Function eigsh_generalized 

pub fn eigsh_generalized<T>(
    a_matrix: &SymCsrMatrix<T>,
    b_matrix: &SymCsrMatrix<T>,
    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 + SparseElement + PartialOrd + Send + Sync + 'static,

Solve the generalized symmetric eigenvalue problem Ax = λBx

This function computes eigenvalues and eigenvectors for the generalized eigenvalue problem where both A and B are symmetric sparse matrices.

Arguments

• a_matrix - The A matrix (left-hand side)
• b_matrix - The B matrix (right-hand side, usually mass matrix)
• k - Number of eigenvalues to compute (optional, defaults to 6)
• which - Which eigenvalues to compute (“LA”, “SA”, “LM”, “SM”)
• options - Additional options for the solver

Returns

Eigenvalue computation result where λ values satisfy Ax = λBx

Examples

use scirs2_sparse::linalg::eigsh_generalized;
use scirs2_sparse::sym_csr::SymCsrMatrix;

// Create matrices A and B
// Symmetric 2x2 A stored as lower: (0,0)=4, (1,0)=2, (1,1)=3
let a_data = vec![4.0, 2.0, 3.0];
let a_indptr = vec![0, 1, 3];
let a_indices = vec![0, 0, 1];
let amatrix = SymCsrMatrix::new(a_data, a_indptr, a_indices, (2, 2)).unwrap();

// Symmetric 2x2 B stored as lower: (0,0)=2, (1,0)=1, (1,1)=2
let b_data = vec![2.0, 1.0, 2.0];
let b_indptr = vec![0, 1, 3];
let b_indices = vec![0, 0, 1];
let bmatrix = SymCsrMatrix::new(b_data, b_indptr, b_indices, (2, 2)).unwrap();

// Solve Ax = λBx
let result = eigsh_generalized(&amatrix, &bmatrix, Some(2), None, None).unwrap();