svd()
Singular value decomposition using QR iteration and inverse iteration algorithm.
The singular value decomposition (SVD) is a factorization of a real or complex matrix. It generalizes the eigendecomposition of a square normal matrix with an orthonormal eigenbasis to any m * n matrix.
Import
import * as datacook from '@pipcook/datacook';
const { svd } = datacook.Linalg;
Syntax
async svd(matrix: Tensor, tol: number, maxIter: number): Promise<Tensor>
Parameters
| Parameter | type | description |
|---|---|---|
| matrix | Tensor | target matrix |
| tol | number | tolerence, default to 1e-4 |
| maxIter | number | maximum iteration times, default to 200 |
Returns
[ u, d, v ], u: left singular vector, d: singluar values, v: right singular vector