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[2508.10480] Pinet: Optimizing hard-constrained neural networks with orthogonal projection layers
Summary
The paper introduces $ ext{Pinet}$, a novel output layer for neural networks that optimizes hard constraints using orthogonal projection layers, enhancing training speed and solution quality.
Why It Matters
This research addresses the challenge of efficiently solving constrained optimization problems in neural networks, which is crucial for applications requiring high reliability and speed, such as robotics and motion planning. The proposed method significantly improves training times and robustness compared to existing solutions, making it a valuable advancement in machine learning.
Key Takeaways
- Introduces $ ext{Pinet}$, a new layer for neural networks that ensures convex constraint satisfaction.
- Utilizes operator splitting for efficient projections during the forward pass.
- Achieves faster training times and improved solution quality compared to traditional methods.
- Demonstrates applicability in multi-vehicle motion planning with non-convex preferences.
- Provides a GPU-ready implementation in JAX for practical use.
Computer Science > Machine Learning arXiv:2508.10480 (cs) [Submitted on 14 Aug 2025 (v1), last revised 18 Feb 2026 (this version, v2)] Title:Pinet: Optimizing hard-constrained neural networks with orthogonal projection layers Authors:Panagiotis D. Grontas, Antonio Terpin, Efe C. Balta, Raffaello D'Andrea, John Lygeros View a PDF of the paper titled Pinet: Optimizing hard-constrained neural networks with orthogonal projection layers, by Panagiotis D. Grontas and Antonio Terpin and Efe C. Balta and Raffaello D'Andrea and John Lygeros View PDF Abstract:We introduce an output layer for neural networks that ensures satisfaction of convex constraints. Our approach, $\Pi$net, leverages operator splitting for rapid and reliable projections in the forward pass, and the implicit function theorem for backpropagation. We deploy $\Pi$net as a feasible-by-design optimization proxy for parametric constrained optimization problems and obtain modest-accuracy solutions faster than traditional solvers when solving a single problem, and significantly faster for a batch of problems. We surpass state-of-the-art learning approaches by orders of magnitude in terms of training time, solution quality, and robustness to hyperparameter tuning, while maintaining similar inference times. Finally, we tackle multi-vehicle motion planning with non-convex trajectory preferences and provide $\Pi$net as a GPU-ready package implemented in JAX. Comments: Subjects: Machine Learning (cs.LG); Artificial Intellige...
