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scipy.optimize.fminbound¶
- scipy.optimize.fminbound(func, x1, x2, args=(), xtol=1.0000000000000001e-05, maxfun=500, full_output=0, disp=1)¶
Bounded minimization for scalar functions.
Parameters: - func : callable f(x,*args)
Objective function to be minimized (must accept and return scalars).
- x1, x2 : float or array scalar
The optimization bounds.
- args : tuple
Extra arguments passed to function.
- xtol : float
The convergence tolerance.
- maxfun : int
Maximum number of function evaluations allowed.
- full_output : bool
If True, return optional outputs.
- disp : int
- If non-zero, print messages.
0 : no message printing. 1 : non-convergence notification messages only. 2 : print a message on convergence too. 3 : print iteration results.
Returns: (xopt, {fval, ierr, numfunc})
- xopt : ndarray
Parameters (over given interval) which minimize the objective function.
- fval : number
The function value at the minimum point.
- ierr : int
An error flag (0 if converged, 1 if maximum number of function calls reached).
- numfunc : int
The number of function calls made.
Notes: Finds a local minimizer of the scalar function func in the interval x1 < xopt < x2 using Brent’s method. (See brent for auto-bracketing).
