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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 Romberg 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 central, forward or backward differences are used.

The methods provided are:

Derivative: Computate derivatives of order 1 through 4 on any scalar function.

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

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

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

Hessdiag: Computes only the diagonal elements of the Hessian matrix

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

A pdf file is also provided to explain the theory behind these tools. Download the toolbox here



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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