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 user-provided algorithm for optimization.

Arguments:

algorithm - has been tested with things like scipy.optimize.basinhopping, and bruteforce, but should work with a user-defined algorithm as well.

Keyword Arguments:

bounds - Limits the search domain

guess - an initial guess at an optimal solution

Which 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 to

achieve an optimal result.