'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 http://numdifftools.readthedocs.org/
Code and issue tracker is at https://github.com/pbrod/numdifftools.
Download the toolbox here: http://pypi.python.org/pypi/Numdifftools
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