fminbound(func, x1, x2, args=(), xtol=1e-05, maxfun=500, full_output=0, disp=1)
Finds a local minimizer of the scalar function :None:None:`func` in the interval x1 < xopt < x2 using Brent's method. (See brent
for auto-bracketing.)
Objective function to be minimized (must accept and return scalars).
The optimization bounds.
Extra arguments passed to function.
The convergence tolerance.
Maximum number of function evaluations allowed.
If True, return optional outputs.
Parameters (over given interval) which minimize the objective function.
The function value at the minimum point.
An error flag (0 if converged, 1 if maximum number of function calls reached).
The number of function calls made.
Bounded minimization for scalar functions.
minimize_scalar
Interface to minimization algorithms for scalar univariate functions. See the 'Bounded' :None:None:`method` in particular.
fminbound
finds the minimum of the function in the given range. The following examples illustrate the same
>>> def f(x):
... return x**2
>>> from scipy import optimize
>>> minimum = optimize.fminbound(f, -1, 2)
... minimum 0.0
>>> minimum = optimize.fminbound(f, 1, 2)See :
... minimum 1.0000059608609866
The following pages refer to to this document either explicitly or contain code examples using this.
scipy.optimize._optimize.fminbound
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