Volume & Issue: Volume 10, Issue 2, 2023, Pages 1-80 (Autumn- Winter) 

cK-frames and cK-Riesz bases in Hilbert spaces

Pages 1-18

https://doi.org/10.22072/wala.2023.550565.1372

Azam Shekari, Mohammad Reza Abdollahpour

Abstract In this paper, we prove some new results about cK-frames. Also, we introduce the concept of cK-Riesz basis and we provide a necessary and sufficient condition under which $F$ is a cK-Riesz basis. Finally, for the closed range operator $K \in B(\mathcal{H})$, we prove that under some conditions, $\pi_{R(K)}F$ is a cK-Riesz basis if and only if it has only one dual, where $\pi_{R(K)}$ is the orthogonal projection from $\mathcal{H}$ onto $R(K)$, i.e., the range of $K$.

Localization Operators on Sobolev Spaces

Pages 19-27

https://doi.org/10.22072/wala.2023.559564.1394

Fatemeh Esmaeelzadeh

Abstract In this paper,  we discuss some generalizations coming from wavelet transform on Sobolev spaces. In particular, we introduce the bounded localization operators on Sobolev spaces which are related to multi-dimensional wavelet transform on Sobolev spaces. Moreover, we propose the localization operators on Sobolev spaces are in $p$-Schatten class and they are compact. Finally, we give the boundedness and compactness of localization operators on Sobolev spaces with two admissible wavelets.

Spectral clustering by considering stationary distribution vector and transition matrix

Pages 29-38

https://doi.org/10.22072/wala.2023.1989619.1413

Elaheh Vaziri, Mina Jamshidi, Hassan Motallebi

Abstract  One of the popular methods of data clustering is spectral clustering. The main step of this method is constructing a graph representation of the data set and its similarity matrix. The similarity matrices which are constructed based on some important points not all data points, are among the main approaches. In this paper, the stationary distribution for a random walk on a weighted graph $G$ is considered to find anchor points of the data set. Then we build the similarity matrix based on the anchor nodes and the weighted random walk transition matrix. After that, spectral clustering is applied on the gained similarity matrix. We propose the theoretical discussions and then we evaluate our method on benchmarks.        

$C$-spectral norm inequalities between operator matrices and their entries

Pages 39-50

https://doi.org/10.22072/wala.2023.1989702.1414

Sharifeh Rezagholi, Mahya Hosseini

Abstract In this paper, the notion of $C-$spectral norm is introduced for operators; it was defined and studied for matrices before. Here, some $C-$spectral norm inequalities between operator matrices and their operator entries, for $2\times 2$ and $n \times n$ operator matrices, are studied. Also, some $C-$spectral norm equalities between operator matrices are brought.

A study on the continuity of some classes of $ E $-$\mathbb {Q} $-convex functions

Pages 63-69

https://doi.org/10.22072/wala.2023.2003060.1422

Zohreh Heydarpour, Masoumeh Aghajani

Abstract As a generalization of convexity,  $ E $-convexity has been defined and studied in many publications. In this study, we recall the class of $ E $-$\mathbb {Q} $-convex sets, $ E $-$ \mathbb {Q}  $-convex and $ E $-additive functions and proved some properties of $ E $-$ \mathbb {Q}  $-convex functions.  Also, we develop the classical theorems of Jensen and Bernstein-Doetsch on $ E $-$ \mathbb {Q}  $-convex functions when vector spaces are over the rational numbers $ \mathbb {Q} $.

An equivalent condition for linear preservers of multivariate group majorization on matrices

Pages 71-80

https://doi.org/10.22072/wala.2023.2006793.1428

Mohammad Soleymani, Abbas Salemi

Abstract T. Ando characterized linear preservers of majorization in  [Linear Algebra Appl. 118 (1989) 163-248]. In this note, we present a method to state a simple proof of Ando's theorem. By using this method, we state an equivalent condition for matrix representations of linear preservers of $G$-majorization on matrices, where  $G$ is a finite subgroup of orthogonal  group $O(\mathbb{R}^n)$.
Moreover, we introduce reflective majorization and characterize all its linear preservers.

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