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AIMS Mathematics

2024, Volume 9, Issue 4: 8476-8496. doi: 10.3934/math.2024412
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Research article

A new two-step inertial algorithm for solving convex bilevel optimization problems with application in data classification problems

  • 1.
    PhD Degree Program in Mathematics, Department of Mathematics, Faculty of Science, Chiang Mai University, under the CMU Presidential Scholarship, Thailand
  • 2.
    Research Center in Optimization and Computational Intelligence for Big Data Prediction, Department of Mathematics, Faculty of Science, Chiang Mai University, Chiang Mai, 50200, Thailand
  • Received: 12 January 2024 Revised: 21 February 2024 Accepted: 23 February 2024 Published: 28 February 2024

  • MSC : 47H10, 65K10, 90C25

  • In this paper, we propose a new accelerated algorithm for solving convex bilevel optimization problems using some fixed point and two-step inertial techniques. Our focus is on analyzing the convergence behavior of the proposed algorithm. We establish a strong convergence theorem for our algorithm under some control conditions. To demonstrate the effectiveness of our algorithm, we utilize it as a machine learning algorithm to solve data classification problems of some noncommunicable diseases, and compare its efficacy with BiG-SAM and iBiG-SAM.

    Citation: Puntita Sae-jia, Suthep Suantai. A new two-step inertial algorithm for solving convex bilevel optimization problems with application in data classification problems[J]. AIMS Mathematics, 2024, 9(4): 8476-8496. doi: 10.3934/math.2024412

    Related Papers:

  • In this paper, we propose a new accelerated algorithm for solving convex bilevel optimization problems using some fixed point and two-step inertial techniques. Our focus is on analyzing the convergence behavior of the proposed algorithm. We establish a strong convergence theorem for our algorithm under some control conditions. To demonstrate the effectiveness of our algorithm, we utilize it as a machine learning algorithm to solve data classification problems of some noncommunicable diseases, and compare its efficacy with BiG-SAM and iBiG-SAM.


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  • © 2024 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
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