bracket(func, xa=0.0, xb=1.0, args=(), grow_limit=110.0, maxiter=1000)
Given a function and distinct initial points, search in the downhill direction (as defined by the initial points) and return new points xa, xb, xc that bracket the minimum of the function f(xa) > f(xb) < f(xc). It doesn't always mean that obtained solution will satisfy xa<=x<=xb.
Objective function to minimize.
Bracketing interval. Defaults :None:None:`xa` to 0.0, and :None:None:`xb` to 1.0.
Additional arguments (if present), passed to :None:None:`func`.
Maximum grow limit. Defaults to 110.0
Maximum number of iterations to perform. Defaults to 1000.
Bracket.
Objective function values in bracket.
Number of function evaluations made.
Bracket the minimum of the function.
This function can find a downward convex region of a function:
>>> import matplotlib.pyplot as plt
... from scipy.optimize import bracket
... def f(x):
... return 10*x**2 + 3*x + 5
... x = np.linspace(-2, 2)
... y = f(x)
... init_xa, init_xb = 0, 1
... xa, xb, xc, fa, fb, fc, funcalls = bracket(f, xa=init_xa, xb=init_xb)
... plt.axvline(x=init_xa, color="k", linestyle="--")
... plt.axvline(x=init_xb, color="k", linestyle="--")
... plt.plot(x, y, "-k")
... plt.plot(xa, fa, "bx")
... plt.plot(xb, fb, "rx")
... plt.plot(xc, fc, "bx")
... plt.show()
The following pages refer to to this document either explicitly or contain code examples using this.
scipy.optimize._optimize.bracket
scipy.optimize._minimize.minimize_scalar
scipy.optimize._optimize.golden
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