New Error Measures and Methods for Realizing Protein Graphs from Distance Data-Reference-Cited by-同舟云学术

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New Error Measures and Methods for Realizing Protein Graphs from Distance Data

Published:2017-01-05 Issue:2 Volume:57 Page:371-418
ISSN:0179-5376
Container-title:Discrete & Computational Geometry
language:en
Short-container-title:Discrete Comput Geom

Author:

D’Ambrosio Claudia,Vu Ky,Lavor Carlile,Liberti Leo^ORCID,Maculan Nelson

Funder

Microsoft Research

Agence Nationale de la Recherche

FAPESP

CNPq

Publisher

Springer Science and Business Media LLC

Subject

Computational Theory and Mathematics,Discrete Mathematics and Combinatorics,Geometry and Topology,Theoretical Computer Science

Link

http://link.springer.com/content/pdf/10.1007/s00454-016-9846-7.pdf

Reference56 articles.

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2. Alt, H., Mehlhorn, K., Wagener, H., Welzl, E.: Congruence, similarity and symmetries of geometric objects. Discrete Comput. Geom. 3, 237–256 (1988)

3. Arora, S., Hazan, E., Kale, S.: Fast algorithms for approximate semidefinite programming using the multiplicative weights update method. In: Foundations of Computer Science, FOCS, vol. 46, pp. 339–348. IEEE (2005)

4. Arora, S., Hazan, E., Kale, S.: The multiplicative weights update method: a meta-algorithm and applications. Theory Comput. 8, 121–164 (2012)

5. Atkinson, M.: An optimal algorithm for geometrical congruence. J. Algorithms 8, 159–172 (1987)

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