## functions in roots.i - m

mnbrent
```
fmin= mnbrent(f, x0, x1, x2)
or fmin= mnbrent(f, x0, x1, x2, xmin)
or fmin= mnbrent(f, x0, x1, x2, xmin, xerr)

returns the minimum of the function F (of a single argument x),
given three points X0, X1, and X2 such that F(X1) is less than
either F(X0) or F(X2), and X1 is between X0 and X2.  If the
XMIN argument is provided, it is set to the x value which
produced FMIN.  If XERR is supplied, the search stops when
a fractional error of XERR in x is reached; note that XERR
smaller than the square root of the machine precision (or
omitted) will cause convergence to machine precision in FMIN.
The algorithm is Brent's method - a combination of inverse
parabolic interpolation and golden section search - as adapted
from Numerical Recipes Ch. 10 (Press, et. al.).
Interpreted function, defined at i/roots.i   line 235

```

mxbrent
```
fmax= mxbrent(f, x0, x1, x2)
or fmax= mxbrent(f, x0, x1, x2, xmax)
or fmax= mxbrent(f, x0, x1, x2, xmax, xerr)

returns the maximum of the function F (of a single argument x),
given three points X0, X1, and X2 such that F(X1) is greater than
either F(X0) or F(X2), and X1 is between X0 and X2.  If the
XMAX argument is provided, it is set to the x value which
produced FMAX.  If XERR is supplied, the search stops when
a fractional error of XERR in x is reached; note that XERR
smaller than the square root of the machine precision (or
omitted) will cause convergence to machine precision in FMAX.
The algorithm is Brent's method - a combination of inverse
parabolic interpolation and golden section search - as adapted
from Numerical Recipes Ch. 10 (Press, et. al.).
Interpreted function, defined at i/roots.i   line 210

```