Euclidean Distance Matrix Completion Tools

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), a non-convex position formulation by Fang and O'Leary (2012), and a dissimilarity parameterization formulation by Trosset (2000). 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). 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), as well as reconstructing the geometry of a molecular structure, as for example in Hendrickson (1995), can also be solved. These and other methods are described in the thesis of Adam Rahman(2018)<>.


Reference manual

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0.2.0 by R. Wayne Oldford, 4 months ago

Browse source code at

Authors: Adam Rahman [aut] , R. Wayne Oldford [aut, cre, ths]

Documentation:   PDF Manual  

GPL-2 | GPL-3 license

Imports Matrix, igraph, lbfgs, truncnorm, MASS, nloptr, vegan, sdpt3r, utils, methods, stats

See at CRAN