forward_backward_pd¶

odl.solvers.nonsmooth.forward_backward.forward_backward_pd(x, f, g, L, h, tau, sigma, niter, callback=None, \*\*kwargs)[source]¶

The forward-backward primal-dual splitting algorithm.

The algorithm minimizes the sum of several convex functionals composed with linear operators:

min_x f(x) + sum_i g_i(L_i x) + h(x)

where f, g_i are convex functionals, L_i are linear operators, and h is a convex and differentiable functional.

The method can also be used to solve the more general problem:

min_x f(x) + sum_i (g_i @ l_i)(L_i x) + h(x)

where l_i are strongly convex functionals and @ is the infimal convolution:

(g @ l)(x) = inf_y { g(y) + l(x-y) }

Note that the strong convexity of l_i makes the convex conjugate l_i^* differentiable; see the Notes section for more information on this.

Parameters

xLinearSpaceElement: Initial point, updated in-place.
fFunctional: The functional f. Needs to have f.proximal.
gsequence of Functional’s: The functionals g_i. Needs to have g_i.convex_conj.proximal.
Lsequence of Operator’s’: Sequence of linear operators L_i, with as many elements as g.
hFunctional: The functional h. Needs to have h.gradient.
taufloat: Step size-like parameter for f.
sigmasequence of floats: Sequence of step size-like parameters for the sequence g.
niterint: Number of iterations.
callbackcallable, optional: Function called with the current iterate after each iteration.

Other Parameters

lsequence of Functional’s, optional: The functionals l_i. Needs to have g_i.convex_conj.gradient. If omitted, the simpler problem without l_i will be considered.

See also

odl.solvers.nonsmooth.primal_dual_hybrid_gradient.pdhg: Solver for similar problems without differentiability in any of the terms.
odl.solvers.nonsmooth.douglas_rachford.douglas_rachford_pd: Solver for similar problems without differentiability in any of the terms.

Notes

The mathematical problem to solve is

$\min_x f(x) + \sum_{i=0}^n (g_i \Box l_i)(L_i x) + h(x),$

where $f$ , $g_i$ , $l_i$ and $h$ are functionals and $L_i$ are linear operators. The infimal convolution $g \Box l$ is defined by

$(g \Box l)(x) = \inf_y g(y) + l(x - y).$

The exact conditions on the involved functionals are as follows: $f$ and $g_i$ are proper, convex and lower semicontinuous, and $h$ is convex and differentiable with $\eta^{-1}$ -Lipschitz continuous gradient, $\eta > 0$ .

The optional operators $\nabla l_i^*$ need to be $\nu_i$ -Lipschitz continuous. Note that in the paper, the condition is formulated as $l_i$ being proper, lower semicontinuous, and $\nu_i^{-1}$ -strongly convex, which implies that $l_i^*$ have $\nu_i$ -Lipschitz continuous gradients.

If the optional operators $\nabla l_i^*$ are omitted, the simpler problem without $l_i$ will be considered. Mathematically, this is done by taking $l_i$ to be the functionals that are zero only in the zero element and $\infty$ otherwise. This gives that $l_i^*$ are the zero functionals, and hence the corresponding gradients are the zero operators.

To guarantee convergence, the parameters $\tau$ , $\sigma$ and $L_i$ need to satisfy

$2 \min \{ \frac{1}{\tau}, \frac{1}{\sigma_1}, \ldots, \frac{1}{\sigma_m} \} \cdot \min\{ \eta, \nu_1, \ldots, \nu_m \} \cdot \sqrt{1 - \tau \sum_{i=1}^n \sigma_i ||L_i||^2} > 1,$

where, if the simpler problem is considered, all $\nu_i$ can be considered to be $\infty$ .

For reference on the forward-backward primal-dual algorithm, see [BC2015].

For more on proximal operators and algorithms see [PB2014].

References

[BC2015] Bot, R I, and Csetnek, E R. On the convergence rate of a forward-backward type primal-dual splitting algorithm for convex optimization problems. Optimization, 64.1 (2015), pp 5–23.

[PB2014] Parikh, N, and Boyd, S. Proximal Algorithms. Foundations and Trends in Optimization, 1 (2014), pp 127-239.