Monday, 15:15 - 15:40 h, Room: H 2038


Houduo Qi
Computing the nearest Euclidean distance matrix


The Nearest Euclidean distance matrix problem (NEDM) is a fundamental computational problem in applications such as multidimensional scaling and molecular conformation from nuclear magnetic resonance data in computational chemistry. Especially in the latter application, the problem is often a large scale with the number of atoms ranging from a few hundreds to a few thousands. In this paper, we introduce a semismooth Newton method that solves the dual problem of (NEDM). We prove that the method is quadratically convergent. We then present an application of the Newton method to NEDM with H-weights via majorization and an accelerated proximal gradient scheme. We demonstrate the superior performance of the Newton method over existing methods including the latest quadratic semi-definite programming solver. This research also opens a new avenue towards efficient solution methods for the molecular embedding problem.


Talk 1 of the invited session Mon.3.H 2038
"Matrix optimization" [...]
Cluster 4
"Conic programming" [...]


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