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:

• f (callable) – The function to bisect

• a (float) – The left side of the interval

• b (float) – The right side of the interval

• xtol (float) – The tolerance value

• maxiter (float) – The maximum number of iterations to perform

Find a solution (zero) of the function f(x) on the interval (a..``b``) using the bisection method. The result is accurate to within xtol unless more than maxiter 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:

• f (callable) – The function to bisect

• x0 (float) – The initial x value

• xatol (float) – The absolute tolerance value

• fatol (float) – The relative tolerance value

Find a minimum of the function f(x) using the downhill simplex method. The located x is within fxtol of the actual minimum, and f(x) is within fatol of the actual minimum unless more than maxiter 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:

• f (callable) – The function to bisect

• x0 (float) – The initial x value

• xtol (float) – The absolute tolerance value

• rtol (float) – The relative tolerance value

• maxiter (float) – The maximum number of iterations to perform

Find a solution (zero) of the function f(x) using Newton’s Method. The result is accurate to within xtol * rtol * |f(x)| unless more than maxiter steps are requried.