Improving Feasibility via Fast Autoencoder-Based Projections

• URL: http://arxiv.org/abs/2604.03489v1
• Date: Fri, 03 Apr 2026 22:33:17 GMT
• Title: Improving Feasibility via Fast Autoencoder-Based Projections
• Abstract summary: Existing methods struggle to efficiently enforce general constraints.<n>We propose a data-driven amortized approach that uses an objective autoencoder as an approximate projector.
• Score: 1.7370580277522711
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Enforcing complex (e.g., nonconvex) operational constraints is a critical challenge in real-world learning and control systems. However, existing methods struggle to efficiently enforce general classes of constraints. To address this, we propose a novel data-driven amortized approach that uses a trained autoencoder as an approximate projector to provide fast corrections to infeasible predictions. Specifically, we train an autoencoder using an adversarial objective to learn a structured, convex latent representation of the feasible set. This enables rapid correction of neural network outputs by projecting their associated latent representations onto a simple convex shape before decoding into the original feasible set. We test our approach on a diverse suite of constrained optimization and reinforcement learning problems with challenging nonconvex constraints. Results show that our method effectively enforces constraints at a low computational cost, offering a practical alternative to expensive feasibility correction techniques based on traditional solvers.

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