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fminbound(func, x1, x2, args=(), xtol=1e-05, maxfun=500, full_output=0, disp=1)

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.)

Parameters

func : callable f(x,*args): Objective function to be minimized (must accept and return scalars).
x1, x2 : float or array scalar: Finite optimization bounds.
args : tuple, optional: Extra arguments passed to function.
xtol : float, optional: The convergence tolerance.
maxfun : int, optional: Maximum number of function evaluations allowed.
full_output : bool, optional: If True, return optional outputs.
disp : int, optional

Returns

xopt : ndarray: Parameters (over given interval) which minimize the objective function.
fval : number: The function value evaluated at the minimizer.
ierr : int: An error flag (0 if converged, 1 if maximum number of function calls reached).
numfunc : int: The number of function calls made.

Bounded minimization for scalar functions.

Examples

`fminbound` finds the minimizer of the function in the given range. The following examples illustrate this.

from scipy import optimize
def f(x):
    return (x-1)**2
minimizer = optimize.fminbound(f, -4, 4)
minimizer

minimum = f(minimizer)
minimum

minimizer = optimize.fminbound(f, 3, 4)
minimizer

minimum = f(minimizer)
minimum

See :

Back References

The following pages refer to to this document either explicitly or contain code examples using this.

scipy.optimize._optimize:brent

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