Summability Methods for the Greedy Algorithm in Banach spaces

For the past 25 years, one of the most studied algorithms in the field of Nonlinear Approximation Theory has been the Thresholding Greedy Algorithm. In this paper, we propose new summability methods for this algorithm, generating two new types of greedy-like bases - namely Cesàro quasi-greedy and de la Vallée-Poussin-quasi-greedy bases. We analyze the connection between these types of bases and the well-known quasi-greedy bases, and leave some open problems for future research. In addition, as a consequence of our techniques for handling these summability methods, we answer a question posed by P. Wojtaszczyk in [16], by giving a categorial proof of equivalence between the uniform boundedness of the greedy sums and the convergence of the thresholding greedy algorithm.

Publication Details

Journal
ArXiv.org
Published
2025-04-17
Primary Topic
Stochastic Gradient Optimization Techniques
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Summability Methods for the Greedy Algorithm in Banach spaces

ArXiv.org
Stochastic Gradient Optimization Techniques
article

Summability Methods for the Greedy Algorithm in Banach spaces

article en

Abstract

For the past 25 years, one of the most studied algorithms in the field of Nonlinear Approximation Theory has been the Thresholding Greedy Algorithm. In this paper, we propose new summability methods for this algorithm, generating two new types of greedy-like bases - namely Cesàro quasi-greedy and de la Vallée-Poussin-quasi-greedy bases. We analyze the connection between these types of bases and the well-known quasi-greedy bases, and leave some open problems for future research. In addition, as a consequence of our techniques for handling these summability methods, we answer a question posed by P. Wojtaszczyk in [16], by giving a categorial proof of equivalence between the uniform boundedness of the greedy sums and the convergence of the thresholding greedy algorithm.

ArXiv.org
Openalex Percentile: Top 73%
Stochastic Gradient Optimization Techniques
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.

Summability Methods for the Greedy Algorithm in Banach spaces · ArXiv.org (2025) | TGRS Research Map | TGRS