Construction of the log‐convex minorant of a sequence {Mα}α∈N0d$\lbrace M_\alpha \rbrace _{\alpha \in \mathbb {N}_0^d}$

Abstract We give a simple construction of the log‐convex minorant of a sequence and consequently extend to the ‐dimensional case the well‐known formula that relates a log‐convex sequence to its associated function , that is, . We show that in the more dimensional anisotropic case the classical log‐convex condition is not sufficient: convexity as a function of more variables is needed (not only coordinate‐wise). We finally obtain some applications to the inclusion of spaces of rapidly decreasing ultradifferentiable functions in the matrix weighted setting.

Authors

Institutions

Publication Details

Journal
Mathematische Nachrichten
Published
2024-11-27
DOI
https://doi.org/10.1002/mana.202400135
Primary Topic
Mathematical Analysis and Transform Methods
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Construction of the log‐convex minorant of a sequence {Mα}α∈N0d$\lbrace M_\alpha \rbrace _{\alpha \in \mathbb {N}_0^d}$

Gerhard Schindl, Chiara Boiti, Alessandro Oliaro, David Jornet
Mathematische Nachrichten
Mathematical Analysis and Transform Methods
article

Construction of the log‐convex minorant of a sequence {Mα}α∈N0d$\lbrace M_\alpha \rbrace _{\alpha \in \mathbb {N}_0^d}$

Gerhard Schindl, Chiara Boiti, Alessandro Oliaro, David Jornet
article en

Abstract

Abstract We give a simple construction of the log‐convex minorant of a sequence and consequently extend to the ‐dimensional case the well‐known formula that relates a log‐convex sequence to its associated function , that is, . We show that in the more dimensional anisotropic case the classical log‐convex condition is not sufficient: convexity as a function of more variables is needed (not only coordinate‐wise). We finally obtain some applications to the inclusion of spaces of rapidly decreasing ultradifferentiable functions in the matrix weighted setting.

Mathematische NachrichtenVol. 298(2)
University of Vienna (AT), University of Ferrara (IT), University of Turin (IT), Universitat Politècnica de València (ES)
Austrian Science Fund, Università degli Studi di Ferrara, Gruppo Nazionale per l'Analisi Matematica, la Probabilità e le loro Applicazioni, European Regional Development Fund, Agencia Estatal de Investigación
Reduced inequalities
Openalex Percentile: Top 65%
Mathematical Analysis and Transform Methods
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.