A practical guide to robust optimization
By Bram L. Gorissen, Ihsan Yanikouglu, Dick den Hertog. 2015
651 citations recorded by the source corpus.
Robust optimization is a young and active research field that has been mainly developed in the last 15 years. Robust optimization is very useful for practice, since it is tailored to the information at hand, and it leads to computationally tractable formulations. It is therefore remarkable that real-life applications of robust optimization are still lagging behind; there is much more potential for real-life applications than has been exploited hitherto. The aim of this paper is to help practitioners to understand robust optimization and to successfully apply it in practice. We provide a brief introduction to robust optimization, and also describe important do׳s and don׳ts for using it in practice. We use many small examples to illustrate our discussions.
Subfields: Management Science and Operations Research, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/e5b2eb3ab3d2aec6b9e7e5635b1a6973c09ae4c5
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In Management Science and Operations Research
Citations in the corpus
- FedLoc: Federated Learning Framework for Data-Driven Cooperative Localization and Location Data Processing (2020, background): “A promising way to solve such issues from the algorithmic perspective is to use robust distributed optimization [125, 126], robust estimation [127], and robust fusion [109] techniques for remedy.”