Dec 4, 2022 · We focus on two widely used minibatch frameworks to tackle this optimization problem: Incremental Gradient (IG) and Random Reshuffling (RR).

We prove convergence under the mild assumption of Lipschitz continuity of the gradients of the component functions and perform extensive computational analysis ...

May 21, 2024 · The authors propose "ease-controlled" modifications to these schemes that require minimal additional computation but can be proven to converge ...

Convergence under Lipschitz smoothness of ease-controlled Random Reshuffling gradient Algorithms Giampaolo Liuzzi∗, Laura Palagi∗, Ruggiero Seccia ...

This work defines ease-controlled modifications of the IG/RR schemes, which require a light additional computational effort but can be proved to converge ...

Laura Palagi - Convergence under Lipschitz smoothness of ease-controlled Random Reshuffling gradient Algorithms. playlist_play. play_arrow pause. replay_10.

We prove convergence under the lonely assumption of Lipschitz continuity of the gradients of the component functions and perform extensive computational ...

We prove convergence under the lonely assumption of Lipschitz continuityof the gradients of the component functions and perform extensive ...

We consider minimizing the average of a very large number of smooth and possibly non-convex functions. This optimization problem has deserved much attention ...

The main idea is to draw at each iteration of our gradient algorithm a realization of the underlying random variable ξ, and then, to extend the gradient of J at ...