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cs.LG2024
Learning to Optimize for Mixed-Integer Non-linear Programming with Feasibility Guarantees
Bo Tang, Elias B. Khalil, Ján Drgoňa
Mixed-integer nonlinear programs (MINLPs) arise in domains such as energy systems, process engineering, and transportation, and are notoriously difficult to solve at scale due to t…
cs.LG2024
Metric Learning to Accelerate Convergence of Operator Splitting Methods for Differentiable Parametric Programming
Ethan King, James Kotary, Ferdinando Fioretto +1
Recent work has shown a variety of ways in which machine learning can be used to accelerate the solution of constrained optimization problems. Increasing demand for real-time decis…