Title: Improving Feasibility via Fast Autoencoder-Based Projections
Primary area: optimization
Keywords: amortized optimization, nonconvex optimization, surrogate models, feasibility

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 and reliably 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, enabling rapid correction of neural network outputs by projecting them 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 improves constraint satisfaction at a low computational cost, offering a practical alternative to expensive feasibility correction techniques based on traditional solvers.

TL;DR: We propose a data-driven amortized approach that uses a trained autoencoder as an approximate projector to provide fast corrections to infeasible predictions.