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Learning with Discrete Least Squares on Multivariate Polynomial Spaces Using Evaluations at Random or Low-Discrepancy Point Sets Read chapter Read first chapter

2015 | OriginalPaper | Chapter

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

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

Published in: Machine Learning, Optimization, and Big Data

Publisher: Springer International Publishing

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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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Metadata
Title
An Efficient Numerical Approximation for the Monge-Kantorovich Mass Transfer Problem
Authors
M. L. Avendaño-Garrido
J. R. Gabriel-Argüelles
L. Quintana-Torres
E. Mezura-Montes
Copyright Year
2015
Book
Machine Learning, Optimization, and Big Data

Print ISBN: 978-3-319-27925-1

Electronic ISBN: 978-3-319-27926-8

Copyright Year: 2015

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

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