Open Problem: Can Single-Shuffle SGD be Better than Reshuffling SGD and GD?
Proceedings of Thirty Fourth Conference on Learning Theory, PMLR 134:4653-4658, 2021.
We propose matrix norm inequalities that extend the Recht and Ré (2012) conjecture on a noncommutative AM-GM inequality, by supplementing it with another inequality that accounts for single-shuffle in stochastic finite-sum minimization. Single-shuffle is a popular without-replacement sampling scheme that shuffles only once in the beginning, but has not been studied in the Recht-Ré conjecture and the follow-up literature. Instead of focusing on general positive semidefinite matrices, we restrict our attention to positive definite matrices with small enough condition numbers, which are more relevant to matrices that arise in the analysis of SGD. For such matrices, we conjecture that the means of matrix products satisfy a series of spectral norm inequalities that imply “single-shuffle SGD converges faster than random-reshuffle SGD, which is in turn faster than with-replacement SGD and GD” in special cases.