arXiv Machine Learning
Oct 3

HUANet: Hard-Constrained Unrolled ADMM for Constrained Convex Optimization

HUANet is a deep neural network architecture that unrolls the Alternating Direction Method of Multipliers (ADMM) into a trainable model for accelerating parametric constrained convex optimization. It embeds a hard‑constrained neural network in each ADMM iteration, using a differentiable correction stage to enforce affine equalities of the primal subproblem. The method also incorporates first‑order optimality conditions into a self‑supervised training loss, and numerical experiments on benchmark problems and a control application demonstrate its effectiveness in speeding up constrained convex optimization.

By Trinh Tran, Binh Nguyen, Truong X. Nghiem
arXiv Machine Learning
Jun 15

Neural Slack Variables for Shape Constraints

arXiv:2606. 13803v1 Announce Type: new Abstract: Enforcing functional inequality constraints such as monotonicity and convexity in neural networks is a fundamental challenge in many industrial and scientific applications.

By Ruben Wiedemann, Antoine Jacquier, Lukas Gonon