Struct SparseMatrix 

pub struct SparseMatrix { /* private fields */ }

Main sparse matrix implementation supporting multiple storage formats.

Implementations

impl SparseMatrix

pub fn from_triplets( triplets: Vec<(usize, usize, Precision)>, rows: DimensionType, cols: DimensionType, ) -> Result<Self>

Create a new sparse matrix from coordinate (triplet) format.

Arguments

• triplets - Vector of (row, col, value) triplets
• rows - Number of rows
• cols - Number of columns

Example

use sublinear_solver::SparseMatrix;
 
let matrix = SparseMatrix::from_triplets(
    vec![(0, 0, 4.0), (0, 1, 1.0), (1, 0, 2.0), (1, 1, 5.0)],
    2, 2
).unwrap();

pub fn from_dense( data: &[Precision], rows: DimensionType, cols: DimensionType, ) -> Result<Self>

Create a sparse matrix from dense row-major data.

Zero elements are automatically filtered out.

pub fn identity(size: DimensionType) -> Result<Self>

Create an identity matrix of the given size.

pub fn diagonal(diag: &[Precision]) -> Result<Self>

Create a diagonal matrix from the given diagonal values.

pub fn convert_to_format(&mut self, new_format: SparseFormat) -> Result<()>

Convert the matrix to a different storage format.

This operation may be expensive for large matrices.

pub fn to_triplets(&self) -> Result<Vec<(usize, usize, Precision)>>

Extract the matrix as coordinate triplets.

pub fn format(&self) -> SparseFormat

Get the current storage format.

pub fn as_csr(&mut self) -> Result<&CSRStorage>

Get a reference to the underlying CSR storage.

Converts to CSR format if necessary.

pub fn as_csc(&mut self) -> Result<&CSCStorage>

Get a reference to the underlying CSC storage.

Converts to CSC format if necessary.

pub fn as_graph(&mut self) -> Result<&GraphStorage>

Get a reference to the underlying graph storage.

Converts to graph format if necessary.

pub fn scale(&mut self, factor: Precision)

Scale the matrix by a scalar value.

pub fn add_diagonal(&mut self, alpha: Precision) -> Result<()>

Add a scalar multiple of the identity matrix: A = A + alpha * I

Trait Implementations

impl Clone for SparseMatrix

fn clone(&self) -> SparseMatrix

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for SparseMatrix

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

Formats the value using the given formatter.

impl<'de> Deserialize<'de> for SparseMatrix

fn deserialize<__D>(__deserializer: __D) -> Result<Self, __D::Error>

where __D: Deserializer<'de>,

Deserialize this value from the given Serde deserializer.

impl Display for SparseMatrix

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

Formats the value using the given formatter.

impl Matrix for SparseMatrix

fn rows(&self) -> DimensionType

Get the number of rows in the matrix.

fn cols(&self) -> DimensionType

Get the number of columns in the matrix.

fn get(&self, row: usize, col: usize) -> Option<Precision>

Get a specific matrix element, returning None if it’s zero or out of bounds.

fn row_iter( &self, row: usize, ) -> Box<dyn Iterator<Item = (IndexType, Precision)> + '_>

Get an iterator over non-zero elements in a specific row. Returns (column_index, value) pairs.

fn col_iter( &self, col: usize, ) -> Box<dyn Iterator<Item = (IndexType, Precision)> + '_>

Get an iterator over non-zero elements in a specific column. Returns (row_index, value) pairs.

fn multiply_vector( &self, x: &[Precision], result: &mut [Precision], ) -> Result<()>

Perform matrix-vector multiplication: result = A * x

fn multiply_vector_add( &self, x: &[Precision], result: &mut [Precision], ) -> Result<()>

Perform matrix-vector multiplication with accumulation: result += A * x

fn is_diagonally_dominant(&self) -> bool

Check if the matrix is diagonally dominant. A matrix is diagonally dominant if |a_ii| >= Σ_{j≠i} |a_ij| for all i.

fn diagonal_dominance_factor(&self) -> Option<Precision>

Get the diagonal dominance factor (minimum ratio of diagonal to off-diagonal).

fn nnz(&self) -> usize

Get the number of non-zero elements.

fn sparsity_info(&self) -> SparsityInfo

Get sparsity pattern information.

fn conditioning_info(&self) -> ConditioningInfo

Get matrix conditioning information.

fn format_name(&self) -> &'static str

Get the storage format name.

fn is_square(&self) -> bool

Check if the matrix is square.

fn frobenius_norm(&self) -> Precision

Get the Frobenius norm of the matrix.

fn spectral_radius_estimate(&self) -> Precision

Estimate the spectral radius (largest eigenvalue magnitude). Uses Gershgorin circle theorem for a conservative estimate.

impl Serialize for SparseMatrix

fn serialize<__S>(&self, __serializer: __S) -> Result<__S::Ok, __S::Error>

where __S: Serializer,

Serialize this value into the given Serde serializer.