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'Solves automatic numerical differentiation problems in one or more variables.'

Project description

Suite of tools written in _Python to solve automatic numerical differentiation problems in one or more variables. Finite differences are used in an adaptive manner, coupled with a Richardson extrapolation methodology to provide a maximally accurate result. The user can configure many options like; changing the order of the method or the extrapolation, even allowing the user to specify whether complex-step, central, forward or backward differences are used.

The methods provided are:

  • Derivative: Compute the derivatives of order 1 through 10 on any scalar function.

  • Gradient: Compute the gradient vector of a scalar function of one or more variables.

  • Jacobian: Compute the Jacobian matrix of a vector valued function of one or more variables.

  • Hessian: Compute the Hessian matrix of all 2nd partial derivatives of a scalar function of one or more variables.

  • Hessdiag: Compute only the diagonal elements of the Hessian matrix

All of these methods also produce error estimates on the result.

The documentation for numdifftools is available here

Code and issue tracker is at

Download the toolbox here:



August 28

New release of Numdifftools 0.9.12.

August 27

New release of Numdifftools 0.9.11.

August 26

New release of Numdifftools 0.9.10.

August 20

New release of Numdifftools 0.9.2.


December 18

New release of Numdifftools 0.7.7.

December 17

New release of Numdifftools 0.7.3.

February 8

New release of Numdifftools 0.6.0. :

January 10

New release of Numdifftools 0.5.0.


May 5

New release of Numdifftools 0.4.0.


May 19

New release of Numdifftools 0.3.5.

Feb 24

New release of Numdifftools 0.3.4.


May 20

New beta release of Numdifftools 0.3.1.

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