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optpy

This library provides some functions to make optimization in python easier. It can use scipy.optimize. Also, it provides an interface that makes minimizing functions of multiple variables easier, especially if only a subset of the variables should be considered for the optimization.

This package used to contain a convenience function minimize_ipopt that mimicked the scipy.mimize.optimize interface. This function is now part of https://github.com/matthias-k/cyipopt. If you want to use optpy with ipopt just set the method argument to the minimize function from cyipopt.

Optimization of multiple variables

In practise, often one has to optimize functions that depend on multiple variables. Most optimizers, including scipy.optimize.minimze and ipopt can handle only functions that depend on one array-like parameter that should be optimized. It gets even more complicated when one wants to include certain variables in a flexible way into the optimimzation or keep them constant. optpy provides an easy way to do so:

Here we have a function of three variables x1, x2, x3, but we want to optimize only x1 and x2.

def f(x1, x2, x3):
    return np.sum(x1**2)+np.sum(x2**2)

def constraint(x1, x2, x3):
    return x1-1.0

constraints = [{'type': 'eq', 'fun': constraint}]

res = optimization.minimize(f, {'x1': 1.0,
                                'x2': np.array([2.0, 2.0]),
                                'x3': 1.0},
                            optimize=['x1', 'x2'],
                            method='SLSQP', constraints = constraints)
np.testing.assert_allclose(res.x1, 1.0)
np.testing.assert_allclose(res.x2, [0.0, 0.0])
np.testing.assert_allclose(res.x3, 1.0)

There is also a more explicit interface:

parameter_manager = optpy.ParameterManager(['x1', 'x2', 'x3'], ['x1', 'x2'],
                                               x1=1.0, x2=np.array([2.0, 2.0]), x3=1.0)

def f(x1, x2, x3):
    return np.sum(x1**2)+np.sum(x2**2)

def constraint(x1, x2, x3):
    return x1-1.0

constraints = [{'type': 'eq', 'fun': constraint}]

res = optpy.minimize(f, parameter_manager, method='SLSQP', constraints = constraints)
np.testing.assert_allclose(res.x1, 1.0)
np.testing.assert_allclose(res.x2, [0.0, 0.0])
np.testing.assert_allclose(res.x3, 1.0)