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First-order Optimization Methods with Generalized Smoothness for Low-rank Matrix Factorization
This paper studies several first-order methods for low-rank matrix minimization in the Burer--Monteiro form, where the target matrix is represented as the product of a nonnegative factor matrix and its transpose. Such a factorization destroys convexity of the objective function and global Lipschitz
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