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Found 3 papers, 0 papers with code
Dual Interior-Point Optimization Learning
This paper introduces Dual Interior Point Learning (DIPL) and Dual Supergradient Learning (DSL) to learn dual feasible solutions to parametric linear programs with bounded variables, which are pervasive across many industries.
Bucketized Active Sampling for Learning ACOPF
This paper considers optimization proxies for Optimal Power Flow (OPF), i. e., machine-learning models that approximate the input/output relationship of OPF.
Risk-Aware Control and Optimization for High-Renewable Power Grids
The transition of the electrical power grid from fossil fuels to renewable sources of energy raises fundamental challenges to the market-clearing algorithms that drive its operations.
Uncertainty Quantification
Vocal Bursts Intensity Prediction
