/**
 * Find the eigenvector corresponding to the largest-magnitude eigenvalue of a
 * symmetric square matrix using power iteration.
 *
 * Designed for small symmetric matrices where the dominant eigenvalue is
 * well-separated from the rest (Horn's quaternion method on a 4x4, PCA on a
 * 3x3 covariance, etc.). Unsuitable when the top two eigenvalues have nearly
 * equal magnitude — convergence stalls in that regime.
 *
 * @param {SquareMatrix} matrix Symmetric, column-major as per SquareMatrix
 * @param {number[]|Float32Array|Float64Array} out Eigenvector is written here, length >= matrix.size
 * @param {number} [max_iterations=64]
 * @param {number} [tolerance=1e-8] Convergence stops when ||v_next - v|| < tolerance
 * @returns {number} The Rayleigh quotient (estimated eigenvalue)
 */
export function matrix_top_eigenvector_power_iteration(matrix: SquareMatrix, out: number[] | Float32Array | Float64Array, max_iterations?: number, tolerance?: number): number;
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