 Optimization for L 1 -Norm Error Fitting via Data AggregationYoung Woong Park ()
INFORMS Journal on Computing, 2021, vol. 33, issue 1, 120-142 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. Keywords: data aggregation; aggregate and iterative disaggregate; regression; principal component analysis (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:inm:orijoc:v:33:y:2021:i:1:p:120-142 Access Statistics for this article More articles in INFORMS Journal on Computing from INFORMS Contact information at EDIRC. |
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