2023
Convergence of Alternating Gradient Descent for Matrix Factorization
NeurIPS 2023spotlight
We consider alternating gradient descent (AGD) with fixed step size applied to the asymmetric matrix factorization objective. We show that, for a rank-$r$ matrix $A \in \mathbb{R}^{m \times n}$, $T = C ( \frac{\sigma_1(A)}{\sigma_r(A)} )^2 \log(1/\epsilon)$ iterations of alternating gradient d…