Taher Yazdanpanah

Taher Yazdanpanah
Persian Gulf University | PGU · Department of Mathematics

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15
Publications
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100
Citations
Citations since 2017
0 Research Items
34 Citations
201720182019202020212022202302468
201720182019202020212022202302468
201720182019202020212022202302468
201720182019202020212022202302468

Publications

Publications (15)
Article
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In this paper, we define the notion of sigma-approximate module amenability of Banach algebras and give some properties about this notion. Also for Banach \(\mathfrak {A}\)-bimodule \(\mathcal {A}\), and \(\mathcal {J}\), the closed ideal of \(\mathcal {A}\) generated by elements of form \((\alpha \cdot a) b-a(b\cdot \alpha )\), \((a,b\in \mathcal...
Article
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In this paper we consider some property of approximate character amenable Banach algebras. Also we express the concept of approximately φ-left amenability of subspaces in A*. Then we investigate the multipliers on such algebras.
Article
Let A and B be Banach algebras. We show that when B is commutative or character space of B is nonempty and A and B is weakly amenable, then A is weakly amenable too.
Article
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We investigate σ -approximate contractibility and σ -approximate amenability of Banach algebras, which are extensions of usual notions of contractibility and amenability, respectively, where σ is a dense range or an idempotent bounded endomorphism of the corresponding Banach algebra.
Article
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The notion of σ-amenability for Banach algebras and its related notions were introduced and extensively studied by M.S. Moslehian and A.N. Motlagh in [10]. We develop these notions parallel to the amenability of Banach algebras introduced by B.E. Johnson in [5]. Briefly, we investigate σ-contractibility and σ-biprojectivity of Banach algebras, whic...
Article
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In this study, we introduce module approximate amenability. Indeed, we extend the concept of approximate amenability of Banach algebra A to the case that there is an extra U-module structure on A and we show that l <sup>1</sup>(S) is module approximately amenable if and only if S is amenable, where, S is an inverse semigroup with subsemigroup E of...
Article
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We introduce new notions of approximate amenability for a Banach algebra A. A Banach algebra A is n-approximately weakly amenable, for n ∈ N, if every continuous derivation from A into the n-th dual space A<sup>(n)</sup> is approximately inner. First we examine the relation between m-approximately weak amenability and n-approximately weak amenabili...
Article
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We introduce two notions of amenability for a Banach algebra A. LetI be a closed two-sided ideal inA, we sayA is I-weakly amenable if the first cohomology group ofA with coefficients in the dual space I* is zero; i.e.,H 1(A, I*) = {0}, and,A is ideally amenable ifA isI-weakly amenable for every closed two-sided idealI inA. We relate these concepts...
Article
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In this paper we extend the notion of n-weak amenability of a Banach algebra \(A\) when n ∈ ℕ. Technical calculations show that when \(A\) is Arens regular or an ideal in \(A\)**, then \(A\)* is an \(A\) (2n)-module and this idea leads to a number of interesting results on Banach algebras. We then extend the concept of n-weak amenability to n ∈ ∕.
Article
Full-text available
Let A be a Banach algebra, we say that A has the strongly double limit property (SDLP) if for each bounded net (a_\alpha) in A and each bounded net $(a^{\ast}\;_\beta)\;in\;A^{\ast},\;lim_\alpha\;lim_\beta=lim_\beta\;lim_\alpha$ whenever both iterated limits exist. In this paper among other results we show that if A has the SDLP and A^{\ast\ast} is...

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Project
ISEDS at Persian Gulf University (PGU) has pioneered many of the tools and ideas behind the research and applications often classified as "intelligent systems" and “data science,” where computer science, electrical engineering, statistics, and mathematics join together. This Faculty sees an even brighter future for data science as it harnesses a wider set of ideas to build a new more subtle and powerful science of data. As well as being interested in prediction and statistical computation, our Faculty puts equal weight on designing experiments, modeling sophisticated dependencies (networks, data streams), and trying to understand and quantify causal mechanisms, not simply averages and associations, with large data sets. These views are reflected in our curriculum targeted to data science specialists, our faculty’s research, and the work of our research students.