1. Getting started

To define an operator, one needs to define a direct function which will replace the usual matrix-vector operation:

>>> def f(x, out):
...     out[...] = 2 * x

Then, you can instantiate an Operator:

>>> A = Operator(f, flags='symmetric')

This operator does not have an explicit shape, it can handle inputs of any shape:

>>> A(np.ones(5))
array([ 2.,  2.,  2.,  2.,  2.])
>>> A(np.ones((2,3)))
array([[ 2.,  2.,  2.],
       [ 2.,  2.,  2.]])

By setting the ‘symmetric’ flag, we ensure that A’s transpose is A:

>>> A.T is A
True

To output a corresponding dense matrix, one needs to specify the input shape:

>>> A.todense(shapein=2)
array([[ 2.,  0.],
       [ 0.,  2.]])

Operators do not have to be linear, but if they are not, they cannot be seen as matrices. Some operators are already predefined, such as the linear operators IdentityOperator and DiagonalOperator or the nonlinear operator ClippingOperator.

The previous A matrix could be defined more easily like this :

>>> A = 2 * I

where I is the identity operator with no explicit shape.

Operators can be combined together by addition, multiplication or composition (note that the * sign stands for composition):

>>> B = 2 * I + DiagonalOperator(np.arange(3))
>>> B.todense()
array([[ 2.,  0.,  0.],
       [ 0.,  3.,  0.],
       [ 0.,  0.,  4.]])

Algebraic rules are used to simplify an expression involving operators, so to speed up its execution:

>>> B
DiagonalOperator(array([ 2.,  3.,  4.]))