sprosr_seq

datasets

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)<https://hdl.handle.net/10012/13365>.

sprosr_seq: Demo Data - SEQ

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