Optimization Suite¶
This contains methods to optimize functions using SciPy and evaluate the methods’ performance in terms of memory, algorithmic iterations, and runtime.

optimization_suite.
GA_minimize
(function, guess)¶  This function runs the genetic algorithm from PyBrain (http://pybrain.org/docs/api/optimization/optimization.html) on a function, provided with an initial guess.NOTE: Format is crucial here or this will not work (blame the authors of PyBrain, sorry):Function must take a tuple (Even if of only one element (in the form (a, )) and guess MUST BE A LIST (even if only with one element)

optimization_suite.
custom_minimize
(function, algorithm, bounds=None, guess=None)¶ This is similar to the minimize_suite, but is defined for functions like basinhopping or brute_force (in scipy.optimize), so the second parameter is a userprovided algorithm for optimization.
 Arguments:
 algorithm  has been tested with things like scipy.optimize.basinhopping, and bruteforce, but should work with a userdefined algorithm as well.
 Keyword Arguments:
 bounds  Limits the search domainguess  an initial guess at an optimal solutionWhich keyword arguments are required or optional depends on the provided algorithm.

optimization_suite.
minimize_suite
(function, methods, guess, SS_stress=None)¶  This method takes any method provided by http://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.minimize.html.This method takes a function, strings representing the method to test it with, and an initial guess for the optimal solution.Every minimizer in this package is a local optimization algorithm, so it will get trapped in convexities of a dataset.In running, this function will display the amount of memory, the runtime, and the number of algorithmic iterations required toachieve an optimal result.