提出双惯性前向-后向分裂算法，提升回归与分类性能
A Double Inertial Forward-Backward Splitting Algorithm With Applications to Regression and Classification Problems
- 引入两个惯性参数加速收敛，改进经典前向-后向分裂方法
- 在回归和分类任务中表现优于现有算法，实验验证有效
- 适用于需要高效求解的优化问题，适合数学与机器学习研究者
本文提出一种带有两个惯性参数的改进型前向-后向分裂算法，旨在寻找实希尔伯特空间中一个点，使得共协同算子与极大单调算子之和为零。在标准假设下，所提算法实现弱收敛。通过大量实验，将该算法与文献中已有算法在回归和数据分类问题上进行对比，结果表明其性能更优，展现出更强的收敛性与实用性。
原文摘要 · Abstract (English)
This paper presents an improved forward-backward splitting algorithm with two inertial parameters. It aims to find a point in the real Hilbert space at which the sum of a co-coercive operator and a maximal monotone operator vanishes. Under standard assumptions, our proposed algorithm demonstrates weak convergence. We present numerous experimental results to demonstrate the behavior of the developed algorithm by comparing it with existing algorithms in the literature for regression and data classification problems. Furthermore, these implementations suggest our proposed algorithm yields superior outcomes when benchmarked against other relevant algorithms in existing literature.
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