Optimization for L1-Norm Error Fitting via Data Aggregation

dc.contributor.author Park, Young-Woong
dc.contributor.author Park, Young-Woong
dc.contributor.department Information Systems and Business Analytics
dc.date 2021-05-03T20:57:21.000
dc.date.accessioned 2021-08-14T18:11:59Z
dc.date.available 2021-08-14T18:11:59Z
dc.date.copyright Wed Jan 01 00:00:00 UTC 2020
dc.date.embargo 2021-05-03
dc.date.issued 2020-06-15
dc.description.abstract <p>We propose a data aggregation-based algorithm with monotonic convergence to a global optimum for a generalized version of the <em>L</em>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 <em>L</em>1-norm error fitting problems. With the proposed algorithm, any <em>L</em>1-norm error fitting model can be solved optimally if it follows the form of the <em>L</em>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 <em>L</em>1-norm regression subset selection and <em>L</em>1-norm regression over a sphere. Furthermore, the relative performance of the proposed algorithm improves as data size increases.</p>
dc.description.comments <p>This article is published as Young Woong Park Optimization for L1-Norm Error Fitting via Data Aggregation.<em> INFORMS Journal on Computing</em> 2020 33(1);120-142 doi: <a target="_blank">10.1287/ijoc.2019.0908</a>. Posted with permission. </p>
dc.format.mimetype application/pdf
dc.identifier archive/lib.dr.iastate.edu/isba_pubs/3/
dc.identifier.articleid 1002
dc.identifier.contextkey 22766438
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath isba_pubs/3
dc.identifier.uri https://dr.lib.iastate.edu/handle/20.500.12876/7wbOPqRv
dc.source.bitstream archive/lib.dr.iastate.edu/isba_pubs/3/2019_ParkYW_Optimization_for_L1_Norm_Error_Fitting_via_Data.pdf|||Fri Jan 14 23:26:30 UTC 2022
dc.subject.disciplines Business Analytics
dc.subject.disciplines Management Information Systems
dc.subject.disciplines Management Sciences and Quantitative Methods
dc.subject.disciplines Technology and Innovation
dc.subject.keywords data aggregation
dc.subject.keywords aggregate and iterative disaggregate
dc.subject.keywords regression
dc.subject.keywords principal component analysis
dc.title Optimization for L1-Norm Error Fitting via Data Aggregation
dc.type article
dc.type.genre article
dspace.entity.type Publication
relation.isAuthorOfPublication 401090a0-a926-4926-87ea-dc3aea9919d8
relation.isOrgUnitOfPublication 0099bcd5-3121-4f25-813d-0ec68d96243f
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