A simple library to compute Singular Value Decomposition as explained in "Singular Value Decomposition and Least Squares Solutions. By G.H. Golub et al."
SVD(a, withu, withv, eps, tol) => { u, v, q }
computes the singular values and complete orthogonal decomposition of a real rectangular matrix
A: A = U * diag(q) * V(t), U(t) * U = V(t) * V = I
The actual parameters corresponding to A, U, V may all be identical unless
withu = withv = {true}. In this case, the actual parameters corresponding to U and V must
differ. m >= n is assumed (with m = a.length and n = a[0].length).
The following is the description of all parameters:
a {Array}: Represents the matrix A to be decomposedwithu (Optional default is true) {bool | 'f'}: true if U is desired false otherwise. It can also be 'f' (see below)withv (Optional default is true) {bool}: true if V is desired false otherwiseeps (Optional) {Number}: A constant used in the test for convergence; should not be smaller
than the machine precisiontol (Optional) {Number}: A machine dependent constant which should be set equal
to B/eps where B is the smallest positive number representable in the computerThe function returns an object with the following values:
q: A vector holding the singular values of A; they are non-negative but not necessarily
ordered in decreasing sequenceu: Represents the matrix U with orthonormalized columns (if withu is true
otherwise u is used as a working storage)v: Represents the orthogonal matrix V (if withv is true, otherwise v is not used)If 'f' is given to withu, it computes 'full' U with m*m dimension.
It is an extension in (i) of '5. Organization and Notation Details' in Golub et al."
The extension part of U (u[n] to u[m-1]) are orthonormal bases of A that correspond to null singular values, or the nullspace of A^T.
Golub and Reinsch first example
import { SVD } from 'svd-js'
const a = [
[22, 10, 2, 3, 7],
[14, 7, 10, 0, 8],
[-1, 13, -1, -11, 3],
[-3, -2, 13, -2, 4],
[9, 8, 1, -2, 4],
[9, 1, -7, 5, -1],
[2, -6, 6, 5, 1],
[4, 5, 0, -2, 2]
]
const { u, v, q } = SVD(a)
console.log(u)
console.log(v)
console.log(q)
Golub and Reinsch first example
<html>
<script src="https://unpkg.com/svd-js" type="application/javascript"></script>
<script>
const a = [
[22, 10, 2, 3, 7],
[14, 7, 10, 0, 8],
[-1, 13, -1, -11, 3],
[-3, -2, 13, -2, 4],
[9, 8, 1, -2, 4],
[9, 1, -7, 5, -1],
[2, -6, 6, 5, 1],
[4, 5, 0, -2, 2]
]
const { u, v, q } = SVDJS.SVD(a)
console.log(u)
console.log(v)
console.log(q)
</script>
</html>
FAQs
A package for the computation of Singular Value Decomposition
The npm package svd-js receives a total of 7,442 weekly downloads. As such, svd-js popularity was classified as popular.
We found that svd-js demonstrated a not healthy version release cadence and project activity because the last version was released a year ago. It has 1 open source maintainer collaborating on the project.
Did you know?
Socket for GitHub automatically highlights issues in each pull request and monitors the health of all your open source dependencies. Discover the contents of your packages and block harmful activity before you install or update your dependencies.
Security News
The official Go SDK for the Model Context Protocol is in development, with a stable, production-ready release expected by August 2025.
Security News
New research reveals that LLMs often fake understanding, passing benchmarks but failing to apply concepts or stay internally consistent.
Security News
Django has updated its security policies to reject AI-generated vulnerability reports that include fabricated or unverifiable content.