American Institute of Mathematical Sciences

Here we present Iyengar type integral inequalities. At the univariate level they involve \begin{document}$ \psi $\end{document}-Hilfer left and right fractional derivatives. At the multivariate level they involve Hilfer left and right fractional derivatives, and they deal with radial and non-radial functions on the ball and spherical shell. All estimates are with respect to norms \begin{document}$ \left \Vert \cdot \right \Vert _{p} $\end{document}, \begin{document}$ 1\leq p\leq \infty $\end{document}. At the end we provide an application.

In the present study, matrix perturbation bounds on the eigenvalues and on the invariant subspaces found by principal component analysis is investigated, for the case in which the data matrix on which principal component analysis is performed is a convex combination of two data matrices. The application of the theoretical analysis to multi-objective optimization problems – e.g., those arising in the design of mechanical metamaterial filters – is also discussed, together with possible extensions.

The concern of this study is to construction of a multidimensional version of Urysohn type generalized sampling operators, whose one dimensional case defined and investigated by the author in [28] and [27]. In details, as a continuation of the studies of the author, the paper centers around to investigation of some approximation and asymptotic properties of the aforementioned linear multidimensional Urysohn type generalized sampling operators.

The translation of an operator is defined by using conjugation with time-frequency shifts. Thus, one can define \begin{document}$ \Lambda $\end{document}-shift-invariant subspaces of Hilbert-Schmidt operators, finitely generated, with respect to a lattice \begin{document}$ \Lambda $\end{document} in \begin{document}$ \mathbb{R}^{2d} $\end{document}. These spaces can be seen as a generalization of classical shift-invariant subspaces of square integrable functions. Obtaining sampling results for these subspaces appears as a natural question that can be motivated by the problem of channel estimation in wireless communications. These sampling results are obtained in the light of the frame theory in a separable Hilbert space.

Some limit theorems are presented for Riemann-Lebesgue integrals where the functions \begin{document}$ G_n $\end{document} and the measures \begin{document}$ M_n $\end{document} are interval valued and the convergence for the multisubmeasures is setwise. In particular sufficient conditions in order to obtain \begin{document}$ \int G_n dM_n \to \int G dM $\end{document} are given.

In this paper, a new generalization of the Bernstein-Kantorovich type operators involving multiple shape parameters is introduced. Certain Voronovskaja and Grüss-Voronovskaya type approximation results, statistical convergence and statistical rate of convergence of proposed operators are obtained by means of a regular summability matrix. Some illustrative graphics that demonstrate the convergence behavior, accuracy and consistency of the operators are given via Maple algorithms. The proposed operators are comprehensively compared with classical Bernstein, Bernstein-Kantorovich and other new modifications of Bernstein operators such as \begin{document}$ \lambda $\end{document}-Bernstein, \begin{document}$ \lambda $\end{document}-Bernstein-Kantorovich, \begin{document}$ \alpha $\end{document}-Bernstein and \begin{document}$ \alpha $\end{document}-Bernstein-Kantorovich operators.