from collections.abc import Callable
from pylops.utils.typing import NDArray
from pyproximal.proximal._Dykstra import (
_select_impl_by_arity,
dykstra_two,
parallel_dykstra_projection,
)
[docs]
class GenericIntersectionProj:
r"""Convex projection of the intersection of convex sets
using Dykstra's algorithm.
Parameters
----------
projections : :obj:`list`
A list of projection functions :math:`P_1, \ldots, P_m`.
niter : :obj:`int`, optional
Maximum number of iterations.
tol : :obj:`float`, optional
Tolerance on change of the solution (used as stopping criterion).
If ``tol=0``, run until ``niter`` is reached.
use_parallel : :obj:`bool`, optional
If True, use the parallel version when $m=2$.
Notes
-----
Given a collection of convex projections :math:`P_i` for :math:`i = 1, \ldots, m`,
where each projection :math:`P_i(\mathbf{x})` maps a point :math:`\mathbf{x}`
onto the convex set :math:`C_i`, the overall projection :math:`P_C(\mathbf{x})`
of :math:`\mathbf{x}` onto the intersection of such sets
:math:`C = \cap_{i=1}^m C_i` is computed using the Dykstra's algorithm.
(:math:`C` should not be empty.)
For :math:`m=2`, the projection :math:`P_C(\mathbf{x})` of :math:`\mathbf{x}` is computed
by the Dykstra's algorithm [1]_, [2]_, [3]_:
* :math:`\mathbf{x}^0 = \mathbf{x}, \mathbf{p}^0 = \mathbf{q}^0 = 0`,
* for :math:`k = 1, \ldots`
* :math:`\mathbf{y}^k = P_1(\mathbf{x}^k + \mathbf{p}^k)`
* :math:`\mathbf{p}^{k+1} = \mathbf{x}^k + \mathbf{p}^k - \mathbf{y}^k`
* :math:`\mathbf{x}^{k+1} = P_2(\mathbf{y}^k + \mathbf{q}^k)`
* :math:`\mathbf{q}^{k+1} = \mathbf{y}^k + \mathbf{q}^k - \mathbf{x}^{k+1}`
For :math:`m \ge 2`, the projection :math:`P_C(\mathbf{x})` is computed
by the parallel Dykstra's algorithm [5]_, [6]_. The following
is taken from [4]_:
* :math:`\mathbf{u}_m^0 = \mathbf{x}, \mathbf{z}_1^0 = \cdots = \mathbf{z}_m^0 = 0`,
* for :math:`k = 1, \ldots`
* :math:`\mathbf{u}_0^k = \mathbf{u}_m^{k-1}`
* for :math:`i = 1, \ldots, m`
* :math:`\mathbf{u}_i^k = P_i(\mathbf{u}_{i-1}^k + \mathbf{z}_i^{k-1})`
* :math:`\mathbf{z}_i^k = \mathbf{z}_i^{k-1} + \mathbf{u}_{i-1}^k - \mathbf{u}_i^k`
Note the this is the proximal operator of the corresponding
indicator function
(see :class:`pyproximal.GenericIntersectionProx` for details).
References
----------
.. [1] Bauschke, H.H., Borwein, J.M., 1994. Dykstra's Alternating
Projection Algorithm for Two Sets. Journal of Approximation Theory 79,
418-443. https://doi.org/10.1006/jath.1994.1136
https://cmps-people.ok.ubc.ca/bauschke/Research/02.pdf
.. [2] Bauschke, H.H., Burachik, R.S., Herman, D.B., Kaya, C.Y., 2020. On
Dykstra's algorithm: finite convergence, stalling, and the method of
alternating projections. Optim Lett 14, 1975-1987.
https://doi.org/10.1007/s11590-020-01600-4
https://arxiv.org/abs/2001.06747
.. [3] Wikipedia, Dykstra's projection algorithm.
https://en.wikipedia.org/wiki/Dykstra%27s_projection_algorithm
.. [4] Tibshirani, R.J., 2017. Dykstra's Algorithm, ADMM, and Coordinate
Descent: Connections, Insights, and Extensions, NeurIPS2017. Eq.(4).
https://proceedings.neurips.cc/paper_files/paper/2017/hash/5ef698cd9fe650923ea331c15af3b160-Abstract.html
.. [5] Bauschke, H.H., Combettes, P.L., 2011. Convex Analysis and Monotone
Operator Theory in Hilbert Spaces, Theorem 29.2, 1st ed, Springer.
https://doi.org/10.1007/978-1-4419-9467-7
.. [6] Bauschke, H.H., Lewis, A.S., 2000. Dykstras algorithm with bregman
projections: A convergence proof. Optimization 48, 409-427.
https://doi.org/10.1080/02331930008844513
https://people.orie.cornell.edu/aslewis/publications/00-dykstras.pdf
See also
--------
pyproximal.projection.IntersectionProj :
The convex projection onto the intersection of a particular type of convex sets.
pyproximal.GenericIntersectionProx :
The corresponding indicator function.
pyproximal.Sum :
Proximal operator of a sum of two or more convex functions
using Dykstra-like algorithm.
"""
def __init__(
self,
projections: list[Callable[[NDArray], NDArray]],
niter: int = 1000,
tol: float = 1e-6,
use_parallel: bool = False,
) -> None:
self.projections = projections
self.niter = niter
self.tol = tol
self._proj = _select_impl_by_arity(
projections,
use_parallel=use_parallel,
single=self._single_proj,
two=self._two_proj,
more=self._more_proj,
)
def __call__(self, x: NDArray) -> NDArray:
r"""compute projection :math:`P_C(\mathbf{x})` of :math:`\mathbf{x}`."""
return self._proj(x)
def _single_proj(self, x0: NDArray) -> NDArray:
r"""Compute projection :math:`P_C(\mathbf{x})` for :math:`m=1`."""
if len(self.projections) != 1:
msg = "len(projections) should be 1"
raise ValueError(msg)
return self.projections[0](x0)
def _two_proj(self, x0: NDArray) -> NDArray:
r"""Compute projection :math:`P_C(\mathbf{x})` for :math:`m=2`."""
if len(self.projections) != 2:
msg = "len(projections) should be 2"
raise ValueError(msg)
step1, step2 = self.projections
return dykstra_two(
x0,
step1,
step2,
niter=self.niter,
tol=self.tol,
)
def _more_proj(self, x0: NDArray) -> NDArray:
r"""Compute projection :math:`P_C(x)` for :math:`m \ge 2`."""
if len(self.projections) < 2:
msg = "len(projections) should be 2 or larger"
raise ValueError(msg)
return parallel_dykstra_projection(
x0,
proj_ops=self.projections,
niter=self.niter,
tol=self.tol,
)
PyProximal