.png){kind=icon}
Return
A RELAXED VECTOR AUXILIARY VARIABLE ALGORITHM FOR UNCONSTRAINED OPTIMIZATION PROBLEMS
.png){kind=icon}
PRE
AI
DOI：10.1137/23M1611087.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
We present a novel optimization algorithm, a relaxed vector auxiliary variable (RVAV), that satisfies an unconditional energy dissipation law and exhibits improved alignment between the modified and the original energy. Our algorithm features rigorous proofs of linear convergence in the convex setting. Furthermore, we present a simple accelerated algorithm that improves the linear convergence rate to superlinear in the univariate case. We also propose an adaptive version of RVAV with Steffensen step size. We validate the robustness and fast convergence of our algorithm through ample numerical experiments.
Keywords:
Key words. optimization
gradient descent
machine learning
SAV
adaptive learning rate
Journal
IF:
2.6
Papers:
5.1K
Citations:
1.8W
Organization
Cited Papers
.png){kind=icon}
ShareSaveImproving the accuracy and consistency of the scalar auxiliary variable (SAV) method with relaxation
OAAI
OAAIShareSaveShareSaveShareSaveShareSaveShareSave
View more