Keywords: [ Computational Learning Theory ] [ Learning Theory ] [ Matrix/Tensor Methods ]

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Abstract
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Abstract:

Stochastic optimization algorithms have become indispensable in modern machine learning. An unresolved foundational question in this area is the difference between with-replacement sampling and without-replacement sampling — does the latter have superior convergence rate compared to the former? A groundbreaking result of Recht and Re reduces the problem to a noncommutative analogue of the arithmetic-geometric mean inequality where n positive numbers are replaced by n positive definite matrices. If this inequality holds for all n, then without-replacement sampling indeed outperforms with-replacement sampling in some important optimization problems. The conjectured Recht–Re inequality has so far only been established for n = 2 and a special case of n = 3. We will show that the Recht–Re conjecture is false for general n. Our approach relies on the noncommutative Positivstellensatz, which allows us to reduce the conjectured inequality to a semidefinite program and the validity of the conjecture to certain bounds for the optimum values, which we show are false as soon as n = 5.