edmcr

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).