jacobianest.m

Estimates the Jacobian matrix of a vector-valued function of n variables by simple finite differences.

Syntax

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

Parameters

 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.

Outputs

 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

lbfgs.m, bracketing.m, sectioning.m, acomm.m, arnoldi.m, atranspose.m, aux_mat.m, binpack.m, cheap_norm.m, cheb_coeff.m, clean_up.m, dirdiff.m, eigenfields.m, expdrop.m, expmint.m, expmint2.m, fftdiff.m, fourdif.m, fourlap.m, frob_chop.m, gaussfun.m, hdot.m, herm_spline.m, keep_rank.m, krondelta.m, kronm_new.m, logfactorial.m, lorentzcon.m, lorentzfun.m, md5_hash.m, mprealloc.m, remncomm.m, remtrace.m, rspert.m, rspt_eig.m, snormpdf.m, svd_shrink.m, tikhoind.m, tikhonov.m, trapdiff.m, unit_oper.m, unit_state.m, vvpert.m, Kernel_utilities

Version 2.2, authors: John D'Errico