pylops.optimization.sparsity.ISTA(Op, data, niter, eps=0.1, alpha=None, eigsiter=None, eigstol=0, tol=1e-10, monitorres=False, returninfo=False, show=False, threshkind='soft', perc=None, callback=None, decay=None, SOp=None, x0=None)[source]

Iterative Shrinkage-Thresholding Algorithm (ISTA).

Solve an optimization problem with \(L^p, \; p=0, 0.5, 1\) regularization, given the operator Op and data y. The operator can be real or complex, and should ideally be either square \(N=M\) or underdetermined \(N<M\).

Op : pylops.LinearOperator

Operator to invert

data : numpy.ndarray


niter : int

Number of iterations

eps : float, optional

Sparsity damping

alpha : float, optional

Step size. To guarantee convergence, ensure \(\alpha \le 1/\lambda_\text{max}\), where \(\lambda_\text{max}\) is the largest eigenvalue of \(\mathbf{Op}^H\mathbf{Op}\). If None, the maximum eigenvalue is estimated and the optimal step size is chosen as \(1/\lambda_\text{max}\). If provided, the convergence criterion will not be checked internally.

eigsiter : float, optional

Number of iterations for eigenvalue estimation if alpha=None

eigstol : float, optional

Tolerance for eigenvalue estimation if alpha=None

tol : float, optional

Tolerance. Stop iterations if difference between inverted model at subsequent iterations is smaller than tol

monitorres : bool, optional

Monitor that residual is decreasing

returninfo : bool, optional

Return info of CG solver

show : bool, optional

Display iterations log

threshkind : str, optional

Kind of thresholding (‘hard’, ‘soft’, ‘half’, ‘hard-percentile’, ‘soft-percentile’, or ‘half-percentile’ - ‘soft’ used as default)

perc : float, optional

Percentile, as percentage of values to be kept by thresholding (to be provided when thresholding is soft-percentile or half-percentile)

callback : callable, optional

Function with signature (callback(x)) to call after each iteration where x is the current model vector

decay : numpy.ndarray, optional

Decay factor to be applied to thresholding during iterations

SOp : pylops.LinearOperator, optional

Regularization operator (use when solving the analysis problem)

x0: :obj:`numpy.ndarray`, optional

Initial guess

xinv : numpy.ndarray

Inverted model

niter : int

Number of effective iterations

cost : numpy.ndarray, optional

History of cost function


If threshkind is different from hard, soft, half, soft-percentile, or half-percentile


If perc=None when threshkind is soft-percentile or half-percentile


If monitorres=True and residual increases

See also

Orthogonal Matching Pursuit (OMP).
Fast Iterative Shrinkage-Thresholding Algorithm (FISTA).
Spectral Projected-Gradient for L1 norm (SPGL1).
Split Bregman for mixed L2-L1 norms.


Solves the following synthesis problem for the operator \(\mathbf{Op}\) and the data \(\mathbf{d}\):

\[J = \|\mathbf{d} - \mathbf{Op}\,\mathbf{x}\|_2^2 + \epsilon \|\mathbf{x}\|_p\]

or the analysis problem:

\[J = \|\mathbf{d} - \mathbf{Op}\,\mathbf{x}\|_2^2 + \epsilon \|\mathbf{SOp}^H\,\mathbf{x}\|_p\]

if SOp is provided. Note that in the first case, SOp should be assimilated in the modelling operator (i.e., Op=GOp * SOp).

The Iterative Shrinkage-Thresholding Algorithms (ISTA) [1] is used, where \(p=0, 0.5, 1\). This is a very simple iterative algorithm which applies the following step:

\[\mathbf{x}^{(i+1)} = T_{(\epsilon \alpha /2, p)} \left(\mathbf{x}^{(i)} + \alpha\,\mathbf{Op}^H \left(\mathbf{d} - \mathbf{Op}\,\mathbf{x}^{(i)}\right)\right)\]


\[\mathbf{x}^{(i+1)} = \mathbf{SOp}\,\left\{T_{(\epsilon \alpha /2, p)} \mathbf{SOp}^H\,\left(\mathbf{x}^{(i)} + \alpha\,\mathbf{Op}^H \left(\mathbf{d} - \mathbf{Op} \,\mathbf{x}^{(i)}\right)\right)\right\}\]

where \(\epsilon \alpha /2\) is the threshold and \(T_{(\tau, p)}\) is the thresholding rule. The most common variant of ISTA uses the so-called soft-thresholding rule \(T(\tau, p=1)\). Alternatively an hard-thresholding rule is used in the case of \(p=0\) or a half-thresholding rule is used in the case of \(p=1/2\). Finally, percentile bases thresholds are also implemented: the damping factor is not used anymore an the threshold changes at every iteration based on the computed percentile.

[1]Daubechies, I., Defrise, M., and De Mol, C., “An iterative thresholding algorithm for linear inverse problems with a sparsity constraint”, Communications on pure and applied mathematics, vol. 57, pp. 1413-1457. 2004.

Examples using pylops.optimization.sparsity.ISTA