AT-SKM-Net: An Accelerated Trainable Sampling Kaczmarz-Motzkin Framework for Linear Hard-Constraint Feasibility on Dynamic Graphs
cs.LG, cs.AI, math.OC
Submitted: 2026-09-24
Updated: 2026-09-24
Terminology
Sources
- End-to-End Probabilistic Framework for Learning with Hard Constraints
- Learning Constrained Optimization with Deep Augmented Lagrangian Methods
- HardNet: Hard-Constrained Neural Networks with Universal Approximation Guarantees
- Enforcing Hard Linear Constraints in Deep Learning Models with Decision Rules
- T-SKM-Net: Trainable Neural Network Framework for Linear Constraint Satisfaction via Sampling Kaczmarz-Motzkin Method
- SCQPTH: an efficient differentiable splitting method for convex quadratic programming
- RAYEN: Imposition of Hard Convex Constraints on Neural Networks
- Enforcing convex constraints in Graph Neural Networks
- Convergence Rates for Greedy Kaczmarz Algorithms, and Faster Randomized Kaczmarz Rules Using the Orthogonality Graph
- Warm-starting active-set solvers using graph neural networks
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