Scilab Function

numdiff - numerical gradient estimation

### Calling Sequence

  `g=numdiff(fun,x [,dx])`

### Parameters

- `fun` : an external, Scilab function or list. See below for calling sequence, see also external for details about external functions.
- `x` : vector, the argument of the function `fun`
- `dx` : vector, the finite difference step. Default value is `dx=sqrt(%eps)*(1+1d-3*abs(x))`
- `g` : vector, the estimated gradient

### Description

given a function `fun(x)` from R^n to R^p computes the matrix `g` such as

        [   d f    ]
        [      i   ]
    g = [   ----   ]
     ij [   d x    ]
        [      j   ]

using finite difference methods.

The function fun calling sequence must be `y=fun(x,p1,p2,..pn)`.If parameters `p1,p2,..pn` exist then `numdiff` can be called as follow g=numdiff(list(fun,p1,p2,..pn),x).

### Examples

    function  f=myfun(x,y,z,t)
    f=(x+y)^t+z
    endfunction

    y=3;z=4;t=2;
    g=numdiff(list(myfun,3,4,2),1)

    x=1;
    exact=t*(x+y)^(t-1)

### See Also

`optim`