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Two-Stage Stochastic and Robust Optimization for Non-Adaptive Group Testing

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  • Ho-Nguyen, Nam

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.

Suggested Citation

  • Ho-Nguyen, Nam, 2020. "Two-Stage Stochastic and Robust Optimization for Non-Adaptive Group Testing," Working Papers BAWP-2020-04, University of Sydney Business School, Discipline of Business Analytics.
    • Handle: RePEc:syb:wpbsba:2123/23695
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    References listed on IDEAS

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    1. Jose Blanchet & Karthyek Murthy, 2019. "Quantifying Distributional Model Risk via Optimal Transport," Mathematics of Operations Research, INFORMS, vol. 44(2), pages 565-600, May.
    2. Aharon Ben-Tal & Elad Hazan & Tomer Koren & Shie Mannor, 2015. "Oracle-Based Robust Optimization via Online Learning," Operations Research, INFORMS, vol. 63(3), pages 628-638, June.
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