Geometric properties of a novel type of orthogonality via norm derivatives
Kallal Pal, Sumit Chandok
Abstract
Open-access reader
Kallal Pal, Sumit Chandok
Abstract
Open-access reader
In this article, we generalize the notion of orthogonality as a linear combination of norm derivatives in order to give a novel concept that we refer to as $ρ_{α,β}$-orthogonality. Also, we discuss some of its geometric properties in a real normed linear space and present some sufficient criteria for the smoothness of a normed space by using $ρ_{α,β}$-orthogonality. We provide a few examples to show that the $ρ_{α,β}$- orthogonality cannot be compared to other well-known orthogonalities in any way. In addition to this, we offer a characterization of inner product spaces by making use of the functional notation $ρ_{α,β}$. In addition, we show that any $ρ_{α,β}$-orthogonality that preserves linear mapping between two normed linear spaces must necessarily be a scalar multiple of an isometry. Also, using the $ρ_{α,β}$-functional, we define the idea of an angle between two vectors and talk about their characteristics in normed spaces.
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In this article, we generalize the notion of orthogonality as a linear combination of norm derivatives in order to give a novel concept that we refer to as $ρ_{α,β}$-orthogonality. Also, we discuss some of its geometric properties in a real normed linear space and present some sufficient criteria for the smoothness of a normed space by using $ρ_{α,β}$-orthogonality. We provide a few examples to show that the $ρ_{α,β}$- orthogonality cannot be compared to other well-known orthogonalities in any way. In addition to this, we offer a characterization of inner product spaces by making use of the functional notation $ρ_{α,β}$. In addition, we show that any $ρ_{α,β}$-orthogonality that preserves linear mapping between two normed linear spaces must necessarily be a scalar multiple of an isometry. Also, using the $ρ_{α,β}$-functional, we define the idea of an angle between two vectors and talk about their characteristics in normed spaces.
Key concepts: Orthogonality, Normed vector space, Inner product space, Mathematics, Norm (philosophy), Scalar (mathematics), Alpha (finance), BETA (programming language)