/**
 * Symmetric eigendecomposition via cyclic Jacobi rotations.
 *
 * @param {number[][]|Float64Array[]|{flat: ArrayLike<number>, n: number}} input
 *   Real symmetric matrix as a 2D array of rows, or `{ flat, n }` (row-major).
 * @param {object} [opts]
 * @param {number} [opts.tol=1e-15] Off-diagonal convergence threshold (relative
 *   to the matrix scale) at which rotations stop.
 * @param {number} [opts.maxSweeps=100] Hard cap on Jacobi sweeps; exceeding it
 *   throws rather than returning an unconverged result.
 * @returns {{ values: Float64Array, vectors: number[][] }}
 *   `values` — eigenvalues in ascending order.
 *   `vectors` — n×n array of rows where column k is the unit eigenvector for
 *   `values[k]` (scipy's column convention).
 */
export function eigh(input: number[][] | Float64Array[] | {
    flat: ArrayLike<number>;
    n: number;
}, { tol, maxSweeps }?: {
    tol?: number;
    maxSweeps?: number;
}): {
    values: Float64Array;
    vectors: number[][];
};