mlsanityData to 14 Oct 2026

Strong NP-hardness of AC power flows feasibility

arXiv 1512.07315 ↗First posted 23 Dec 2015math.OCcited by 14

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Abstract (arXiv)

We present a rigorous proof of strong NP-hardness of the AC-OPF problem.

Key facts. Cited by 14 papers in the map (data through 14 Oct 2026). First posted on arXiv 23 Dec 2015.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 2; 2022: 1; 2023: 1; 2024: 1; 2025: 2; 2026: 7.

14 papers.

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  1. 2021Modeling the AC Power Flow Equations with Optimally Compact Neural Networks: Application to Unit Commitmentcited by 17
  2. 2022Unsupervised Optimal Power Flow Using Graph Neural Networkscited by 14
  3. 2021A Reinforcement Learning Approach to Parameter Selection for Distributed Optimal Power Flowcited by 5
  4. 2026LUMINA: A Grid Foundation Model for Benchmarking AC Optimal Power Flow Surrogate Learningcited by 2
  5. 2023Optimal Power Flow in Highly Renewable Power System Based on Attention Neural Networkscited by 1
  6. 2024End-to-End Reinforcement Learning of Curative Curtailment with Partial Measurement Availabilitycited by 1
  7. 2025Quantum Artificial Intelligence for Mission-Critical Systems: Foundations, Architectural Elements, and Future Directionscited by 1
  8. 2026Towards Systematic Generalization for Power Grid Optimization Problemscited by 1
  9. 2025Rebuild AC Power Flow Models with Graph Attention Networkscited by 0
  10. 2026Robustness of Reinforcement Learning-Based Congestion Management in Low-Voltage Gridscited by 0
  11. 2026Graph Machine Learning: An Opportunity for Power Systemscited by 0
  12. 2026Scaling Laws for Physics-Aware ACOPF Surrogate Learningcited by 0
2 more
  1. 2026Learning Samples Importance: Parameterizing Dual Variables in Everywhere Learningcited by 0
  2. 2026ML-OPF-Bench: Benchmarking Machine Learning for Optimal Power Flowcited by 0