5 papers
Learning Discrete Decisions for MIPs with Constraint-Aware Diffusion
Vincenzo Di Vito, Mehdi Taghizadeh, Deepjyoti Deka +2
This paper proposes a novel learning-based approach to approximately solve instances of mixed-integer optimization problems. These problems are computationally challenging, as they…
Learning to Solve Optimization Problems Constrained with Partial Differential Equations
Yusuf Guven, Vincenzo Di Vito, Ferdinando Fioretto
Partial differential equation (PDE)-constrained optimization arises in many scientific and engineering domains, such as energy systems, fluid dynamics and material design. In these…
Learning to Optimize meets Neural-ODE: Real-Time, Stability-Constrained AC OPF
Vincenzo Di Vito, Mostafa Mohammadian, Kyri Baker +1
Recent developments in applying machine learning to address Alternating Current Optimal Power Flow (AC OPF) problems have demonstrated significant potential in providing close to o…
Learning To Solve Differential Equation Constrained Optimization Problems
Vincenzo Di Vito, Mostafa Mohammadian, Kyri Baker +1
Differential equations (DE) constrained optimization plays a critical role in numerous scientific and engineering fields, including energy systems, aerospace engineering, ecology,…
Learning Joint Models of Prediction and Optimization
James Kotary, Vincenzo Di Vito, Jacob Cristopher +2
The Predict-Then-Optimize framework uses machine learning models to predict unknown parameters of an optimization problem from exogenous features before solving. This setting is co…