pylops.optimization.cls_sparsity.SPGL1

class pylops.optimization.cls_sparsity.SPGL1(Op, callbacks=None)

Spectral Projected-Gradient for L1 norm.

Solve a constrained system of equations given the operator Op and a sparsyfing transform SOp aiming to retrive a model that is sparse in the sparsifying domain.

This is a simple wrapper to spgl1.spgl1 which is a porting of the well-known SPGL1 MATLAB solver into Python. In order to be able to use this solver you need to have installed the spgl1 library.

Parameters:

Oppylops.LinearOperator

Operator to invert of size \([N \times M]\).

Attributes:

ncpmodule

Array module used by the solver (obtained via pylops.utils.backend.get_array_module) ). Available only after setup is called.

Raises:

ModuleNotFoundError

If the spgl1 library is not installed

Notes

Solve different variations of sparsity-promoting inverse problem by imposing sparsity in the retrieved model [1].

The first problem is called basis pursuit denoise (BPDN) and its cost function is

\[\|\mathbf{x}\|_1 \quad \text{subject to} \quad \left\|\mathbf{Op}\,\mathbf{S}^H\mathbf{x}-\mathbf{y}\right\|_2^2 \leq \sigma,\]

while the second problem is the ℓ₁-regularized least-squares or LASSO problem and its cost function is

\[\left\|\mathbf{Op}\,\mathbf{S}^H\mathbf{x}-\mathbf{y}\right\|_2^2 \quad \text{subject to} \quad \|\mathbf{x}\|_1 \leq \tau\]

[1]

van den Berg E., Friedlander M.P., “Probing the Pareto frontier for basis pursuit solutions”, SIAM J. on Scientific Computing, vol. 31(2), pp. 890-912. 2008.

Methods

__init__(Op[, callbacks])
callback(x, *args, **kwargs)	Callback routine
finalize(*args[, show])	Finalize solver
memory_usage([show, unit])	Compute memory usage of the solver
run(x[, show])	Run solver
setup(y[, SOp, tau, sigma, show])	Setup solver
solve(y[, x0, SOp, tau, sigma, show])	Run entire solver
step()	Run one step of solver