On the Convergence of Orbits in Sequence Space l^2

This paper discusses the convergence of orbits for diagonal operators defined on . In particular, the basis elements of are obtained using the linear combinations of the elements of the orbit. Furthermore, via the classical result of the determinant of the Vandermonde matrix, it is shown that, the more the elements of the orbit are used, the faster the convergence of the orbit to the basis elements of . Keywords: Diagonal operators; Convergence of Orbits of operators; Vandermonde matrix; Norm topology

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The location of critical points of polynomials

Asian-European Journal of Mathematics ◽

10.1142/s1793557119500876 ◽

2019 ◽

Vol 12 (07) ◽

pp. 1950087

Author(s):

Suhail Gulzar ◽

N. A. Rather ◽

F. A. Bhat

Keyword(s):

Complex Plane ◽

Simple Proof ◽

Critical Points ◽

Classical Result ◽

Zeros Of Polynomials ◽

Linear Combinations ◽

Incomplete Polynomials ◽

Location Of Zeros ◽

Set Of Points

Given a set of points in the complex plane, an incomplete polynomial is defined as one which has these points as zeros except one of them. Recently, the classical result known as Gauss–Lucas theorem on the location of zeros of polynomials and their derivatives was extended to the linear combinations of incomplete polynomials. In this paper, a simple proof of this result is given, and some results concerning the critical points of polynomials due to Jensen and others have extended the linear combinations of incomplete polynomials.

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The visuospatial imagery of sequence-space synaesthesia

PsycEXTRA Dataset ◽

10.1037/e512592013-262 ◽

2011 ◽

Author(s):

M. Price

Keyword(s):

Sequence Space

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Faculty Opinions recommendation of Alternative evolutionary histories in the sequence space of an ancient protein.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.731118711.793539563 ◽

2017 ◽

Author(s):

Reinhard Sterner ◽

Rainer Merkl

Keyword(s):

Sequence Space

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Faculty Opinions recommendation of Engineering orthogonal signalling pathways reveals the sparse occupancy of sequence space.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.736799165.793567477 ◽

2019 ◽

Author(s):

Luciano Marraffini ◽

Alexander Meeske

Keyword(s):

Sequence Space ◽

Signalling Pathways

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On the Characterization of Nonnegatively Estimable Linear Combinations of Variance Components.

10.21236/ada153786 ◽

1985 ◽

Author(s):

T. Mathew

Keyword(s):

Variance Components ◽

Linear Combinations

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On the domain of Riesz mean in the space Ls

Filomat ◽

10.2298/fil1704925y ◽

2017 ◽

Vol 31 (4) ◽

pp. 925-940 ◽

Cited By ~ 12

Author(s):

Medine Yeşilkayagil ◽

Feyzi Başar

Keyword(s):

Banach Space ◽

Sequence Space ◽

Double Sequence ◽

Sufficient Conditions ◽

Necessary And Sufficient Conditions ◽

Double Sequences ◽

Riesz Mean ◽

Double Sequence Space ◽

Necessary And Sufficient ◽

Barrelled Space

Let 0 < s < ?. In this study, we introduce the double sequence space Rqt(Ls) as the domain of four dimensional Riesz mean Rqt in the space Ls of absolutely s-summable double sequences. Furthermore, we show that Rqt(Ls) is a Banach space and a barrelled space for 1 ? s < 1 and is not a barrelled space for 0 < s < 1. We determine the ?- and ?(?)-duals of the space Ls for 0 < s ? 1 and ?(bp)-dual of the space Rqt(Ls) for 1 < s < 1, where ? ? {p, bp, r}. Finally, we characterize the classes (Ls:Mu), (Ls:Cbp), (Rqt(Ls) : Mu) and (Rqt(Ls):Cbp) of four dimensional matrices in the cases both 0 < s < 1 and 1 ? s < 1 together with corollaries some of them give the necessary and sufficient conditions on a four dimensional matrix in order to transform a Riesz double sequence space into another Riesz double sequence space.

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Descriptive statistics

10.1093/oso/9780198785699.003.0003 ◽

2017 ◽

Author(s):

Andrew Gelman ◽

Deborah Nolan

Keyword(s):

Descriptive Statistics ◽

Metabolic Rates ◽

World Record ◽

Linear Combinations ◽

Record Times ◽

Starting Point ◽

Statistics Course ◽

Scatter Plots ◽

Economic Indexes ◽

The Mean

Descriptive statistics is the typical starting point for a statistics course, and it can be tricky to teach because the material is more difficult than it first appears. The activities in this chapter focus more on the topics of data displays and transformations, rather than the mean, median, and standard deviation, which are covered easily in a textbook and on homework assignments. Specific topics include: distributions and handedness scores; extrapolation of time series and world record times for the mile run; linear combinations and economic indexes; scatter plots and exam scores; and logarithmic transformations and metabolic rates.

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