Abstract
Distance geometry problems (DGP) arise from the need to position entities in the Euclidean K-space given some of their respective distances. Entities may be atoms (molecular distance geometry), wireless sensors (sensor network localization), or abstract vertices of a graph (graph drawing). In the context of molecular distance geometry, the distances are usually known because of chemical properties and nuclear magnetic resonance experiments; sensor networks can estimate their relative distance by recording the power loss during a two-way exchange; finally, when drawing graphs in two or three dimensions, the graph to be drawn is given, and therefore distances between vertices can be computed. DGPs involve a search in a continuous Euclidean space, but sometimes the problem structure helps reduce the search to a discrete set of points. In this paper we survey some continuous and discrete methods for solving some problems of molecular distance geometry.
| Original language | English |
|---|---|
| Pages (from-to) | 33-51 |
| Number of pages | 19 |
| Journal | International Transactions in Operational Research |
| Volume | 18 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - 1 Jan 2011 |
Keywords
- Distance geometry
- Optimization
- Protein conformation
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