May 19, 2021 · In this paper, we consider gradient methods for minimizing smooth convex functions, which employ the information obtained at the previous iterations.

Jan 13, 2021 · We compare the usual Gradient Method with two gradient methods with memory, which use different strategies for updating the piece-wise model ...

Mar 14, 2022 · In this work, we introduce several Gradient Methods with Memory that can solve composite problems efficiently, including unconstrained problems with non-smooth ...

We compare the usual Gradient Method with two gradient methods with memory, which use different strategies for updating the piece-wise model of the ...

We compare the usual Gradient Method with two gradient methods with memory, which use different strategies for updating the piece-wise model of the ...

Jul 20, 2022 · The Inexact Gradient Method with Memory (IGMM) is able to considerably outperform the Gradient Method by employing a piece-wise linear lower ...

Momentum methods in common use include the heavy- ball method, the conjugate gradient method, and Nesterov's accelerated gradient methods. We will also consider ...

In this work we propose an accelerated gradient method with memory applicable to composite problems. In our method the model overhead remains negligible.

Apr 15, 2023 · In this paper, we propose a new descent method, called multiobjective memory gradient method, for finding Pareto critical points of a multiobjective ...

A set of accelerated first order algorithms with memory are proposed for minimising strongly convex functions. The algorithms are differentiated by their ...