ulab.scipy.optimize
- ulab.scipy.optimize.bisect(fun: Callable[[float], float], a: float, b: float, *, xtol: float = 2.4e-07, maxiter: int = 100) float
- Parameters:
Find a solution (zero) of the function
f(x)
on the interval (a
..``b``) using the bisection method. The result is accurate to withinxtol
unless more thanmaxiter
steps are required.
- ulab.scipy.optimize.fmin(fun: Callable[[float], float], x0: float, *, xatol: float = 2.4e-07, fatol: float = 2.4e-07, maxiter: int = 200) float
- Parameters:
Find a minimum of the function
f(x)
using the downhill simplex method. The locatedx
is withinfxtol
of the actual minimum, andf(x)
is withinfatol
of the actual minimum unless more thanmaxiter
steps are requried.
- ulab.scipy.optimize.newton(fun: Callable[[float], float], x0: float, *, xtol: float = 2.4e-07, rtol: float = 0.0, maxiter: int = 50) float
- Parameters:
Find a solution (zero) of the function
f(x)
using Newton’s Method. The result is accurate to withinxtol * rtol * |f(x)|
unless more thanmaxiter
steps are requried.