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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:
| num_min (⟨exp⟩, ⟨var1⟩[=val1] [,var2[=val2] …] [,accuracy=a][,iterations=i]) |
or
|
| num_min (⟨exp⟩, {⟨var1⟩[=val1] [,var2[=val2] …]} [,accuracy=a][,iterations=i]) |
where ⟨exp⟩ is a function expression, ⟨var1⟩, ⟨var2⟩, … are the variables in ⟨exp⟩ and ⟨val1⟩, ⟨val2⟩, … are the (optional) start values.
num_min tries 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:
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