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An Efficient Numerical Approximation for the Monge-Kantorovich Mass Transfer Problem

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Machine Learning, Optimization, and Big Data (MOD 2015)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9432))

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Abstract

The approximation scheme for the Monge-Kantorovich mass transfer problem on compact spaces proposed in [7] is improved. The upgrade presented is inspired on a meta-heuristic algorithm called Scatter Search in order to reduce the dimensionality of the problem. The new approximation scheme solves finite linear programs similar to the transport problem but with lower dimension. A numerical example is presented and compared with the scheme studied in [7].

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Authors and Affiliations

  1. Universidad Veracruzana, Xalapa, Veracruz, Mexico

    M. L. Avendaño-Garrido, J. R. Gabriel-Argüelles, L. Quintana-Torres & E. Mezura-Montes

Authors
  1. M. L. Avendaño-Garrido
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  2. J. R. Gabriel-Argüelles
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  3. L. Quintana-Torres
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  4. E. Mezura-Montes
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Corresponding author

Correspondence to M. L. Avendaño-Garrido .

Editor information

Editors and Affiliations

  1. University of Florida, Gainsville, Florida, USA

    Panos Pardalos

  2. University of Catania, Catania, Italy

    Mario Pavone

  3. University of Catania, Catania, Italy

    Giovanni Maria Farinella

  4. University of Catania, Catania, Italy

    Vincenzo Cutello

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Avendaño-Garrido, M.L., Gabriel-Argüelles, J.R., Quintana-Torres, L., Mezura-Montes, E. (2015). An Efficient Numerical Approximation for the Monge-Kantorovich Mass Transfer Problem. In: Pardalos, P., Pavone, M., Farinella, G., Cutello, V. (eds) Machine Learning, Optimization, and Big Data. MOD 2015. Lecture Notes in Computer Science(), vol 9432. Springer, Cham. https://doi.org/10.1007/978-3-319-27926-8_20

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  • DOI: https://doi.org/10.1007/978-3-319-27926-8_20

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-27925-1

  • Online ISBN: 978-3-319-27926-8

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