Preprint
Sep 2026
Low-rank matrix recovery landscapes beyond RIP with application to rank-one measurements
Through high-probability concentration bounds on the random measurement operator, a novel landscape guarantee with statistically near-optimal sample complexity and recovery error is proved: new guarantees of a benign landscape under a weaker isometry condition.
Andrew D. McRae
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