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국가지식-학술정보

Stability of Hahn difference equations in Banach algebras

Stability of Hahn difference equations in Banach algebras

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Hahn difference operator $D_{q,\omega}$ which is defined by \begin{equation*} D_{q,\omega}g(t)= \left\{ \begin{array}{ll} \frac {g(qt+\omega)-g(t)}{t(q-1)+\omega},&\text{if} \ \ t\neq \theta:=\frac{\omega}{1-q},\\[0.5em] g^{\prime}(\theta),&\text{if}\ \ t=\theta \end{array} \right. \end{equation*} received a lot of interest from many researchers due to its applications in constructing families of orthogonal polynomials and in some approximation problems. In this paper, we investigate sufficient conditions for stability of the abstract linear Hahn difference equations of the form \begin{equation*} D_{q,\omega}x(t)=A(t)x(t)+f(t),~ t\in I, \end{equation*} and \begin{equation*} D^{2}_{q,\omega}x(t)+A(t)D_{q,\omega}x(t)+R(t)x(t)=f(t), ~t\in I, \end{equation*} where $A,R:I\rightarrow \mathbb{X}$, and $f:I\rightarrow \mathbb{X}$. Here $\mathbb{X}$ is a Banach algebra with a unit element $\mathfrak{e}$ and $I$ is an interval of $\mathbb{R}$ containing $\theta$.

Hahn difference operator $D_{q,\omega}$ which is defined by \begin{equation*} D_{q,\omega}g(t)= \left\{ \begin{array}{ll} \frac {g(qt+\omega)-g(t)}{t(q-1)+\omega},&\text{if} \ \ t\neq \theta:=\frac{\omega}{1-q},\\[0.5em] g^{\prime}(\theta),&\text{if}\ \ t=\theta \end{array} \right. \end{equation*} received a lot of interest from many researchers due to its applications in constructing families of orthogonal polynomials and in some approximation problems. In this paper, we investigate sufficient conditions for stability of the abstract linear Hahn difference equations of the form \begin{equation*} D_{q,\omega}x(t)=A(t)x(t)+f(t),~ t\in I, \end{equation*} and \begin{equation*} D^{2}_{q,\omega}x(t)+A(t)D_{q,\omega}x(t)+R(t)x(t)=f(t), ~t\in I, \end{equation*} where $A,R:I\rightarrow \mathbb{X}$, and $f:I\rightarrow \mathbb{X}$. Here $\mathbb{X}$ is a Banach algebra with a unit element $\mathfrak{e}$ and $I$ is an interval of $\mathbb{R}$ containing $\theta$.

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