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Optimization for L 1 -Norm Error Fitting via Data Aggregation

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  • Young Woong Park

    (Ivy College of Business, Iowa State University, Ames, Iowa 50011)

Abstract

We propose a data aggregation-based algorithm with monotonic convergence to a global optimum for a generalized version of the L 1 -norm error fitting model with an assumption of the fitting function. The proposed algorithm generalizes the recent algorithm in the literature, aggregate and iterative disaggregate (AID), which selectively solves three specific L 1 -norm error fitting problems. With the proposed algorithm, any L 1 -norm error fitting model can be solved optimally if it follows the form of the L 1 -norm error fitting problem and if the fitting function satisfies the assumption. The proposed algorithm can also solve multidimensional fitting problems with arbitrary constraints on the fitting coefficients matrix. The generalized problem includes popular models, such as regression and the orthogonal Procrustes problem. The results of the computational experiment show that the proposed algorithms are faster than the state-of-the-art benchmarks for L 1 -norm regression subset selection and L 1 -norm regression over a sphere. Furthermore, the relative performance of the proposed algorithm improves as data size increases.

Suggested Citation

  • Young Woong Park, 2021. "Optimization for L 1 -Norm Error Fitting via Data Aggregation," INFORMS Journal on Computing, INFORMS, vol. 33(1), pages 120-142, January.
    • Handle: RePEc:inm:orijoc:v:33:y:2021:i:1:p:120-142
    DOI: 10.1287/ijoc.2019.0908
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    References listed on IDEAS

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    Cited by:

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