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Estimate of the Jacobian matrix of a vector valued function of n variables.


    [jac,err] = jacobianest(fun,x0)


Simple finite difference estimation.


 fun - (vector valued) analytical function to differentiate.
       fun must be a function of the vector or array x0.
 x0  - vector location at which to differentiate fun
       If x0 is an nxm array, then fun is assumed to be
       a function of n*m variables.


 jac - array of first partial derivatives of fun.
       Assuming that x0 is a vector of length p
       and fun returns a vector of length n, then
       jac will be an array of size (n,p)

 err - vector of error estimates corresponding to
       each partial derivative in jac.

See also

quasinewton.m, lbfgs.m, linesearch.m

Version 2.1, authors: John D'Errico