Compute Numerical Hessian Matrix

Calculate the Hessian matrix (matrix of second-order partial derivatives) of a multi-variable function. The Hessian provides information about local curvature and is used in second-order optimization methods (Newton’s method), analyzing critical points, and measuring convexity. Essential for advanced optimization and risk analysis. [Tier: BASIC, Credits: 1]

POST /quantlib/numerical/differentiation/hessian

Compute Numerical Hessian Matrix

cURL

curl --request POST \
  --url https://api.fincept.in/quantlib/numerical/differentiation/hessian \
  --header 'Content-Type: application/json' \
  --header 'X-API-Key: <api-key>' \
  --data '
{
  "func_name": "sphere",
  "x": [
    2,
    3
  ],
  "h": 0.00001
}
'

200

{
  "success": true,
  "data": {
    "function": "sphere",
    "x": [
      2,
      3
    ],
    "hessian": [
      [
        2,
        0
      ],
      [
        0,
        2
      ]
    ]
  }
}

Authorizations

X-API-Key · string · header · required

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

Body

application/json

func_name · enum<string> · required

Built-in multi-variable function name

Available options: rosenbrock, sphere, rastrigin, quadratic

Example:

"sphere"

x · number[] · required

Point at which to evaluate the Hessian

Example:

[2, 3]

h · number · default: 0.00001

Step size for finite difference

Example:

0.00001

Response

200

application/json

Successful Response

success · boolean

Example:

true

data · object

Show child attributes