A Double Inertial Forward-Backward Splitting Algorithm With Applications to Regression and Classification Problems
This is an incremental improvement for optimization in machine learning, specifically targeting regression and classification problems.
The paper tackles the problem of finding a point where the sum of a co-coercive operator and a maximal monotone operator vanishes in a real Hilbert space by proposing a double inertial forward-backward splitting algorithm, and it demonstrates weak convergence with experimental results showing superior outcomes in regression and classification benchmarks compared to existing algorithms.
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.