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Low-Rank/Sparse-Inverse Decomposition via Woodbury

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Operations Research Proceedings 2016

Part of the book series: Operations Research Proceedings ((ORP))

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Abstract

Based on the Woodbury matrix identity, we present a heuristic and a test-problem generation method for decomposing an invertible input matrix into a low-rank component and a component having a sparse inverse.

V.K. Fuentes: Supported in part by MICDE.

J. Lee: Supported by NSF grant CMMI-1160915 and ONR grant N00014-14-1-0315.

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Author information

Authors and Affiliations

  1. University of Michigan, Ann Arbor, MI, USA

    Victor K. Fuentes & Jon Lee

Authors
  1. Victor K. Fuentes
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  2. Jon Lee
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Corresponding author

Correspondence to Victor K. Fuentes .

Editor information

Editors and Affiliations

  1. Institute of Computer Science, Helmut Schmidt University, Institute of Computer Science, Hamburg, Germany

    Andreas Fink

  2. Applied Mathematics, Helmut Schmidt University, Applied Mathematics, Hamburg, Germany

    Armin Fügenschuh

  3. Institute of Logistics and Organization, Helmut Schmidt University, Institute of Logistics and Organization, Hamburg, Germany

    Martin Josef Geiger

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Fuentes, V.K., Lee, J. (2018). Low-Rank/Sparse-Inverse Decomposition via Woodbury. In: Fink, A., Fügenschuh, A., Geiger, M. (eds) Operations Research Proceedings 2016. Operations Research Proceedings. Springer, Cham. https://doi.org/10.1007/978-3-319-55702-1_16

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