Scalable distributional robustness in a class of non convex optimization with guarantees
Distributionally robust optimization (DRO) has shown lot of promise in providing robustness in learning as well as sample based optimization problems. We endeavor to provide DRO solutions for a class of sum of fractionals, non-convex optimization which is used for decision making in prominent areas...
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sg-smu-ink.sis_research-84472022-10-20T07:37:58Z Scalable distributional robustness in a class of non convex optimization with guarantees BOSE, Avinandan SINHA, Arunesh MAI, Tien Distributionally robust optimization (DRO) has shown lot of promise in providing robustness in learning as well as sample based optimization problems. We endeavor to provide DRO solutions for a class of sum of fractionals, non-convex optimization which is used for decision making in prominent areas such as facility location and security games. In contrast to previous work, we find it more tractable to optimize the equivalent variance regularized form of DRO rather than the minimax form. We transform the variance regularized form to a mixed-integer second order cone program (MISOCP), which, while guaranteeing near global optimality, does not scale enough to solve problems with real world data-sets. We further propose two abstraction approaches based on clustering and stratified sampling to increase scalability, which we then use for real world data-sets. Importantly, we provide near global optimality guarantees for our approach and show experimentally that our solution quality is better than the locally optimal ones achieved by state-of-the-art gradient-based methods. We experimentally compare our different approaches andbaselines, and reveal nuanced properties of a DRO solution. 2022-12-01T08:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/7444 https://ink.library.smu.edu.sg/context/sis_research/article/8447/viewcontent/DRO_final.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Fractional distributionally robustness mixed-integer second order cone Artificial Intelligence and Robotics Systems Architecture |
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Fractional distributionally robustness mixed-integer second order cone Artificial Intelligence and Robotics Systems Architecture BOSE, Avinandan SINHA, Arunesh MAI, Tien Scalable distributional robustness in a class of non convex optimization with guarantees |
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Distributionally robust optimization (DRO) has shown lot of promise in providing robustness in learning as well as sample based optimization problems. We endeavor to provide DRO solutions for a class of sum of fractionals, non-convex optimization which is used for decision making in prominent areas such as facility location and security games. In contrast to previous work, we find it more tractable to optimize the equivalent variance regularized form of DRO rather than the minimax form. We transform the variance regularized form to a mixed-integer second order cone program (MISOCP), which, while guaranteeing near global optimality, does not scale enough to solve problems with real world data-sets. We further propose two abstraction approaches based on clustering and stratified sampling to increase scalability, which we then use for real world data-sets. Importantly, we provide near global optimality guarantees for our approach and show experimentally that our solution quality is better than the locally optimal ones achieved by state-of-the-art gradient-based methods. We experimentally compare our different approaches andbaselines, and reveal nuanced properties of a DRO solution. |
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text |
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BOSE, Avinandan SINHA, Arunesh MAI, Tien |
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BOSE, Avinandan SINHA, Arunesh MAI, Tien |
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BOSE, Avinandan |
title |
Scalable distributional robustness in a class of non convex optimization with guarantees |
title_short |
Scalable distributional robustness in a class of non convex optimization with guarantees |
title_full |
Scalable distributional robustness in a class of non convex optimization with guarantees |
title_fullStr |
Scalable distributional robustness in a class of non convex optimization with guarantees |
title_full_unstemmed |
Scalable distributional robustness in a class of non convex optimization with guarantees |
title_sort |
scalable distributional robustness in a class of non convex optimization with guarantees |
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Institutional Knowledge at Singapore Management University |
publishDate |
2022 |
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https://ink.library.smu.edu.sg/sis_research/7444 https://ink.library.smu.edu.sg/context/sis_research/article/8447/viewcontent/DRO_final.pdf |
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