Tight Majorizations and Convergence Rates of Nuclear Norm Minimization IRLS

arXiv:2608.23765 · cs.LG, cs.NA, math.NA, math.OC · Submitted 2026-08-24 · Read on arXiv

cs.LG, cs.NA, math.NA, math.OC

Submitted: 2026-08-24

Updated: 2026-08-24

Comments: 97 pages, 9 figures, 2 tables

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Core — 51× in this paper · explained in 2 episodes
• Matrix Completion — 7× in this paper · explained in 3 episodes
• Generalization — 5× in this paper · explained in 13 episodes
• Convergence Guarantees — 4× in this paper · explained in 3 episodes
• Benchmark — 3× in this paper · explained in 2 episodes
• International Conference on Machine Learning — 3× in this paper
• Regularization — 3× in this paper · explained in 3 episodes
• Sample Complexity — 3× in this paper · explained in 15 episodes
• Ambient Dimension — 2× in this paper · explained in 1 episode
• Inverse Problem — 2× in this paper · explained in 2 episodes
• Riemannian optimization — 2× in this paper · explained in 2 episodes
• Trace — 2× in this paper · explained in 2 episodes
• Accuracy — 1× in this paper · explained in 1 episode
• Alignment — 1× in this paper · explained in 1 episode
• Hilbert Space — 1× in this paper · explained in 2 episodes
• Objective Function — 1× in this paper · explained in 2 episodes
• Phase Transitions — 1× in this paper · explained in 2 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Spectral Geometry — 1× in this paper · explained in 2 episodes

Sources

• IRLS for Sparse Recovery Revisited: Examples of Failure and a Remedy
• Muon Optimizes Under Spectral Norm Constraints
• DeepSeek-V4: Towards Highly Efficient Million-Token Context Intelligence
• Kimi K2: Open Agentic Intelligence
• Global Convergence of Iteratively Reweighted Least Squares for Robust Subspace Recovery
• Muon is Scalable for LLM Training
• Iteratively reweighted kernel machines efficiently learn sparse functions

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