NLPOpt-Net is an unsupervised neural architecture that learns parametric solutions to constrained NLPs by pairing a backbone network with quadratic projection layers that guarantee feasibility and near-zero constraint violations.
Differentiable convex optimization layers
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Hybrid quantum-classical optimization for unit commitment uses Pauli-Correlation Encoding to solve multi-period schedules with up to 312 binary variables while satisfying load, ramping, and reserve constraints.
The paper proposes a CVaR-guided decision-focused learning framework with risk-triggered re-optimization that improves probabilistic load forecasting and two-stage robust microgrid operation while reducing online computation.
A mean-field limit yields a convex, price-responsive surrogate for aggregated storage that is learned via gradient descent on historical data and converges with population size.
An end-to-end learning framework for joint building-data-center integrated energy systems improves operational performance 7-9% over predict-then-optimize baselines and cuts total energy cost ~10% via waste-heat recovery.
citing papers explorer
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NLPOpt-Net: A Learning Method for Nonlinear Optimization with Feasibility Guarantees
NLPOpt-Net is an unsupervised neural architecture that learns parametric solutions to constrained NLPs by pairing a backbone network with quadratic projection layers that guarantee feasibility and near-zero constraint violations.
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Scaling Quantum Optimization for Unit Commitment via Pauli Correlation Encoding
Hybrid quantum-classical optimization for unit commitment uses Pauli-Correlation Encoding to solve multi-period schedules with up to 312 binary variables while satisfying load, ramping, and reserve constraints.
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CVaR-Guided Decision-Focused Learning and Risk-Triggered Re-Optimization for Two-Stage Robust Microgrid Operation
The paper proposes a CVaR-guided decision-focused learning framework with risk-triggered re-optimization that improves probabilistic load forecasting and two-stage robust microgrid operation while reducing online computation.
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Mean-Field Learning for Storage Aggregation
A mean-field limit yields a convex, price-responsive surrogate for aggregated storage that is learned via gradient descent on historical data and converges with population size.
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End-to-End Learning-based Operation of Integrated Energy Systems for Buildings and Data Centers
An end-to-end learning framework for joint building-data-center integrated energy systems improves operational performance 7-9% over predict-then-optimize baselines and cuts total energy cost ~10% via waste-heat recovery.