May 19, 2021 · In this paper, we consider gradient methods for minimizing smooth convex functions, which employ the information obtained at the previous iterations.
In this paper, we consider gradient methods for minimizing smooth convex functions, which employ the information obtained at the previous iterations.
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We compare the usual Gradient Method with two gradient methods with memory, which use different strategies for updating the piece-wise model of the objective ...
An efficient accelerated gradient method with memory applicable to ...
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Gradient Methods with Memory store at runtime part of the oracle information obtained at previous iterations. This model allows them to outperform classical ...
1. The Gradient Method (GM), the simplest among first-order methods, was initially designed to minimize unconstrained smooth convex functions.
In this paper, we consider gradient methods for minimizing smooth convex functions, which employ the information obtained at the pre-.
We develop and analyze an accelerated distributed gradient descent method, named Acc-DGDlm, which utilizes gradient tracking technique and local memory.
Momentum methods in common use include the heavy- ball method, the conjugate gradient method, and Nesterov's accelerated gradient methods. We will also consider ...
A limited mem- ory version of the nonlinear conjugate gradient method is developed. The memory is used to both detect the loss of orthogonality and to restore ...
A set of accelerated first order algorithms with memory are proposed for minimising strongly convex functions. The algorithms are differentiated by their ...