:mod:`ulab.scipy.optimize`
==========================

.. py:module:: ulab.scipy.optimize





.. py:function:: bisect(fun: Callable[[float], float], a: float, b: float, *, xtol: float = 2.4e-07, maxiter: int = 100) -> float

   :param callable f: The function to bisect
   :param float a: The left side of the interval
   :param float b: The right side of the interval
   :param float xtol: The tolerance value
   :param float maxiter: 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.


.. py:function:: fmin(fun: Callable[[float], float], x0: float, *, xatol: float = 2.4e-07, fatol: float = 2.4e-07, maxiter: int = 200) -> float

   :param callable f: The function to bisect
   :param float x0: The initial x value
   :param float xatol: The absolute tolerance value
   :param float fatol: 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.


.. py:function:: newton(fun: Callable[[float], float], x0: float, *, xtol: float = 2.4e-07, rtol: float = 0.0, maxiter: int = 50) -> float

   :param callable f: The function to bisect
   :param float x0: The initial x value
   :param float xtol: The absolute tolerance value
   :param float rtol: The relative tolerance value
   :param float maxiter: 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.