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)[https://doi.org/10.1023%2FA%3A1008655427845], a non-convex position formulation by Fang and O'Leary (2012)[https://doi.org/10.1080%2F10556788.2011.643888], and a dissimilarity parameterization formulation by Trosset (2000)[https://doi.org/10.1023%2FA%3A1008722907820]. 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)[https://doi.org/10.1137%2F16M1092350]. 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)[https://doi.org/10.1137%2F090759392], as well as reconstructing the geometry of a molecular structure, as for example in Hendrickson (1995)[https://doi.org/10.1137%2F0805040], can also be solved. These and other methods are described in the thesis of Adam Rahman(2018)[https://hdl.handle.net/10012/13365].

Last 30 days

Last 365 days

The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.