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Sampling from the complement of a polyhedron: An MCMC algorithm for data augmentation.

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Title: Sampling from the complement of a polyhedron: An MCMC algorithm for data augmentation.
Authors: Chan, Timothy C.Y.1 (AUTHOR) tcychan@mie.utoronto.ca, Diamant, Adam2 (AUTHOR) adiamant@schulich.yorku.ca, Mahmood, Rafid1 (AUTHOR) rafid.mahmood@mail.utoronto.ca
Source: Operations Research Letters. Nov2020, Vol. 48 Issue 6, p744-751. 8p.
Subjects: Algorithms, Markov chain Monte Carlo, Supervised learning, Machine learning
Abstract: We present an MCMC algorithm for sampling from the complement of a polyhedron. Our approach is based on the Shake-and-bake algorithm for sampling from the boundary of a set and provably covers the complement. We use this algorithm for data augmentation in a machine learning task of classifying a hidden feasible set in a data-driven optimization pipeline. Numerical results on simulated and MIPLIB instances demonstrate that our algorithm, along with a supervised learning technique, outperforms conventional unsupervised baselines. [ABSTRACT FROM AUTHOR]
Copyright of Operations Research Letters is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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PubType: Academic Journal
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  Data: We present an MCMC algorithm for sampling from the complement of a polyhedron. Our approach is based on the Shake-and-bake algorithm for sampling from the boundary of a set and provably covers the complement. We use this algorithm for data augmentation in a machine learning task of classifying a hidden feasible set in a data-driven optimization pipeline. Numerical results on simulated and MIPLIB instances demonstrate that our algorithm, along with a supervised learning technique, outperforms conventional unsupervised baselines. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Operations Research Letters is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1016/j.orl.2020.08.014
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        Text: English
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      – SubjectFull: Algorithms
        Type: general
      – SubjectFull: Markov chain Monte Carlo
        Type: general
      – SubjectFull: Supervised learning
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      – SubjectFull: Machine learning
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      – TitleFull: Sampling from the complement of a polyhedron: An MCMC algorithm for data augmentation.
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            NameFull: Chan, Timothy C.Y.
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              Text: Nov2020
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