Implements various general algorithms to estimate missing elements of a Euclidean (squared) distance matrix. Includes optimization methods based on semidefinite programming found in Alfakih, Khadani, and Wolkowicz (1999)<doi:10.1023/A:1008655427845>, a nonconvex position formulation by Fang and O'Leary (2012)<doi:10.1080/10556788.2011.643888>, and a dissimilarity parameterization formulation by Trosset (2000)<doi:10.1023/A:1008722907820>. When the only nonmissing distances are those on the minimal spanning tree, the guided random search algorithm will complete the matrix while preserving the minimal spanning tree following Rahman and Oldford (2018)<doi:10.1137/16M1092350>. Point configurations in specified dimensions can be determined from the completions. Special problems such as the sensor localization problem, as for example in Krislock and Wolkowicz (2010)<doi:10.1137/090759392>, as well as reconstructing the geometry of a molecular structure, as for example in Hendrickson (1995)<doi:10.1137/0805040>, can also be solved. These and other methods are described in the thesis of Adam Rahman(2018)<https://hdl.handle.net/10012/13365>.
Package details 


Maintainer  R. Wayne Oldford <rwoldford@uwaterloo.ca> 
License  GPL2  GPL3 
Version  0.2.0 
URL  https://github.com/greatnortherndiver/edmcr 
Package repository  View on GitHub 
Installation 
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