Kangtunyakarn, Atid

Preferred name

Kangtunyakarn, Atid

Alternative Name

Kangtunyakarn, A.

Main Affiliation

School of Science

atid.ka@kmitl.ac.th

Now showing 1 - 10 of 37

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Method for Solving Variational Inequality Problems and Fixed Point Problems without Some Well-known Condition
(2024-01-01)
Nuisman, Anucha
;
Kangtunyakarn, Atid
This study presents an innovative method for approximating solutions to the variational inequality and fixed-point problems. The proposed approach deviates from traditional methods by employing different conditions and techniques drawn from [18] [20] [21]. Uniquely, our work circumvents the utilization of a commonly used lemma (see [10]) that forms the basis for most proofs related to strong convergence theorems. As part of our investigation, we provide a comprehensive numerical example to substantiate our findings, thus enhancing the practical relevance and applicability of our research.
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Modified intermixed iteration for solving the split general system of variational inequality problems and applications
(2021-12-01)
Saechou, Kanyanee
;
Kangtunyakarn, Atid
Inspired by the works of Siriyan and Kangtunyakarn (2018) and Yao et al. (2015), we first introduce the two-step intermixed iteration for finding a common element of the set of the solutions of the split general system of variational inequality problem (SGSV), and also, we prove strong convergence theorem of the intermixed algorithm. Using our main theorem, we prove strong convergence theorems for finding solutions to the split variational inequality problem (SVIP), the split feasibility problem (SFP), and the split common fixed point problem (SCFP). Moreover, we give three numerical examples of these classical problems introduced by the previous studies and an example that conflicts with our main theorem where some conditions fail.
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An intermixed algorithm for solving fixed point problems of proximal operators in Hilbert Spaces
(2024-01-01)
Khuangsatung, Wongvisarut
;
Kangtunyakarn, Atid
The aim of this paper is to modify proximal operators in Hilbert spaces. We introduce an inter-mixed algorithm with viscosity technique to find the solution of fixed point problem of two proximal operators in a real Hilbert space, utilizing the modified proximal operators. Under some mild conditions, a strong convergence theorem is established for the proposed algorithm. We also apply our main result to the split feasibility problem. Finally we provide numerical examples for supporting the main result.
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The modification of generalized mixed equilibrium problems for convergence theorem of variational inequality problems and fixed point problems
(2021-03-01)
Khuangsatung, Wongvisarut
;
Suantai, Suthep
;
Kangtunyakarn, Atid
The purpose of this research, we modify generalized mixed equilibrium problems and prove a strong convergence theorem for approximating a common element of the set of such a problem and variational inequality problems and the set of fixed points of infinite family of strictly pseudo contractive mappings. Utilizing our main result, we also prove a strong convergence theorem involving generalized equilibrium problems and variational inequality problems.
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Modified Halpern Iteration for Finding a Common Element of the Set of Solutions of Generalized Equilibrium Problem and Fixed Point Problem of Quasi-nonexpansive Mapping
(2025-09-01)
Sripattanet, Anchalee
;
Kangtunyakarn, Atid
In this paper, we introduce an iterative process for finding a common element of the set of solutions of generlized equilibrium problem and the set of fixed points problem of a quasi-nonexpansive mapping without demi-closed condition and T<inf>ω</inf>:= (1 − ω)I + ωT, where T is a quasi-nonexpansive mapping and ω ∈ (0,<sup>1</sup>) in a framework of a real Hilbert space. By using our main result, we obtain 2 strong convergence theorems involving finite families of nonspreading mapping and κ-demicontractive mapping.
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An Enhanced Discriminant Analysis Approach for Multi-Classification with Integrated Machine Learning-Based Missing Data Imputation
(2025-11-01)
Araveeporn, Autcha
;
Kangtunyakarn, Atid
This study addresses the challenge of accurate classification under missing data conditions by integrating multiple imputation strategies with discriminant analysis frameworks. The proposed approach evaluates six imputation methods (Mean, Regression, KNN, Random Forest, Bagged Trees, MissRanger) across several discriminant techniques. Simulation scenarios varied in sample size, predictor dimensionality, and correlation structure, while the real-world application employed the Cirrhosis Prediction Dataset. The results consistently demonstrate that ensemble-based imputations, particularly regression, KNN, and MissRanger, outperform simpler approaches by preserving multivariate structure, especially in high-dimensional and highly correlated settings. MissRanger yielded the highest classification accuracy across most discriminant analysis methods in both simulated and real data, with performance gains most pronounced when combined with flexible or regularized classifiers. Regression imputation showed notable improvements under low correlation, aligning with the theoretical benefits of shrinkage-based covariance estimation. Across all methods, larger sample sizes and high correlation enhanced classification accuracy by improving parameter stability and imputation precision.
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A three-intermixed algorithm for common fixed point problems with applications to convex optimization and network allocation
(2026-07-01)
Saechou, Kanyanee
;
Kangtunyakarn, Atid
This study investigates a new algorithm, termed the three-intermixed algorithm, developed from the intermixed algorithm introduced by Yao et al. (Fixed Point Theory Appl 206, 2015). A strong convergence theorem is established for finding a common element in the set of fixed points of nonexpansive mappings. To illustrate our main theorem, we provide an example in R2 along with a graphical representation showing the behavior of the sequences {xn},{yn}, and {zn}. Beyond theoretical development, the applicability of the main theorem is demonstrated through several important problems, including the convex minimization problem, the split feasibility problem, and both general and new systems of variational inequality problems. In addition, a special case of the main theorem is applied to the network bandwidth allocation problem. A detailed numerical example is presented to show how the proposed iterative scheme can be used to compute a proportionally fair allocation under capacity and minimum rate constraints, thereby confirming the practical efficiency of the method.
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An Approximation Algorithm for the Combination of G-Variational Inequalities and Fixed Point Problems
(2025-01-01)
Kheawborisut, Araya
;
Kangtunyakarn, Atid
In this paper, we introduce a modified form of the G-variational inequality problem, called the combination of G-variational inequalities problem, within a Hilbert space structured by graphs. Furthermore, we develop an iterative scheme to find a common element between the set of fixed points of a G-nonexpansive mapping and the solution set of the proposed G-variational inequality problem. Under appropriate assumptions, we establish a strong convergence theorem within the framework of a Hilbert space endowed with graphs. Additionally, we present the concept of the G-minimization problem, which diverges from the conventional minimization problem. Applying our main results, we demonstrate a strong convergence theorem for the G-minimization problem. Finally, we provide illustrative examples to validate and support our theoretical findings.
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A new subgradient extragradient method for solving the split modified system of variational inequality problems and fixed point problem
(2022-01-01)
Sripattanet, Anchalee
;
Kangtunyakarn, Atid
We introduce a new subgradient extragradient algorithm utilizing the concept of the set of solutions of the split modified system of variational inequality problems (SMSVIP). Our main theorem is weak convergence theorem for such an algorithm for approximating the fixed point problem in a real Hilbert space. We also apply these results to approximate the split minimization problem. In the last section, we provide an example to illustrate the potential of our main theorem.
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An Approximation Method of Nonlinear Mapping for a Modified General Equilibrium and System of Variational Inequality Problems
(2024-01-01)
Khumsup, Pimpawee
;
Kangtunyakarn, Atid
In this article, we present a new iteration for approximating the solutions to generalized equilibrium problems, fixed point problems of κ-strictly pseudononspreading mapping, and modifications of the system of variational inequality. The variational inequality problem and the general split feasibility problem were then solved using our primary theorem. Additionally, we provide a numerical example to support our primary theorem.

Kangtunyakarn, Atid

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