Paranormed Motzkin Difference Sequence Spaces of Fractional

This study presents a theoretical investigation of a newly defined sequence space, termed the “Motzkin fractional difference sequence space,” which is constructed through the combination of the Motzkin matrix derived from Motzkin numbers and fractional difference operators. Motzkin numbers, notable for their special role in discrete mathematics and combinatorial structures, are revisited in this work from both algebraic and topological perspectives. The completeness, normability, and topological properties of the defined spaces are thoroughly analyzed, and it is demonstrated that they are linearly isomorphic to classical sequence spaces via modulus functions. For each space, original Schauder bases are constructed, and the structures of normed Banach and quasi-paranormed spaces are rigorously established. Furthermore, the characterization of dual spaces is provided, relevant matrix transformations are identified, and norm estimations are presented. Algebraic analyses of Motzkin numbers contribute to numerical solutions, while their connections to symmetric structures offer new insights within the framework of operator theory. These findings also open pathways for interdisciplinary applications in computer science, biological modeling, and the analysis of physical systems.In conclusion, this study reveals that the newly defined sequence space based on Motzkin numbers offers original contributions to both theoretical and applied mathematics. By integrating the analysis of algebraic structures with the characterization of functional spaces, it provides a valuable addition to the existing literature.

Authors

Institutions

Publication Details

Journal
Recep Tayyip Erdoğan Üniversitesi Fen ve Mühendislik Bilimleri Dergisi
Published
2026-06-30
DOI
https://doi.org/10.53501/rteufemud.1789329
Primary Topic
Approximation Theory and Sequence Spaces
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Paranormed Motzkin Difference Sequence Spaces of Fractional

Emine Özçelik
Recep Tayyip Erdoğan Üniversitesi Fen ve Mühendislik Bilimleri Dergisi
Approximation Theory and Sequence Spaces
article

Paranormed Motzkin Difference Sequence Spaces of Fractional

Emine Özçelik
article en

Abstract

This study presents a theoretical investigation of a newly defined sequence space, termed the “Motzkin fractional difference sequence space,” which is constructed through the combination of the Motzkin matrix derived from Motzkin numbers and fractional difference operators. Motzkin numbers, notable for their special role in discrete mathematics and combinatorial structures, are revisited in this work from both algebraic and topological perspectives. The completeness, normability, and topological properties of the defined spaces are thoroughly analyzed, and it is demonstrated that they are linearly isomorphic to classical sequence spaces via modulus functions. For each space, original Schauder bases are constructed, and the structures of normed Banach and quasi-paranormed spaces are rigorously established. Furthermore, the characterization of dual spaces is provided, relevant matrix transformations are identified, and norm estimations are presented. Algebraic analyses of Motzkin numbers contribute to numerical solutions, while their connections to symmetric structures offer new insights within the framework of operator theory. These findings also open pathways for interdisciplinary applications in computer science, biological modeling, and the analysis of physical systems.In conclusion, this study reveals that the newly defined sequence space based on Motzkin numbers offers original contributions to both theoretical and applied mathematics. By integrating the analysis of algebraic structures with the characterization of functional spaces, it provides a valuable addition to the existing literature.

Recep Tayyip Erdoğan Üniversitesi Fen ve Mühendislik Bilimleri DergisiVol. 7(1)
Fırat University (TR)
Openalex Percentile: Top 6%
Approximation Theory and Sequence Spaces
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.