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Can I read Mixed-Integer Optimization with Constraint Learning on EtoBox?
Mixed-Integer Optimization with Constraint Learning by Donato Maragno; Holly Wiberg; Dimitris Bertsimas; Ş. İlker Birbil; Dick den Hertog; Adejuyigbe O. Fajemisin is a Engineering article available to read on EtoBox.
What is Mixed-Integer Optimization with Constraint Learning about?
In today’s data-driven world, there is a growing opportunity for optimization models to more closely resemble real-world scenarios, namely through learning constraints or objective functions that are not explicitly known and must be estimated through data. In “Mixed-Integer Optimization with Constraint Learning,” the authors establish a novel methodological framework for data-driven decision making. Their approach enables constraints and objectives to be embedded directly from trained machine learning models that are mixed-integer optimization representable including linear models, decision trees, ensembles, and neural networks. The authors propose two different strategies to manage uncertainty in learned constraints. The first is based on the concept of trust region where the convex hull of data points is used to avoid extrapolation. Additionally, they present an ensemble learning method for enforcing constraints across multiple estimators, improving the robustness of the downstream prediction accuracy. Practitioners can access this framework through the “OptiCL” Python package. Case studies on World Food Programme humanitarian aid planning and chemotherapy regimen optimization de
Who reads Mixed-Integer Optimization with Constraint Learning?
It is typically read by researchers, students, and practitioners in Engineering.
- Author
- Donato Maragno; Holly Wiberg; Dimitris Bertsimas; Ş. İlker Birbil; Dick den Hertog; Adejuyigbe O. Fajemisin
- Publisher
- Institute for Operations Research and the Management Sciences (INFORMS)
- Published
- 2023
- Language
- EN
- Field
- Engineering (Social Sciences)