Implements various general algorithms to estimate missing elements of a Euclidean (squared) distance matrix. Includes optimization methods based on semi-definite programming found in Alfakih, Khadani, and Wolkowicz (1999)<doi:10.1023/A:1008655427845>, a non-convex 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 non-missing 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>.
|Author||Adam Rahman [aut], R. Wayne Oldford [aut, cre, ths]|
|Maintainer||R. Wayne Oldford <firstname.lastname@example.org>|
|License||GPL-2 | GPL-3|
|Package repository||View on CRAN|
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