Fourier coefficients of normalized Cauchy transforms

Abstract We consider a certain uniqueness problem concerning the Fourier coefficients of normalized Cauchy transforms. These problems inherently involve proving a simultaneous approximation phenomenon and establishing the existence of cyclic inner functions in certain sequence spaces. Our results have several applications in different directions. First, we offer a new non-probabilistic proof of a classic theorem by Kahane, Katzenelson and Nestoridis on simultaneous approximation. Secondly, we demonstrate the absence of uniform admissible majorants of Fourier coefficients in de Branges–Rovnyak spaces, and deduce complementary results to the classical Ivashev-Musatov Theorem.

Authors

Institutions

Publication Details

Journal
Journal d Analyse Mathématique
Published
2025-12-03
DOI
https://doi.org/10.1007/s11854-025-0414-z
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

Fourier coefficients of normalized Cauchy transforms

Adem Limani
Journal d Analyse Mathématique
Approximation Theory and Sequence Spaces
article

Fourier coefficients of normalized Cauchy transforms

Adem Limani
article en

Abstract

Abstract We consider a certain uniqueness problem concerning the Fourier coefficients of normalized Cauchy transforms. These problems inherently involve proving a simultaneous approximation phenomenon and establishing the existence of cyclic inner functions in certain sequence spaces. Our results have several applications in different directions. First, we offer a new non-probabilistic proof of a classic theorem by Kahane, Katzenelson and Nestoridis on simultaneous approximation. Secondly, we demonstrate the absence of uniform admissible majorants of Fourier coefficients in de Branges–Rovnyak spaces, and deduce complementary results to the classical Ivashev-Musatov Theorem.

Journal d Analyse Mathématique
Lund University (SE)
Openalex Percentile: Top 64%
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.