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A double inertial forward-backward splitting algorithm with applications to regression and classification problems
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AI
DOI：10.1080/0305215X.2025.2612000.png)
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Abstract
En 中文
This article 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 cocoercive operator and a maximal monotone operator vanishes. Under standard assumptions, the proposed algorithm demonstrates weak convergence. Numerous experimental results are presented to demonstrate the behaviour of the developed algorithm by comparing it with existing algorithms in the literature for regression and data classification problems. Furthermore, these implementations suggest that the proposed algorithm yields superior outcomes when benchmarked against other relevant algorithms in the existing literature.
Keywords:
Double inertial
monotone inclusion problem
regression
classification
weak convergence
Journal
IF:
2.2
Papers:
105
Citations:
3.8K