Univariate Function Decomposition via Tridiagonal Vector Enhanced Multivariance Products Representation (TVEMPR)

Gürvit, Ercan; Baykara, N. A.; Demіralp, Metin

doi:10.1109/mcsi.2014.24

Cited by 4 publications

(3 citation statements)

References 14 publications

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“…The presently proposed method has also an application to develop a univariate function decomposition method [26] will also appear as another contributed paper of this conference.…”

Section: Discussionmentioning

confidence: 97%

“…Each element of the matrix is assumed to be real-valued. (26) is just a single matrix equation while it contains four unknown entities one of which is a matrix with same type with the target matrix. The scalar and vector EMPR components involves extra unknown scalar elements.…”

Section: Tmempr Of An Outer Productmentioning

confidence: 99%

“…On the other hand, (26) can be postmultiplied by the vector v to enable us to get the following result for a 1…”

Section: Tmempr Of An Outer Productmentioning

confidence: 99%

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Infinite Vector Decomposition in Tridiagonal Matrix Enhanced Multivariance Products Representation (TMEMPR) Perspective

Baykara

Demіralp

2014

2014 International Conference on Mathematics and Computers in Sciences and in Industry

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In this work a new version of Enhanced Multivariance Products Representation (EMPR) is taken into consideration. Recent researches on the bivariate arrays (i.e, matrices) have led us to a new scheme which we have called Tridiagonal Matrix Enhanced Multivariate Products Representation (TMEMPR). Therein we have been consecutively using four term EMPR on its bivariate component under different support functions such that the remainder was becoming to have less rank as we proceed until no bivariate component remains. Here however, we focus on denumerably infinite vectors and first appropriately fold them to semi infinite matrices with finite number of denumerable infinite rows, then decompose the resulting infinite matrices via TMEMPR, and at the final stage we unfold each additive term of the representation via unique inversion of the folding procedure we use.

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“…The presently proposed method has also an application to develop a univariate function decomposition method [26] will also appear as another contributed paper of this conference.…”

Section: Discussionmentioning

confidence: 97%

Section: Tmempr Of An Outer Productmentioning

confidence: 99%