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This paper establishes conditions under which the noncommutative AM-GM inequality holds, directly addressing counterexamples relevant to optimization and random reshuffling in machine learning. By leveraging techniques from computational complexity and quadrature estimates, it advances theoretical bounds that impact convergence guarantees for certain ML algorithms.
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When the noncommutative AM-GM inequality holds

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arXiv:2610.04874v1 Announce Type: cross Abstract: In this note, we prove that the noncommutative AM-GM inequality holds if $n\ge 2\lceil m/2 \rceil^2$. The motivation comes from counterexamples constructed in [De Sa, Random reshuffling is not always better, NeurIPS2020]. The proof constructs a vertex measure based on the Chebyshev nodes on the Boolean cube to extract the distinct indices. The main difficulty is that the measure is not positive on non-integer nodes. The key technique comes from [Grigoriev, Complexity of Positivstellensatz proofs for the knapsack, Computational Complexity (2001)] and eventually transforms the problem into a quadrature estimate.

Abstract:In this note, we prove that the noncommutative AM-GM inequality holds if $n\ge 2\lceil m/2 \rceil^2$. The motivation comes from counterexamples constructed in [De Sa, Random reshuffling is not always better, NeurIPS2020]. The proof constructs a vertex measure based on the Chebyshev nodes on the Boolean cube to extract the distinct indices. The main difficulty is that the measure is not positive on non-integer nodes. The key technique comes from [Grigoriev, Complexity of Positivstellensatz proofs for the knapsack, Computational Complexity (2001)] and eventually transforms the problem into a quadrature estimate.

Probability (math.PR); Machine Learning (cs.LG); Optimization and Control (math.OC)

arXiv:2610.04874 [math.PR]

(or arXiv:2610.04874v1 [math.PR] for this version)

https://doi.org/10.48550/arXiv.2610.04874 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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