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20212026
most citedSharp Global Guarantees for Nonconvex Low-rank Recovery in the Noisy Overparameterized Regime

4 citations · 4 across the 6 of their papers we have counts for

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math.OC2026

Spectral Initialization and Certification for Power System Angle Estimation

Iven Guzel, Andrew D. McRae, Richard Y. Zhang

Power System State Estimation (PSSE) is commonly formulated as a nonconvex weighted least-squares (WLS) problem, making global optimality difficult both to attain and to certify. R…

math.OC2026

Sharp recovery and landscape guarantees for the nonconvex matrix LASSO

Andrew D. McRae, Richard Y. Zhang

Low-rank matrix recovery can be solved to statistical optimality by convex matrix optimization under the classical assumption of restricted isometry property (RIP). However, for la…

math.OC2025

Nonnegative Low-rank Matrix Recovery Can Have Spurious Local Minima

Richard Y. Zhang

Low-rank matrix recovery is well-known to exhibit benign nonconvexity under the restricted isometry property (RIP): every second-order critical point is globally optimal, so local…

math.OC2024

Power System State Estimation by Phase Synchronization and Eigenvectors

Iven Guzel, Richard Y. Zhang

To estimate accurate voltage phasors from inaccurate voltage magnitude and complex power measurements, the standard approach is to iteratively refine a good initial guess using the…

math.OC2021★ 4 cited

Sharp Global Guarantees for Nonconvex Low-rank Recovery in the Noisy Overparameterized Regime

Richard Y. Zhang

Recent work established that rank overparameterization eliminates spurious local minima in nonconvex low-rank matrix recovery under the restricted isometry property (RIP). But this…