Bibliographic Details
| 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] |
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| Database: |
Engineering Source |
