Name
Abstract | ||
|---|---|---|
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 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. We demonstrate the superior performance of the Newton method over existing methods including the latest quadratic semidefinite programming solver. This research also opens a new avenue toward efficient solution methods for the molecular embedding problem. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1137/110849523 | SIAM JOURNAL ON MATRIX ANALYSIS AND APPLICATIONS |
Keywords | Field | DocType |
Euclidean distance matrix,semismooth Newton method,quadratic convergence | Computational problem,Mathematical optimization,Quadratic growth,Duality (optimization),Rate of convergence,Solver,Semidefinite programming,Mathematics,Euclidean distance matrix,Newton's method | Journal |
Volume | Issue | ISSN |
34 | 1 | 0895-4798 |
Citations | PageRank | References |
10 | 0.53 | 6 |
Authors | ||
1 | ||