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)<10.1023>,
a non-convex position formulation by Fang and O'Leary (2012)<10.1080>, and
a dissimilarity parameterization formulation by Trosset (2000)<10.1023>.
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)<10.1137>.
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)<10.1137>,
as well as reconstructing
the geometry of a molecular structure, as for example in
Hendrickson (1995)<10.1137>, can also be solved.
These and other methods are described in the thesis of Adam Rahman(2018)< https://hdl.handle.net/10012/13365>.10.1137>10.1137>10.1137>10.1023>10.1080>10.1023>