexType, sparsity: Option<Self::Sparsity>, ctx: Self::C, ) -> Self

Create a new matrix from a sparsity pattern. Non-zero elements are not initialized.

fn is_sparse() -> bool

Returns true if this matrix is stored in a sparse format

fn split( &self, algebraic_indices: &<Self::V as Vector>::Index, ) -> [(Self, <Self::V as Vector>::Index); 4]

Split this matrix into four submatrices based on algebraic constraint indices.

fn combine( ul: &Self, ur: &Self, ll: &Self, lr: &Self, algebraic_indices: &<Self::V as Vector>::Index, ) -> Self

Combine four submatrices back into a single matrix based on algebraic constraint indices.

impl<T: Scalar + FaerScalar> MatrixCommon for FaerSparseMat<T>

type T = T

type V = FaerVec<T>

type C = FaerContext

type Inner = SparseColMat<Own<usize, T>>

fn nrows(&self) -> IndexType

Get the number of rows in this matrix.

fn ncols(&self) -> IndexType

Get the number of columns in this matrix.

fn inner(&self) -> &Self::Inner

Get a reference to the inner representation of the matrix.

impl<T: FaerScalar> MatrixSparsity<FaerSparseMat<T>> for SymbolicSparseColMat<IndexType>

fn union( self, other: SymbolicSparseColMatRef<'_, IndexType>, ) -> Result<SymbolicSparseColMat<IndexType>, LaError>

fn as_ref(&self) -> SymbolicSparseColMatRef<'_, IndexType>

fn nrows(&self) -> IndexType

fn ncols(&self) -> IndexType

fn is_sparse() -> bool

fn indices(&self) -> Vec<(IndexType, IndexType)>

fn new_diagonal(n: IndexType) -> Self

fn try_from_indices( nrows: IndexType, ncols: IndexType, indices: Vec<(IndexType, IndexType)>, ) -> Result<Self, LaError>

fn get_index( &self, indices: &[(IndexType, IndexType)], ctx: FaerContext, ) -> <<FaerSparseMat<T> as MatrixCommon>::V as Vector>::Index

impl<'a, T: FaerScalar> MatrixSparsityRef<'a, FaerSparseMat<T>> for SymbolicSparseColMatRef<'a, IndexType>

fn to_owned(&self) -> SymbolicSparseColMat<IndexType>

fn nrows(&self) -> IndexType

fn ncols(&self) -> IndexType

fn is_sparse() -> bool

fn split( &self, indices: &<<FaerSparseMat<T> as MatrixCommon>::V as Vector>::Index, ) -> [(SymbolicSparseColMat<IndexType>, <<FaerSparseMat<T> as MatrixCommon>::V as Vector>::Index); 4]

fn indices(&self) -> Vec<(IndexType, IndexType)>

impl<'a, T: FaerScalar> Mul<Scale<T>> for FaerSparseMat<T>

type Output = FaerSparseMat<T>

The resulting type after applying the * operator.

fn mul(self, rhs: Scale<T>) -> Self::Output

Performs the * operation.

impl<'a, T: FaerScalar> Mul<Scale<T>> for &FaerSparseMat<T>

type Output = FaerSparseMat<T>

The resulting type after applying the * operator.

fn mul(self, rhs: Scale<T>) -> Self::Output

Performs the * operation.

impl<T: Scalar + FaerScalar> Sub<&FaerSparseMat<T>> for FaerSparseMat<T>

type Output = FaerSparseMat<T>

The resulting type after applying the - operator.

fn sub(self, rhs: &FaerSparseMat<T>) -> Self::Output

Performs the - operation.