Min-Max Grassmannian Optimization for Online Subspace Tracking

arXiv:2604.00825 · eess.SY, cs.SY, math.OC · Submitted 2026-04-01 · Read on arXiv

eess.SY, cs.SY, math.OC

Submitted: 2026-04-01

Updated: 2026-09-22

Comments: Accepted for the 65th IEEE Conference on Decision and Control, December 15-18 2026, Honolulu, Hawaii, USA

Code: https://github.com/shreyasnb/gerost

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

Terminology

• Robustness — 10× in this paper · explained in 5 episodes
• Spectral Gap — 7× in this paper · explained in 5 episodes
• AUC — 6× in this paper · explained in 2 episodes
• Convergence Guarantees — 6× in this paper · explained in 3 episodes
• System Identification — 5× in this paper · explained in 3 episodes
• Trace — 3× in this paper · explained in 2 episodes
• Ambient Dimension — 2× in this paper · explained in 1 episode
• Benchmark — 1× in this paper · explained in 2 episodes
• Benchmarking — 1× in this paper · explained in 3 episodes
• Core — 1× in this paper · explained in 2 episodes
• Stiefel manifold — 1× in this paper · explained in 2 episodes

Sources

• ReProCS: A Missing Link between Recursive Robust PCA and Recursive Sparse Recovery in Large but Correlated Noise
• Online Subspace Learning on Flag Manifolds for System Identification
• Geometrically robust least squares through manifold optimization
• Robust Least-Squares Optimization for Data-Driven Predictive Control: A Geometric Approach
• Simple algorithms for optimization on Riemannian manifolds with constraints

Related papers

• One Request, Multiple Experts: LLM Orchestrates Domain Specific Models via Adaptive Task Routing
• A Geometric Decision Procedure for STL Feasibility and Repair
• From Asymptotic to Designer-Assigned-Time Control: A Review of Stability Notions, Design Mechanisms, and Controller Architectures
• Battery Second Life: A Review of Experimental Studies
• Submodular Multi-Agent Policy Learning for Online Distributed Task Allocation in Open Multi-Agent Systems
• Policy-Level Recursive Self-Improvement for Embodied AI with a Criticality World Model