Function eigs 

pub fn eigs<T, S>(
    matrix: &S,
    k: Option<usize>,
    which: Option<&str>,
    options: Option<LanczosOptions>,
) -> SparseResult<EigenResult<T>>
where
    T: Float + Debug + Copy + Add<Output = T> + Sub<Output = T> + Mul<Output = T> + Div<Output = T> + Sum + SimdUnifiedOps + Send + Sync + 'static + ScalarOperand,
    S: SparseArray<T>,

Find eigenvalues and eigenvectors of a general (non-symmetric) sparse matrix

This function computes eigenvalues of general sparse matrices using iterative methods. For symmetric matrices, consider using the specialized symmetric solvers which are more efficient.

Arguments

• matrix - The sparse matrix (any SparseArray implementation)
• k - Number of eigenvalues to compute (optional, defaults to 6)
• which - Which eigenvalues to compute:
  • “LM”: Largest magnitude
  • “SM”: Smallest magnitude
  • “LR”: Largest real part
  • “SR”: Smallest real part
  • “LI”: Largest imaginary part
  • “SI”: Smallest imaginary part
• options - Additional options for the solver

Returns

Eigenvalue computation result with the requested eigenvalues and eigenvectors

Examples

use scirs2_sparse::linalg::eigs;
use scirs2_sparse::csr_array::CsrArray;
use scirs2_core::ndarray::Array1;

// Create a general sparse matrix
let data = Array1::from(vec![1.0, 2.0, 3.0, 4.0]);
let indices = Array1::from(vec![0, 1, 0, 1]);
let indptr = Array1::from(vec![0, 2, 4]);
let matrix = CsrArray::new(data, indices, indptr, (2, 2)).unwrap();

// Find the 2 largest eigenvalues in magnitude
let result = eigs(&matrix, Some(2), Some("LM"), None).unwrap();