pyproximal.ProxOperator#

class pyproximal.ProxOperator(Op=None, hasgrad=False)[source]#

Common interface for proximal operators of a function.

This class defines the overarching structure of any proximal operator. It contains two main methods, prox and dualprox which are both implemented by means of the Moreau decomposition assuming explicit knowledge of the other method. For this reason any proximal operators that subclasses the ProxOperator class needs at least one of these two methods to be implemented directly.

Note

End users of PyProx should not use this class directly but simply use operators that are already implemented. This class is meant for developers and it has to be used as the parent class of any new operator developed within PyProx. Find more details regarding implementation of new operators at Implementing new operators.

Parameters
Oppylops.LinearOperator, optional

Linear operator used by the Proximal operator

hasgradbool, optional

Flag to indicate if the function is differentiable, i.e., has a uniquely defined gradient (True) or not (False).

Notes

The proximal operator of a function f is defined as:

\[prox_{\tau f} (\mathbf{x}) = \argmin_{\mathbf{y}} f(\mathbf{y}) + \frac{1}{2 \tau}||\mathbf{y} - \mathbf{x}||^2_2\]

Methods

__init__([Op, hasgrad])

affine_addition(v)

Affine addition

chain(g)

Chain

grad(x)

Compute gradient

postcomposition(sigma)

Postcomposition

precomposition(a, b)

Precomposition

prox(**kwargs)

proxdual(**kwargs)

Examples using pyproximal.ProxOperator#

Concave penalties

Concave penalties

Norms

Norms

Norms

Norms

Quadratic

Quadratic

Adaptive Primal-Dual

Adaptive Primal-Dual

Basis Pursuit

Basis Pursuit

Denoising

Denoising

Group sparsity

Group sparsity

Hankel matrix estimation

Hankel matrix estimation

IHT, ISTA, FISTA, and TWIST for Compressive sensing

IHT, ISTA, FISTA, and TWIST for Compressive sensing

Low-Rank completion via Matrix factorization

Low-Rank completion via Matrix factorization

Low-Rank completion via SVD

Low-Rank completion via SVD

MRI Imaging and Segmentation of Brain

MRI Imaging and Segmentation of Brain

Non-rigid structure-from-motion (NRSfM)

Non-rigid structure-from-motion (NRSfM)

Nonlinear inversion with box constraints

Nonlinear inversion with box constraints

Plug and Play Priors

Plug and Play Priors

Quadratic program with box constraints

Quadratic program with box constraints

Relaxed Mumford-Shah regularization

Relaxed Mumford-Shah regularization