Two-Stage Stochastic and Robust Optimization for Non-Adaptive Group Testing
Field | Value | Language |
dc.contributor.author | Ho-Nguyen, Nam | |
dc.date.accessioned | 2020-10-28 | |
dc.date.available | 2020-10-28 | |
dc.date.issued | 2020-10-28 | |
dc.identifier.uri | https://hdl.handle.net/2123/23695 | |
dc.description.abstract | We consider the problem of detecting defective items amongst a large collection, by conducting tests of individual or groups of items. Group testing offers improvements over the naive individual testing scheme by potentially certifying multiple individual items as non-defective with a single test. The group testing problem aims to design a group testing plan to detect the defective items using as few tests as possible. We propose novel two-stage stochastic and robust optimization formulations for the design of group testing plans in the noiseless non-adaptive setting. Our formulations enable us to certify optimality for existing group testing schemes, as well as model complex grouping constraints, a feature that is not discussed in the existing literature. | en_AU |
dc.language.iso | en | en_AU |
dc.rights | Copyright All Rights Reserved | en_AU |
dc.title | Two-Stage Stochastic and Robust Optimization for Non-Adaptive Group Testing | en_AU |
dc.type | Working Paper | en_AU |
dc.subject.asrc | 0102 Applied Mathematics | en_AU |
dc.subject.asrc | 0103 Numerical and Computational Mathematics | en_AU |
dc.subject.asrc | 0104 Statistics | en_AU |
usyd.faculty | The University of Sydney Business School | en_AU |
usyd.department | Business Analytics | en_AU |
workflow.metadata.only | No | en_AU |
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