Two noise-tolerant, bound-constrained AdaGrad variants for multilevel and domain-decomposition problems are proved to find an epsilon-approximate critical point in O(epsilon^-2) iterations with high probability.
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Recursive Bound-Constrained AdaGrad with Applications to Multilevel and Domain Decomposition Minimization
Two noise-tolerant, bound-constrained AdaGrad variants for multilevel and domain-decomposition problems are proved to find an epsilon-approximate critical point in O(epsilon^-2) iterations with high probability.