lapanda: A Matrix-Free Differentiable Solver for Nonconvex Constrained Optimization Layers

arXiv:2609.33630 · math.OC, cs.LG, cs.SY, eess.SY · Submitted 2026-09-27 · Read on arXiv

math.OC, cs.LG, cs.SY, eess.SY

Submitted: 2026-09-27

Updated: 2026-09-27

Code: https://github.com/optiXlab1/lapanda

Terminology

• Accuracy — 19× in this paper · explained in 1 episode
• Benchmark — 14× in this paper · explained in 2 episodes
• Alignment — 13× in this paper · explained in 1 episode
• Core — 8× in this paper · explained in 2 episodes
• Cosine Similarity — 5× in this paper · explained in 3 episodes
• Regularization — 3× in this paper · explained in 3 episodes
• Graph Construction — 2× in this paper · explained in 3 episodes
• IEEE International Conference on Robotics and Automation — 2× in this paper
• Robustness — 2× in this paper · explained in 5 episodes
• Steering — 2× in this paper · explained in 2 episodes
• Ablation — 1× in this paper · explained in 1 episode
• Actor — 1× in this paper · explained in 3 episodes
• Actor-Critic Model — 1× in this paper · explained in 1 episode
• Degeneracy — 1× in this paper · explained in 3 episodes
• Reinforcement Learning — 1× in this paper · explained in 4 episodes
• Sparsity — 1× in this paper · explained in 4 episodes

Sources

• Differentiating Through a Cone Program
• TurboMPC: Fast, Scalable, and Differentiable Model Predictive Control on the GPU
• Differentiable Nonlinear Model Predictive Control
• End-to-End Constrained Optimization Learning: A Survey
• A Fully First-Order Layer for Differentiable Optimization

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