as_scipy_functional

odl.operator.oputils.as_scipy_functional(func, return_gradient=False)

Wrap op as a function operating on linear arrays.

This is intended to be used with the scipy solvers.

Parameters

funcFunctional.

A functional that should be wrapped

return_gradientbool, optional

True if the gradient of the functional should also be returned, False otherwise.

Returns

functioncallable

The wrapped functional.

gradientcallable, optional

The wrapped gradient. Only returned if return_gradient is true.

Notes

If the data representation of op’s domain is of type NumpyTensorSpace, this incurs no significant overhead. If the space type is CudaFn or some other nonlocal type, the overhead is significant.

Examples

Wrap functional and solve simple problem (here toy problem min_x ||x||^2):

>>> func = odl.solvers.L2NormSquared(odl.rn(3))
>>> scipy_func = odl.as_scipy_functional(func)
>>> from scipy.optimize import minimize
>>> result = minimize(scipy_func, x0=[0, 1, 0])
>>> np.allclose(result.x, [0, 0, 0])
True

The gradient (jacobian) can also be provided:

>>> func = odl.solvers.L2NormSquared(odl.rn(3))
>>> scipy_func, scipy_grad = odl.as_scipy_functional(func, True)
>>> from scipy.optimize import minimize
>>> result = minimize(scipy_func, x0=[0, 1, 0], jac=scipy_grad)
>>> np.allclose(result.x, [0, 0, 0])
True