Inference on graphs via semidefinite programming
Name
Bandeira-2016-Inference on graphs.pdf
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527.82 KB
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Adobe PDF
Checksum (MD5)
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Author(s)
Sousa Bandeira, Afonso Jose
Date Issued
April 2016
Journal
Proceedings of the National Academy of Sciences of the United States of America
Publisher
National Academy of Sciences (U.S.)
Citation
Bandeira, Afonso S. “Inference on Graphs via Semidefinite Programming.” Proceedings of the National Academy of Sciences 113, no. 16 (April 8, 2016): 4238–4239. © 2017 National Academy of Sciences
Version
Final published version
Abstract
Inference problems on graphs arise naturally when trying to make sense of network data. Oftentimes, these problems are formulated as intractable optimization programs. This renders the need for fast heuristics to find adequate solutions and for the study of their performance. For a certain class of problems, Javanmard et al. (1) successfully use tools from statistical physics to analyze the performance of semidefinite programming relaxations, an important heuristic for intractable problems.
MIT Department
Massachusetts Institute of Technology. Department of Mathematics
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
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DOI of Published Version
https://doi.org/10.1073/pnas.1603405113
