## 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 235SEE ALSO: mxbrent, nraphson, f_inverse

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 210SEE ALSO: mxbrent, nraphson, f_inverse