clustering-tfjs/dist/utils/eigen_qr.d.ts

High-performance TypeScript clustering algorithms (K-Means, Spectral, Agglomerative) with TensorFlow.js acceleration and scikit-learn compatibility

import * as tf from '../tf-adapter';
/**
 * QR Algorithm-based eigendecomposition for symmetric matrices.
 *
 * The QR algorithm is more numerically stable than Jacobi iteration
 * and converges faster for most matrices. This implementation uses
 * TensorFlow.js's built-in QR decomposition.
 *
 * Algorithm:
 * 1. Start with A₀ = A
 * 2. For each iteration:
 *    - Compute QR decomposition: Aᵢ = QᵢRᵢ
 *    - Form Aᵢ₊₁ = RᵢQᵢ
 * 3. As i → ∞, Aᵢ converges to a diagonal matrix of eigenvalues
 * 4. The product Q₀Q₁...Qᵢ gives the eigenvectors
 */
export declare function qr_eigen_decomposition(matrix: tf.Tensor2D, { maxIterations, tolerance, }?: {
    maxIterations?: number;
    tolerance?: number;
}): {
    eigenvalues: number[];
    eigenvectors: number[][];
};
/**
 * Specialized QR algorithm for tridiagonal matrices.
 * Since normalized Laplacians are often nearly tridiagonal after
 * similarity transformations, this can be more efficient.
 */
export declare function tridiagonal_qr_eigen(diagonal: number[], offDiagonal: number[], computeVectors?: boolean): {
    eigenvalues: number[];
    eigenvectors?: number[][];
};
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