Adaptive Robust Numerical Differentiation: All Files - File Exchange

Code covered by the BSD License

Highlights from
Adaptive Robust Numerical Differentiation

derivest_demo
multivariable_calc_demo
derivest(fun,x0,varargin) DERIVEST: estimate the n'th derivative of fun at x0, provide an error estimate
directionaldiff(fun,x0,vec) directionaldiff: estimate of the directional derivative of a function of n variables
gradest(fun,x0) gradest: estimate of the gradient vector of an analytical function of n variables
hessdiag(fun,x0) HESSDIAG: diagonal elements of the Hessian matrix (vector of second partials)
hessian(fun,x0) hessian: estimate elements of the Hessian matrix (array of 2nd partials)
jacobianest(fun,x0) gradest: estimate of the Jacobian matrix of a vector valued function of n variables
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from Adaptive Robust Numerical Differentiation by John D'Errico
Numerical derivative of an analytically supplied function, also gradient, Jacobian & Hessian

All files for Adaptive Robust Numerical Differentiation

/DERIVESTsuite/ReadMe.rtf
/DERIVESTsuite/demo/derivest_demo.m
/DERIVESTsuite/demo/html/derivest_demo.html
/DERIVESTsuite/demo/html/derivest_demo.png
/DERIVESTsuite/demo/html/derivest_demo_01.png
/DERIVESTsuite/demo/html/multivariable_calc_demo.html
/DERIVESTsuite/demo/multivariable_calc_demo.m
/DERIVESTsuite/derivest.m
/DERIVESTsuite/directionaldiff.m
/DERIVESTsuite/doc/DERIVEST.pdf
/DERIVESTsuite/doc/DERIVEST.tex
/DERIVESTsuite/gradest.m
/DERIVESTsuite/hessdiag.m
/DERIVESTsuite/hessian.m
/DERIVESTsuite/jacobianest.m
/license.txt

Highlights from Adaptive Robust Numerical Differentiation

Highlights from
Adaptive Robust Numerical Differentiation