2012•LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas)Open access

GENERALIZED HEAVISIDE FUNCTIONS IN THE COLOMBEAU THEORY CONTEXT

Francisco Villarreal

Open full text 0 citations

Abstract

We defined generalized Heaviside functions for a variable x in $mathbb{R}^n$, and for variables (x,t) in $mathbb{R}^nimesmathbb{R}^m$. Then study properties such as: composition, invertibility, and association relation (the weak equality). This work is developed in the Colombeau generalized functions context.

About this research paper

What this paper is about

We defined generalized Heaviside functions for a variable x in $mathbb{R}^n$, and for variables (x,t) in $mathbb{R}^nimesmathbb{R}^m$. Then study properties such as: composition, invertibility, and association relation (the weak equality). This work is developed in the Colombeau generalized functions context.

Why it matters

A significance statement is not available in the OpenAlex record.

Key contribution

A contribution statement is not available in the OpenAlex record.

Method / approach

Method details are not available in the OpenAlex metadata.

Main findings

Findings are not separately available in the OpenAlex metadata.

Limitations

Limitations are not available in the OpenAlex metadata.

Applications

Application details are not available in the OpenAlex metadata.

Available abstract

We defined generalized Heaviside functions for a variable x in $mathbb{R}^n$, and for variables (x,t) in $mathbb{R}^nimesmathbb{R}^m$. Then study properties such as: composition, invertibility, and association relation (the weak equality). This work is developed in the Colombeau generalized functions context.

Key concepts: Heaviside step function, Mathematics, Generalized function, Context (archaeology), Pure mathematics, Variable (mathematics), Mathematical analysis, Paleontology

Related papers

Back to paper searchBrowse research topicsOriginal source
GENERALIZED HEAVISIDE FUNCTIONS IN THE COLOMBEAU THEORY CONTEXT — Research Paper | ScholarLens