L1Norm

class odl.solvers.functional.default_functionals.L1Norm(*args, **kwargs)[source]

Bases: odl.solvers.functional.default_functionals.LpNorm

The functional corresponding to L1-norm.

The L1-norm, ||x||_1, is defined as the integral/sum of |x|.

Notes

If the functional is defined on an \mathbb{R}^n-like space, the \| \cdot \|_1-norm is defined as

\| x \|_1 = \sum_{i=1}^n |x_i|.

If the functional is defined on an L_2-like space, the \| \cdot \|_1-norm is defined as

\| x \|_1 = \int_\Omega |x(t)| dt.

The proximal factory allows using vector-valued stepsizes:

>>> space = odl.rn(3)
>>> f = odl.solvers.L1Norm(space)
>>> x = space.one()
>>> f.proximal([0.5, 1.0, 1.5])(x)
rn(3).element([ 0.5,  0. ,  0. ])
Attributes
adjoint

Adjoint of this operator (abstract).

convex_conj

The convex conjugate functional of the Lp-norm.

domain

Set of objects on which this operator can be evaluated.

grad_lipschitz

Lipschitz constant for the gradient of the functional.

gradient

Gradient operator of the functional.

inverse

Return the operator inverse.

is_functional

True if this operator’s range is a Field.

is_linear

True if this operator is linear.

proximal

Return the proximal factory of the functional.

range

Set in which the result of an evaluation of this operator lies.

Methods

_call(self, x)

Return the Lp-norm of x.

bregman(self, point, subgrad)

Return the Bregman distance functional.

derivative(self, point)

Return the derivative operator in the given point.

norm(self[, estimate])

Return the operator norm of this operator.

translated(self, shift)

Return a translation of the functional.

__init__(self, space)[source]

Initialize a new instance.

Parameters
spaceDiscretizedSpace or TensorSpace

Domain of the functional.