A Self-adaptive Super Set-relaxed Projection Method for Multiple-sets Split Feasibility Problem with Multiple Output Sets

This paper introduces an inertial accelerated super set-relaxed CQ method to solve a multiple-sets split feasibility problem with multiple output sets. The convex subsets involved are assumed to be level subsets of given strongly convex functions. Instead of using the involved sets, we approximate the original convex subsets with a sequence of closed balls. The proposed method is easy to implement as the projection onto the closed ball has a closed form. Additionally, we develop a new self-adaptive step-size that does not require any prior information of the norm. Under suitable assumptions, we establish and prove a strong convergence result for the algorithm. Numerical experiments are provided to demonstrate the performance of the proposed algorithm, which generalizes and improves upon existing literature.

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Publication Details

Journal
Nonlinear Convex Analysis and Optimization: An International Journal on Numerical, Computation and Applications
Published
2024-12-31
DOI
https://doi.org/10.58715/ncao.2024.3.4
Primary Topic
Optimization and Variational Analysis
Type
article
Field-Weighted Citation Impact
0.00

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article

A Self-adaptive Super Set-relaxed Projection Method for Multiple-sets Split Feasibility Problem with Multiple Output Sets

Songpon Sriwongsa, Guash Haile Taddele, Attapol Kaewkhao
Nonlinear Convex Analysis and Optimization: An International Journal on Numerical, Computation and Applications
Optimization and Variational Analysis
article

A Self-adaptive Super Set-relaxed Projection Method for Multiple-sets Split Feasibility Problem with Multiple Output Sets

Songpon Sriwongsa, Guash Haile Taddele, Attapol Kaewkhao
article en

Abstract

This paper introduces an inertial accelerated super set-relaxed CQ method to solve a multiple-sets split feasibility problem with multiple output sets. The convex subsets involved are assumed to be level subsets of given strongly convex functions. Instead of using the involved sets, we approximate the original convex subsets with a sequence of closed balls. The proposed method is easy to implement as the projection onto the closed ball has a closed form. Additionally, we develop a new self-adaptive step-size that does not require any prior information of the norm. Under suitable assumptions, we establish and prove a strong convergence result for the algorithm. Numerical experiments are provided to demonstrate the performance of the proposed algorithm, which generalizes and improves upon existing literature.

Nonlinear Convex Analysis and Optimization: An International Journal on Numerical, Computation and ApplicationsVol. 3(2)
Xiamen University Malaysia (MY), Chiang Mai University (TH), King Mongkut's University of Technology Thonburi (TH)
Chiang Mai University, King Mongkut's University of Technology Thonburi, Xiamen University, Thailand Science Research and Innovation
Openalex Percentile: Top 69%
Optimization and Variational Analysis
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