Constructing Packings in Grassmannian Manifolds via Alternating Projection
This paper describes a numerical method for finding good packings in Grassmannian manifolds equipped with various metrics. This investigation also encompasses packing in projective spaces. In each case, producing a good packing is equivalent to constructing a matrix that has certain structural and spectral properties. By alternately enforcing the structural condition and then the spectral condition, it is often possible to reach a matrix that satisfies both. One may then extract a packing from this matrix. This approach is both powerful and versatile. In cases in which experiments have been performed, the alternating projection method yields packings that compete with the best packings recorded. It also extends to problems that have not been studied numerically. For example, it can be used to produce packings of subspaces in real and complex Grassmannian spaces equipped with the Fubini–Study distance; these packings are valuable in wireless communications. One can prove that some of the novel configurations constructed by the algorithm have packing diameters that are nearly optimal.
© 2007 A. K. Peters. Date: May 2004. Revised November 2006 and August 2007. ISD was supported by NSF grant CCF-0431257, NSF Career Award ACI-0093404, and NSF-ITR award IIS-0325116. RWH was supported by NSF CCF Grant #514194. TS was supported by NSF DMS Grant #0511461. JAT was supported by an NSF Graduate Fellowship, a J. T. Oden Visiting Faculty Fellowship, and NSF DMS 0503299.
Accepted Version - 0709.0535.pdf