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Low-dimensional embedding with extra information
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Source Annual Symposium on Computational Geometry archive
Proceedings of the twentieth annual symposium on Computational geometry table of contents
Brooklyn, New York, USA
SESSION: Session 9 table of contents
Pages: 320 - 329  
Year of Publication: 2004
ISBN:1-58113-885-7
Authors
Mihai Bǎdoiu  MIT, Cambridge, MA
Erik D. Demaine  MIT, Cambridge, MA
Mohammad Taghi Hajiaghayi  MIT, Cambridge, MA
Piotr Indyk  MIT, Cambridge, MA
Sponsors
ACM: Association for Computing Machinery
SIGACT: ACM Special Interest Group on Algorithms and Computation Theory
SIGGRAPH: ACM Special Interest Group on Computer Graphics and Interactive Techniques
Publisher
ACM  New York, NY, USA
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Downloads (6 Weeks): 3,   Downloads (12 Months): 18,   Citation Count: 11
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ABSTRACT

A frequently arising problem in computational geometry is when a physical structure, such as an ad-hoc wireless sensor network or a protein backbone, can measure local information about its geometry (e.g., distances, angles, and/or orientations), and the goal is to reconstruct the global geometry from this partial information. More precisely, we are given a graph, the approximate lengths of the edges, and possibly extra information, and our goal is to assign coordinates to the vertices that satisfy the given constraints up to a constant factor away from the best possible. We obtain the first subexponential-time (quasipolynomial-time) algorithm for this problem given a complete graph of Euclidean distances with additive error and no extra information. For general graphs, the analogous problem is NP-hard even with exact distances. Thus, for general graphs, we consider natural types of extra information that make the problem more tractable, including approximate angles between edges, the order type of vertices, a model of coordinate noise, or knowledge about the range of distance measurements. Our quasipolynomial-time algorithm for no extra information can also beviewed as a polynomial-time algorithm given an "extremum oracle" as extra information. We give several approximation algorithms and contrasting hardness results for these scenarios.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
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CITED BY  11

Collaborative Colleagues:
Mihai Bǎdoiu: colleagues
Erik D. Demaine: colleagues
Mohammad Taghi Hajiaghayi: colleagues
Piotr Indyk: colleagues