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Complexity and performance for two classes of noise-tolerant first-order algorithms
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Two classes of algorithms for optimization in the presence of noise are presented, that do not require the evaluation of the objective function. The first generalizes the well-known Adagrad method. Its complexity is then analyzed as a function of its parameters. A second class of algorithms is then derived whose complexity is at least as good as that of the first class. Initial numerical experiments on finite-sum problems arising from deep-learning applications suggest that methods of the second class may outperform those of the first.
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prunAdag: an adaptive pruning-aware gradient method
prunAdag separates parameters into optimisable and decreasable sets, updates them with Adagrad-like rules, and provably drives the average gradient norm to zero at rate O(log(k)/sqrt(k+1)).
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