.png){kind=icon}
Return
Accelerating randomized surrounding method with momentum for consistent linear systems
.png){kind=icon}
PRE
AI
DOI：10.1007/s00025-025-02518-y.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
The recently proposed restarted randomized surrounding algorithm improves the performance of the original randomized surrounding method by incorporating a restarting strategy. In this work, we introduce a fast restarted randomized surrounding algorithm that further accelerates convergence by integrating Polyak momentum. Compared to its predecessor, the proposed algorithm reduces the number of required iterations while maintaining nearly the same computational cost per iteration. We provide a rigorous convergence analysis to characterize the convergence rate of the proposed method. Numerical experiments demonstrate that the new algorithm outperforms existing reflection-based methods in both iteration count and total runtime.
Keywords:
Consistent linear system
Surrounding method
Polyak momentum
Kaczmarz algorithm
Randomized algorithm
Journal
R
IF:
1.2
Papers:
157
Citations:
0
Organization
No organization information available
Cited Papers
On the Polyak momentum variants of the greedy deterministic single and multiple row‐action methods.png)
IF0
PREAI
ShareSaveShareSaveA residual-based surrogate hyperplane extended Kaczmarz algorithm for large least squares problemsCalcolo
CalcoloIF0
PREAI
ShareSave
ShareSave
View more