A Tauberian theorem for the logarithmic summability in ordered spaces

The present manuscript aims to extend a Tauberian theorem previously established for the Cesàro and weighted mean summability methods of single sequences in ordered spaces to the logarithmic summability method, also known as the (ℓ,1,1) method, for double sequences. In order to achieve this, we present several Tauberian conditions which address the O_L-oscillatory behavior of a double sequence (s_mn ) with respect to logarithmic summability in certain senses. These conditions facilitate the transition from (ℓ,1,1), (ℓ,1,0), and (ℓ,0,1) summability to P-convergence in ordered spaces.

Authors

Institutions

Publication Details

Journal
Balıkesir Üniversitesi Fen Bilimleri Enstitüsü Dergisi
Published
2024-12-11
DOI
https://doi.org/10.25092/baunfbed.1556267
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

A Tauberian theorem for the logarithmic summability in ordered spaces

Zerrin Önder
Balıkesir Üniversitesi Fen Bilimleri Enstitüsü Dergisi
Approximation Theory and Sequence Spaces
article

A Tauberian theorem for the logarithmic summability in ordered spaces

Zerrin Önder
article en

Abstract

The present manuscript aims to extend a Tauberian theorem previously established for the Cesàro and weighted mean summability methods of single sequences in ordered spaces to the logarithmic summability method, also known as the (ℓ,1,1) method, for double sequences. In order to achieve this, we present several Tauberian conditions which address the O_L-oscillatory behavior of a double sequence (s_mn ) with respect to logarithmic summability in certain senses. These conditions facilitate the transition from (ℓ,1,1), (ℓ,1,0), and (ℓ,0,1) summability to P-convergence in ordered spaces.

Balıkesir Üniversitesi Fen Bilimleri Enstitüsü DergisiVol. 27(1)
Yaşar University (TR)
Openalex Percentile: Top 77%
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.