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arXiv Machine Learning By Jesus Tordesillas, Victor Klemm, Jonathan P. How, Marco Hutter

RAYEN: Imposition of Hard Convex Constraints on Neural Networks

Read the original on arXiv Machine Learning →

RAYEN is a framework that enforces hard convex constraints—linear, quadratic, second‑order cone, and linear matrix inequality—on neural network outputs or latent variables. It guarantees constraint satisfaction during both training and testing for any input and network weights without relying on expensive orthogonal projections, soft constraints, or iterative corrections. The method incurs negligible computational overhead, enabling rapid enforcement of thousands of constraints and significantly speeding up constrained trajectory optimization compared to existing algorithms.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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