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math.OC2022
Convergence of ease-controlled Random Reshuffling gradient Algorithms under Lipschitz smoothness
Ruggiero Seccia, Corrado Coppola, Giampaolo Liuzzi +1
In this work, we consider minimizing the average of a very large number of smooth and possibly non-convex functions, and we focus on two widely used minibatch frameworks to tackle…
math.OC2020★ 3 cited
Block Layer Decomposition schemes for training Deep Neural Networks
Laura Palagi, Ruggiero Seccia
Deep Feedforward Neural Networks' (DFNNs) weights estimation relies on the solution of a very large nonconvex optimization problem that may have many local (no global) minimizers,…