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'''Projections onto Convex Sets''' (POCS), sometimes known as the '''alternating projection''' method, is a method to find a point in the intersection of two [[closed set|closed]] [[convex set|convex]] sets. It is a very simple algorithm and has been rediscovered many times.<ref name="SIAMreview"/> The simplest case, when the sets are [[affine spaces]], was analyzed by [[John von Neumann]].<ref>J. von Neumann, On rings of operators. Reduction theory, Ann. of Math. 50 (1949) 401–485 (a reprint of lecture notes first distributed in 1933).</ref>
<ref>J. von Neumann. Functional Operators, volume II. Princeton University Press, Princeton, NJ, 1950. Reprint of mimeographed lecture notes first distributed in 1933.</ref> The case when the sets are affine spaces is special, since the iterates not only converge to a point in the intersection (assuming the intersection is non-empty) but in fact to the orthogonal projection onto the intersection of the initial iterate. For general closed convex sets, the limit point need not be the projection. Classical work on the case of two closed convex sets shows that the [[rate of convergence]] of the iterates is linear.
<ref>L.G. Gubin, B.T. Polyak, and E.V. Raik. The method of projections for finding the common point of convex sets. U.S.S.R. Computational Mathematics and Mathematical Physics, 7:1–24, 1967.</ref>
<ref>H.H. Bauschke and J.M. Borwein. On the convergence of von Neumann's alternating projection algorithm for two sets. Set-Valued Analysis, 1:185–212, 1993.</ref>
There are now extensions that consider cases when there are more than one set, or when the sets are not [[convex set|convex]]<ref>{{cite DOI| 10.1287/moor.1070.0291}}</ref>, or that give faster convergence rates. Analysis of POCS and related methods attempt to show that the algorithm converges (and if so, find the [[rate of convergence]]), and whether it converges to the [[Projection_(linear_algebra)#Orthogonal_projections|projection]] of the original point. These questions are largely known for simple cases, but a topic of active research for the extensions. There are also variants of the algorithm, such as [[Dykstra's projection algorithm]]. See the references in the [[#Further_reading|further reading]] section for an overview of the variants, extensions and applications of the POCS method; a good historical background can be found in section III of <ref name="PLC">P. L. Combettes, "The foundations of set theoretic estimation," Proceedings of the IEEE, vol. 81, no. 2, pp. 182-208, February 1993. [http://www.ann.jussieu.fr/~plc/proc.pdf PDF]</ref>.
== Algorithm ==
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<math> \text{find} \; x \in \mathcal{R}^n \quad\text{such that}\; x \in C \cap D </math>
where ''C'' and ''D'' are [[closed set|closed]] [[convex set|convex sets]].
To use the POCS algorithm, one must know how to project onto the sets ''C'' and ''D'' separately.
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<math>x_{k+1} = \mathcal{P}_C \left( \mathcal{P}_D ( x_k ) \right) </math>.
The simplicity of the algorithm explains some of its popularity. If the [[Intersection (set theory)|intersection]] of ''C'' and ''D'' is non-empty, then the [[sequence]] generated by the algorithm will [[Convergent series|converge]] to some point in this intersection.
Unlike [[Dykstra's projection algorithm]], the solution need not be a projection onto the intersection ''C'' and ''D''.
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