arXiv Machine Learning
Jun 9
LEAF: A Learning-Enabled ADMM Framework for Accelerated Convex Optimization
arXiv:2606. 08993v1 Announce Type: new Abstract: We propose LEAF, a learning-enabled ADMM framework for accelerated convex optimization.
Learning to Optimize by Differentiable Programming
arXiv:2601. 16510v3 Announce Type: replace-cross Abstract: Solving massive-scale optimization problems requires scalable first-order methods with low per-iteration cost.
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arXiv Machine Learning
Jun 16
A Fully First-Order Layer for Differentiable Optimization
arXiv:2512. 02494v2 Announce Type: replace Abstract: Differentiable optimization layers enable learning systems to make decisions by solving embedded optimization problems.
arXiv Machine Learning
1d ago
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.
arXiv AI
Jun 3
Introduction to optimization methods for training SciML models
arXiv:2601. 10222v2 Announce Type: replace-cross Abstract: Optimization is central to both modern machine learning (ML) and scientific machine learning (SciML), yet the structure of the underlying optimization problems differs substantially across these domains.
arXiv Machine Learning
Jul 27
Energy Manifold Natural Gradient Descent: Riemannian Optimization for Neural PDE Solvers
arXiv:2607. 22004v1 Announce Type: new Abstract: Energy natural gradient descent (ENGD) aligns parameter updates with the curvature of an underlying function-space energy, but existing formulations assume an unconstrained Euclidean parameter domain.
arXiv Machine Learning
Jun 29
Mosaic: A Benchmark Suite for Differentiable Physics Solvers
arXiv:2606. 27895v1 Announce Type: cross Abstract: Differentiable partial differential equation (PDE) solvers underpin solver-in-the-loop ML training, gradient-based optimal control, and inverse problems, yet the practical cost of obtaining correct, usable gradients from a given solver on a given problem is largely undocumented.
arXiv Machine Learning
Jun 30
Extrapolating from Regularised Solutions for Solving Ill-Conditioned Linear Systems in Machine Learning
arXiv:2606. 30328v1 Announce Type: cross Abstract: Rapid prototyping of algorithms is a critical step in modern machine learning.
arXiv Statistics ML
Sep 16
A proximal augmented Lagrangian method for nonconvex optimization with equality and inequality constraints
arXiv:2509.02894v2 Announce Type: replace-cross Abstract: We propose an inexact proximal augmented Lagrangian method (P-ALM) for nonconvex structured optimization problems. The proposed method featur...
arXiv AI
2d ago
Reinforcement Learning to Accelerate Primal-Dual Hybrid Gradient for Linear Programming
arXiv:2610.01546v1 Announce Type: cross Abstract: Primal-dual hybrid gradient (PDHG) methods solve large-scale linear programs (LPs) using GPU-friendly matrix-vector products and projections, but the...
arXiv Machine Learning
Jun 16
Self-Supervised Learning of Iterative Solvers for Constrained Optimization
arXiv:2409. 08066v3 Announce Type: replace Abstract: The real-time solution of parametric optimization problems is critical for applications that demand high accuracy under tight real-time constraints, such as model predictive control.
arXiv Machine Learning
Jun 9
Parameter Tuning with Generalization Guarantees for GPU-Accelerated Linear Programming
arXiv:2606. 08638v1 Announce Type: cross Abstract: Recent research has developed practical, parallelizable first-order methods for large scale linear programming, but performance is highly dependent on hyperparameter selection.
arXiv Machine Learning
Jul 27
Complexity Bounds and Approaches to Learning Projected Gradient Descent Solver Iterates
arXiv:2607. 22467v1 Announce Type: new Abstract: Data scarcity poses a fundamental challenge in training generative models to produce initial guesses for parametric optimization problems that are otherwise numerically expensive to solve.