optimization Package

optimization Package

pypr.optimization.minimize(X, f, df, length, verbose=False, return_result=False, callback=None)

This is Carl Edward Rasmussen’s minimize method rewritten in Python.

Inputs:

X: Input for the function f.

f: The function to be minimized. f is a method that takes X as input and provides af function evaluation.

df: The function gradient, evaluated at X.

length: The maximum number of line searches.

callback: An optional user-supplied function to call after each iteration.

Called as callback(Xk), where Xk is the current parameter vector.

Returns: By default the function value for each iteration, but if

return_result is set to True, then it returns a tuple containing (function value, final function inputs).

minimize Module

pypr.optimization.minimize.minimize(X, f, df, length, verbose=False, return_result=False, callback=None)

This is Carl Edward Rasmussen’s minimize method rewritten in Python.

Inputs:

X: Input for the function f.

f: The function to be minimized. f is a method that takes X as input and provides af function evaluation.

df: The function gradient, evaluated at X.

length: The maximum number of line searches.

callback: An optional user-supplied function to call after each iteration.

Called as callback(Xk), where Xk is the current parameter vector.

Returns: By default the function value for each iteration, but if

return_result is set to True, then it returns a tuple containing (function value, final function inputs).

pypr.optimization.minimize.rosenbrock(x)

Returns the rosenbrock function value for x. Inputs x1, x2, .., xn are given column wise. Input vetors as rows.

pypr.optimization.minimize.rosenbrock_d(x)

Returns the values of the partial derivative of the rosenbrock function. Inputs x1, x2, .., xn are given column wise. Input vetors as rows.