fides/minimize.py
def track_minimum(self,
                      x_new: np.ndarray,
                      fval_new: float,
                      grad_new: np.ndarray) -> None:
        """
        Function that tracks the optimization variables that have minimal
        function value independent of whether the step is accepted or not.

        :param x_new:
        :param fval_new:
        :param grad_new:
        :return:
        """
        if np.isfinite(fval_new) and fval_new < self.fval_min:
            self.x_min = x_new
            self.fval_min = fval_new
            self.grad_min = grad_new

        Function that tracks the optimization variables that have minimal
        function value independent of whether the step is accepted or not.

        :param x_new:
        :param fval_new:
        :param grad_new:
        :return:
        
Function that tracks the optimization variables that have minimal
function value independent of whether the step is accepted or not.
Function that tracks the optimization variables that have minimal function value independent of whether the step is accepted or not .
def track_minimum(self,
                      x_new: np.ndarray,
                      fval_new: float,
                      grad_new: np.ndarray) -> None:
        if np.isfinite(fval_new) and fval_new < self.fval_min:
            self.x_min = x_new
            self.fval_min = fval_new
            self.grad_min = grad_new
def track_minimum ( self , x_new : np . ndarray , fval_new : float , grad_new : np . ndarray ) -> None : if np . isfinite ( fval_new ) and fval_new < self . fval_min : self . x_min = x_new self . fval_min = fval_new self . grad_min = grad_new
Function that tracks the optimization variables that have minimal
function value independent of whether the step is accepted or not.
Function that tracks the optimization variables that have minimal function value independent of whether the step is accepted or not .
"""
        Function that tracks the optimization variables that have minimal
        function value independent of whether the step is accepted or not.

        :param x_new:
        :param fval_new:
        :param grad_new:
        :return:
        """
None
self
x_new
np.ndarray
None
fval_new
float
None
grad_new
np.ndarray
None