An alternating preconditioned gradient algorithm removes the damping term and achieves linear convergence to near-optimal error for noisy over-parameterized matrix sensing and related low-rank problems.
Preconditioning matters: Fast global convergence of non-convex matrix factorization via scaled gradient descent,
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Efficient Over-parameterized Matrix Sensing from Noisy Measurements via Alternating Preconditioned Gradient Descent
An alternating preconditioned gradient algorithm removes the damping term and achieves linear convergence to near-optimal error for noisy over-parameterized matrix sensing and related low-rank problems.