Research Article | Volume 115 Issue 2 (2025) | Published in 2025-09-10
Advances in Fixed Point Theory: Unified Results in M∗-Metric Spaces with Applications
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ABSTRACT
Fixed point theory in generalized metric spaces has emerged as an important framework for addressing nonlinear problems and differential equations beyond the classical metric setting. In this study, we develop a unified fixed point framework in (M^*)-metric spaces, extending and generalizing several existing approaches in (M_b)-metric, (M_R)-metric, and (\Omega_b)-distance spaces. The proposed framework is motivated by the need for more flexible generalized distance structures capable of accommodating complex nonlinear problems, particularly those arising in fractional calculus and differential equations.
The principal contribution of this work is the establishment of new fixed point results for mappings defined on complete (M^)-metric spaces. In particular, we prove a common fixed point theorem for two mappings (S,T\rightarrow X) satisfying a generalized contractive condition of the form[M^(S\zeta,T\kappa,T\kappa)\leq \lambda M^(\zeta,\kappa,\kappa),\qquad \lambda\in[0,1),]
and demonstrate the existence and uniqueness of a common fixed point. The proof is developed through the construction of interwoven iterative sequences, contraction estimates, the Cauchy property, and completeness of the underlying (M^)-metric space. The results further extend existing contraction principles and establish connections with weak contraction, simulation function, admissibility, and interpolative contraction approaches.
To illustrate the applicability of the theoretical findings, a detailed example is provided in a complete (M^)-metric space, demonstrating the convergence behavior of the constructed sequences toward the common fixed point. Beyond the theoretical developments, the proposed results provide a foundation for applications to nonlinear operators and fractional differential equations, while offering potential computational approaches related to atomic solution methods. Overall, this study strengthens the theoretical framework of generalized fixed point theory by providing new common fixed point results in (M^)-metric spaces and broadening their potential applications in nonlinear analysis, fractional calculus, and computational mathematics.
Keywords: M∗-metric spaces; Fixed point theory; Nonlinear contractions; Fractional calculus; Simulation functions
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المراجع
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Article history
Received : Apr 22, 2025
Revised : Apr 27, 2025
Accepted : Aug 11, 2025
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Authors Affiliations
Mohd Qasim Surmat a,1,*,
a Department of Mathematics Education, Universitas Islam Negeri Syarif Hidayatullah Jakarta, Indonesia, Email: surmat.Mohd@uinjkt.ac.id
* Corresponding Author Mohd Qasim Surmat, surmat.Mohd@uinjkt.ac.id
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Acknowledgment The author would like to express their sincere gratitude to The International Journal of Applied Sciences - Noor Al-Ilm for Publishing and Distribution for their generous support in waiving all publication fees and facilitating the publication of this manuscript free of charge. Their commitment to promoting scientific research and supporting researchers is highly appreciated. Author Contribution All authors contributed equally to the main contributor to this paper. All authors read and approved the final paper. Conflicts of Interest “The authors declare no conflict of interest.” Funding This research received no external financial funding. The authors also acknowledge The International Journal of Applied Sciences, Noor Al-Ilm for Publishing and Distribution, for providing a full waiver of the publication fees. The publication fee waiver was provided as editorial support and did not involve any financial contribution to the conduct, design, analysis, or reporting of Ethical Considerations Not applicable. This study did not require ethical approval because it does not include human or animal subjects and does not involve any personal or sensitive data. List of Abbrevation None Declaration of generative AI and AI-assisted technologies in the writing process The authors hereby declare that no generative artificial intelligence or AI-assisted technologies were used at any stage during the preparation of this manuscript, including language editing, proofreading, or content development. The authors take full responsibility for the originality and integrity of the work presented in this publication.
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