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bundles / scipy latest / scipy / optimize / _optimize / fminbound

function

scipy.optimize._optimize:fminbound

source: /scipy/optimize/_optimize.py :2195

Signature

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

Summary

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

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

If non-zero, print messages.

0no message printing.

1non-convergence notification messages only.

2print a message on convergence too.

3print iteration results.

Returns

xopt : ndarray

Parameters (over given interval) which minimize the objective function.

fval : number

(Optional output) The function value evaluated at the minimizer.

ierr : int

(Optional output) An error flag (0 if converged, 1 if maximum number of function calls reached).

numfunc : int

(Optional output) 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.)

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
✗
res = optimize.fminbound(f, 3, 4, full_output=True)
minimizer, fval, ierr, numfunc = res
✓
minimizer
✗
minimum = f(minimizer)
✓
minimum, fval
✗

See also

minimize_scalar

Interface to minimization algorithms for scalar univariate functions. See the 'Bounded' method in particular.

Aliases

  • scipy.optimize.fminbound

Referenced by

This package