Publisher review:Automatic Numerical Differentiation - Numerical derivative of an analytically supplied function, also gradient, Jacobian & Hessian The DERIVESTsuite now provides automatic numerical differentiation for both scalar and vector valued functions. Tools for derivatives (up to 4th order) of a scalar function are provided, as well as the gradient vector, directional derivative, Jacobian matrix, and Hessian matrix. Error estimates are provided for all tools.DERIVEST provides automatic numerical differentiation (up to the fourth derivative) of a user supplied function, much as quad does for integration. It is semi-intelligent, trying to use that step size which minimizes its estimate of the uncertainty in the derivative.High order methods are used, although full control is provided to the user when you want it. You can direct the order of the method to be used, the general class of difference method employed (forward, backward, or central differences), the number of terms employed in its generalized Romberg acceleration, step sizes, etc. Although you can not provide a user supplied tolerance, DERIVEST does return an estimate of its uncertainty in the final result. For example, the derivative of exp(x), at x=1 is exp(1)==2.71828182845905. DERIVEST comes pretty close.[d,err]=derivest(@(x) exp(x),1)d =2.71828182845904err =1.02015503167879e-14See the provided demos for many more examples. Requirements: ยท MATLAB Release: R14SP1
Automatic Numerical Differentiation is a Matlab script for Mathematics scripts design by John D`Errico.
It runs on following operating system: Windows / Linux / Mac OS / BSD / Solaris.
Automatic Numerical Differentiation - Numerical derivative of an analytically supplied function, also gradient, Jacobian & Hessian
Operating system:Windows / Linux / Mac OS / BSD / Solaris
Automatic Numerical Differentiation script details 
