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Fast Approximate Quadratic Programming for Graph Matching

Author

Listed:
  • Joshua T Vogelstein
  • John M Conroy
  • Vince Lyzinski
  • Louis J Podrazik
  • Steven G Kratzer
  • Eric T Harley
  • Donniell E Fishkind
  • R Jacob Vogelstein
  • Carey E Priebe

Abstract

Quadratic assignment problems arise in a wide variety of domains, spanning operations research, graph theory, computer vision, and neuroscience, to name a few. The graph matching problem is a special case of the quadratic assignment problem, and graph matching is increasingly important as graph-valued data is becoming more prominent. With the aim of efficiently and accurately matching the large graphs common in big data, we present our graph matching algorithm, the Fast Approximate Quadratic assignment algorithm. We empirically demonstrate that our algorithm is faster and achieves a lower objective value on over 80% of the QAPLIB benchmark library, compared with the previous state-of-the-art. Applying our algorithm to our motivating example, matching C. elegans connectomes (brain-graphs), we find that it efficiently achieves performance.

Suggested Citation

  • Joshua T Vogelstein & John M Conroy & Vince Lyzinski & Louis J Podrazik & Steven G Kratzer & Eric T Harley & Donniell E Fishkind & R Jacob Vogelstein & Carey E Priebe, 2015. "Fast Approximate Quadratic Programming for Graph Matching," PLOS ONE, Public Library of Science, vol. 10(4), pages 1-17, April.
  • Handle: RePEc:plo:pone00:0121002
    DOI: 10.1371/journal.pone.0121002
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    References listed on IDEAS

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    1. Marguerite Frank & Philip Wolfe, 1956. "An algorithm for quadratic programming," Naval Research Logistics Quarterly, John Wiley & Sons, vol. 3(1‐2), pages 95-110, March.
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    Cited by:

    1. Chung, Jaewon & Bridgeford, Eric & Arroyo, Jesus & Pedigo, Benjamin D. & Saad-Eldin, Ali & Gopalakrishnan, Vivek & Xiang, Liang & Priebe, Carey E. & Vogelstein, Joshua T., 2020. "Statistical Connectomics," OSF Preprints ek4n3, Center for Open Science.

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