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Research questionHow can Adam’s error be bounded for strongly convex stochastic optimization without assuming bounded iterates?Analyses of Adam on strongly convex stochastic problems have often assumed that its iterates remain uniformly bounded. Without that premise, it is unclear whether the optimizer’s error can be controlled unconditionally.
Machine Learning
Neural and Evolutionary Computing
Statistical Machine Learning
Latest papersRecent research connected to this question, newest first.Uniform a priori bounds and error analysis for the Adam stochastic gradient descent optimization methodConcerns Adam applied to a large class of strongly convex stochastic optimization problems, with relevance to deep neural network training. The source supports uniform a priori bounds and unconditional error analysis as the target, but does not specify exact assumptions, rates, or parameter settings.research paper · Sep 1, 2026
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