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ZeroSARAH: Efficient Nonconvex Finite-Sum Optimization with Zero Full Gradient Computations
Avoiding any full gradient computations (which are time-consuming steps) is important in many applications as the number of data samples $n$ usually is very large.
Adaptive Learning of the Optimal Batch Size of SGD
Recent advances in the theoretical understanding of SGD led to a formula for the optimal batch size minimizing the number of effective data passes, i. e., the number of iterations times the batch size.
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