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Despite the modeling power for problems under uncertainty, robust optimization (RO) and adaptive RO (ARO) can exhibit too conservative solutions in terms of objective value degradation compared with the nominal case. One of the main reasons behind this conservatism is that, in many practical applications, uncertain constraints are directly designed as constraint-wise without taking into account couplings over multiple constraints. In this paper, we define a coupled uncertainty set as the intersection between a constraint-wise uncertainty set and a coupling set. We study the benefit of coupling in alleviating conservatism in RO and ARO. We provide theoretical tight and computable upper and lower bounds on the objective value improvement of RO and ARO problems under coupled uncertainty over constraint-wise uncertainty. In addition, we relate the power of adaptability over static solutions with the coupling of uncertainty set. Computational results demonstrate the benefit of coupling in applications. Funding: I. Wang was supported by the NSF CAREER Award [ECCS 2239771] and Wallace Memorial Honorific Fellowship from Princeton University. B. Stellato was supported by the NSF CAREER Award [ECCS 2239771].
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fauci-email: a json digest of Anthony Fauci's released emails
We provide a processed JSON version of the 3234 page PDF document of Anthony Fauci's emails that were released in 2021 to provide a better understanding of the United States government response to the COVID-19 pandemic. The main JSON file contains a collection of 1289 email threads with 2761 emails among the threads, which includes 101 duplicate emails. For each email, we provide information about the sender, recipients, CC-list, subject, email body text, and email time stamp (when available). We also provide a number of derived datasets stored in individual JSON files: 5 different types of derived email networks, 1 email hypergraph, 1 temporal graph, and 3 tensors. Details for the data conversion process, the construction of the derived datasets, and subsequent analyses can all be found in an online technical report at https://arxiv.org/abs/2108.01239. Updated code for processing and analyzing the data can be found at https://github.com/nveldt/fauci-email.</p> Research additionally supported by ARO Award W911NF-19-1-0057, ARO MURI, and NSF CAREER Award IIS-2045555, as well as NSF awards CCF-1909528, IIS-2007481, and the Sloan Foundation.
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- PAR ID:
- 10351793
- Publisher / Repository:
- Zenodo
- Date Published:
- Subject(s) / Keyword(s):
- Anthony Fauci email COVID-19
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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