NUM_MIN _ _ _ _ _ _ _ _ _ _ _ _ operator

The Fletcher Reeves version of the steepest descent algorithms is used to find the minimum of a function of one or more variables. The function must have continuous partial derivatives with respect to all variables. The starting point of the search can be specified; if not, random values are taken instead. The steepest descent algorithms in general find only local minima.

syntax:

or

num_min(exp, { <var>[=<val>] [,<var>[=<val>] ...] } [,accuracy=<a>] [,iterations=<i>])

where <exp> is a function expression, <var> are the variables in <exp> and <val> are the (optional) start values. For <a> and <i> see numeric accuracy.

Num_mintries to find the next local minimum along the descending path starting at the given point. The result is a list with the minimum function value as first element followed by a list of equations, where the variables are equated to the coordinates of the result point.

examples:

num_min(sin(x)+x/5, x)

  {4.9489585606,{X=29.643767785}}


num_min(sin(x)+x/5, x=0)

  { - 1.3342267466,{X= - 1.7721582671}}