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Estimating Matching Affinity Matrices under Low-Rank Constraints

Author

Listed:
  • Arnaud Dupuy

    (University of Luxembourg [Luxembourg])

  • Alfred Galichon

    (NYU - NYU System, CIMS - Courant Institute of Mathematical Sciences [New York] - NYU - New York University [New York] - NYU - NYU System, ECON - Département d'économie (Sciences Po) - Sciences Po - Sciences Po - CNRS - Centre National de la Recherche Scientifique)

  • Yifei Sun

    (CIMS - Courant Institute of Mathematical Sciences [New York] - NYU - New York University [New York] - NYU - NYU System)

Abstract

In this paper, we address the problem of estimating transport surplus (a.k.a. matching affinity) in high-dimensional optimal transport problems. Classical optimal transport theory specifies the matching affinity and determines the optimal joint distribution. In contrast, we study the inverse problem of estimating matching affinity based on the observation of the joint distribution, using an entropic regularization of the problem. To accommodate high dimensionality of the data, we propose a novel method that incorporates a nuclear norm regularization that effectively enforces a rank constraint on the affinity matrix. The low-rank matrix estimated in this way reveals the main factors that are relevant for matching.

Suggested Citation

  • Arnaud Dupuy & Alfred Galichon & Yifei Sun, 2019. "Estimating Matching Affinity Matrices under Low-Rank Constraints," Post-Print hal-03948102, HAL.
    • Handle: RePEc:hal:journl:hal-03948102
    DOI: 10.1093/imaiai/iaz015
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    Cited by:

    1. Alfred Galichon, 2021. "The Unreasonable Effectiveness of Optimal Transport in Economics," SciencePo Working papers Main hal-03936221, HAL.
    2. Alfred Galichon, 2021. "The Unreasonable Effectiveness of Optimal Transport in Economics," Working Papers hal-03936221, HAL.

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