Matrix Decomposition

Decompose a matrix using various methods: LU decomposition (A = LU, for solving linear systems), Cholesky decomposition (A = LL^T, for positive definite matrices in risk models), QR decomposition (A = QR, for least squares and eigenvalue problems), or Singular Value Decomposition (A = UΣV^T, for dimensionality reduction and pseudoinverses). Essential for numerical stability, solving systems, and matrix analysis. [Tier: BASIC, Credits: 1]

POST /quantlib/numerical/linalg/decompose

Matrix Decomposition

cURL

curl --request POST \
  --url https://api.fincept.in/quantlib/numerical/linalg/decompose \
  --header 'Content-Type: application/json' \
  --header 'X-API-Key: <api-key>' \
  --data '
{
  "A": [
    [
      4,
      12,
      -16
    ],
    [
      12,
      37,
      -43
    ],
    [
      -16,
      -43,
      98
    ]
  ],
  "method": "cholesky"
}
'

200

{
  "success": true,
  "data": {
    "L": [
      [
        2,
        0,
        0
      ],
      [
        6,
        1,
        0
      ],
      [
        -8,
        5,
        3
      ]
    ]
  }
}

Authorizations

X-API-Key · string · header · required

API key for authentication. Get your key at https://api.fincept.in/auth/register

Body

application/json

A · number[][] · required

Input matrix (requirements depend on method: LU/QR/SVD work on any, Cholesky requires positive definite symmetric)

Example:

[[4, 12, -16], [12, 37, -43], [-16, -43, 98]]

method · enum<string> · default: lu

Decomposition method

Available options: lu, cholesky, qr, svd

Example:

"cholesky"

Response

200

application/json

Successful Response

success · boolean

Example:

true

data · object

Decomposition result (structure depends on method)

Option 1

Option 2

Option 3

Show child attributes