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

This package has been downloaded 62 times in the last 30 days. This number of downloads could probably be counted on one hand. Well, maybe two hands and a couple of toes. The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 6 times.

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Last 365 days

This package has been downloaded 2,743 times in the last 365 days. That's enough downloads to impress a room full of undergrads. A commendable achievement indeed. The day with the most downloads was Jun 19, 2024 with 38 downloads.

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