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Ekrem Savaş - One of the best experts on this subject based on the ideXlab platform.
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On general summability factor theorem of infinite series
Applied Mathematics and Computation, 2011Co-Authors: Hamdullah Şevli, Ekrem SavaşAbstract:Abstract A Lower Triangular Matrix with nonzero principal diagonal entries is called a triangle. In this paper we obtain the sufficient conditions for ∑ a n λ n to be summable ∣ A ∣ k whenever ∑ a n is summable ∣ T ∣ k for a triangle T .
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A summability factor theorem for quasi-power-increasing sequences.
Journal of Inequalities and Applications, 2010Co-Authors: Ekrem SavaşAbstract:We establish a summability factor theorem for summability , where is Lower Triangular Matrix with nonnegative entries satisfying certain conditions. This paper is an extension of the main result of the work by Rhoades and Savas (2006) by using quasi -increasing sequences.
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A GENERALIZED SUMMABILITY FACTOR THEOREM FOR ABSOLUTE SUMMABILITY AND QUASI POWER INCREASING SEQUENCES
2009Co-Authors: Ekrem SavaşAbstract:The object of this paper is to establish a summability factor theorem for summability 1 , , k A k where A is the Lower Triangular Matrix with non-negative entries satisfying certain conditions.
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A Note on Open image in new window Summability Factors for Infinite Series
2007Co-Authors: Ekrem Savaş, Billy E. RhoadesAbstract:We obtain sufficient conditions on a nonnegative Lower Triangular Matrix Open image in new window and a sequence Open image in new window for the series Open image in new window to be absolutely summable of order Open image in new window by Open image in new window .
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A summability factor theorem involving an almost increasing sequence for generalized absolute summability
Computers & Mathematics With Applications, 2007Co-Authors: Ekrem SavaşAbstract:In this paper we prove a general theorem on |A;@d|"k-summability factors of infinite series under suitable conditions by using an almost increasing sequence, where A is a Lower Triangular Matrix with non-negative entries. Also, we deduce a similar result for the weighted mean method.
P. D. Srivastava - One of the best experts on this subject based on the ideXlab platform.
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Spectra of the Lower Triangular Matrix $\mathbb{B}(r_1,\dots , r_l; s_1, \dots, s_{l'})$ over $c_0$
arXiv: Functional Analysis, 2018Co-Authors: Sanjay Kumar Mahto, Arnab Patra, P. D. SrivastavaAbstract:The spectra and fine spectra of the Lower Triangular Matrix $\mathbb{B}$ $(r_1,\dots , r_l;$ $ s_1, \dots, s_{l'})$ over the sequence space $c_0$ are determined. The diagonal and sub-diagonal entries of the Matrix consist of two oscillatory sequences $r=(r_{k (\text{mod} \ l)+1})$ and $s= (s_{k(\text{mod} \ l')+1})$ respectively, whereas the rest of the entries of the Matrix are zero. In particular, the spectra and fine spectra of the Lower Triangular Matrix $\mathbb{B}(r_1,\dots , r_4; s_1, \dots, s_{6})$ over $c_0$ are discussed.
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spectra of the Lower Triangular Matrix mathbb b r_1 dots r_l s_1 dots s_ l over c_0
arXiv: Functional Analysis, 2018Co-Authors: Sanjay Kumar Mahto, Arnab Patra, P. D. SrivastavaAbstract:The spectra and fine spectra of the Lower Triangular Matrix $\mathbb{B}$ $(r_1,\dots , r_l;$ $ s_1, \dots, s_{l'})$ over the sequence space $c_0$ are determined. The diagonal and sub-diagonal entries of the Matrix consist of two oscillatory sequences $r=(r_{k (\text{mod} \ l)+1})$ and $s= (s_{k(\text{mod} \ l')+1})$ respectively, whereas the rest of the entries of the Matrix are zero. In particular, the spectra and fine spectra of the Lower Triangular Matrix $\mathbb{B}(r_1,\dots , r_4; s_1, \dots, s_{6})$ over $c_0$ are discussed.
Sanjay Kumar Mahto - One of the best experts on this subject based on the ideXlab platform.
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Spectra of the Lower Triangular Matrix $\mathbb{B}(r_1,\dots , r_l; s_1, \dots, s_{l'})$ over $c_0$
arXiv: Functional Analysis, 2018Co-Authors: Sanjay Kumar Mahto, Arnab Patra, P. D. SrivastavaAbstract:The spectra and fine spectra of the Lower Triangular Matrix $\mathbb{B}$ $(r_1,\dots , r_l;$ $ s_1, \dots, s_{l'})$ over the sequence space $c_0$ are determined. The diagonal and sub-diagonal entries of the Matrix consist of two oscillatory sequences $r=(r_{k (\text{mod} \ l)+1})$ and $s= (s_{k(\text{mod} \ l')+1})$ respectively, whereas the rest of the entries of the Matrix are zero. In particular, the spectra and fine spectra of the Lower Triangular Matrix $\mathbb{B}(r_1,\dots , r_4; s_1, \dots, s_{6})$ over $c_0$ are discussed.
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spectra of the Lower Triangular Matrix mathbb b r_1 dots r_l s_1 dots s_ l over c_0
arXiv: Functional Analysis, 2018Co-Authors: Sanjay Kumar Mahto, Arnab Patra, P. D. SrivastavaAbstract:The spectra and fine spectra of the Lower Triangular Matrix $\mathbb{B}$ $(r_1,\dots , r_l;$ $ s_1, \dots, s_{l'})$ over the sequence space $c_0$ are determined. The diagonal and sub-diagonal entries of the Matrix consist of two oscillatory sequences $r=(r_{k (\text{mod} \ l)+1})$ and $s= (s_{k(\text{mod} \ l')+1})$ respectively, whereas the rest of the entries of the Matrix are zero. In particular, the spectra and fine spectra of the Lower Triangular Matrix $\mathbb{B}(r_1,\dots , r_4; s_1, \dots, s_{6})$ over $c_0$ are discussed.
Arnab Patra - One of the best experts on this subject based on the ideXlab platform.
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Spectra of the Lower Triangular Matrix $\mathbb{B}(r_1,\dots , r_l; s_1, \dots, s_{l'})$ over $c_0$
arXiv: Functional Analysis, 2018Co-Authors: Sanjay Kumar Mahto, Arnab Patra, P. D. SrivastavaAbstract:The spectra and fine spectra of the Lower Triangular Matrix $\mathbb{B}$ $(r_1,\dots , r_l;$ $ s_1, \dots, s_{l'})$ over the sequence space $c_0$ are determined. The diagonal and sub-diagonal entries of the Matrix consist of two oscillatory sequences $r=(r_{k (\text{mod} \ l)+1})$ and $s= (s_{k(\text{mod} \ l')+1})$ respectively, whereas the rest of the entries of the Matrix are zero. In particular, the spectra and fine spectra of the Lower Triangular Matrix $\mathbb{B}(r_1,\dots , r_4; s_1, \dots, s_{6})$ over $c_0$ are discussed.
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spectra of the Lower Triangular Matrix mathbb b r_1 dots r_l s_1 dots s_ l over c_0
arXiv: Functional Analysis, 2018Co-Authors: Sanjay Kumar Mahto, Arnab Patra, P. D. SrivastavaAbstract:The spectra and fine spectra of the Lower Triangular Matrix $\mathbb{B}$ $(r_1,\dots , r_l;$ $ s_1, \dots, s_{l'})$ over the sequence space $c_0$ are determined. The diagonal and sub-diagonal entries of the Matrix consist of two oscillatory sequences $r=(r_{k (\text{mod} \ l)+1})$ and $s= (s_{k(\text{mod} \ l')+1})$ respectively, whereas the rest of the entries of the Matrix are zero. In particular, the spectra and fine spectra of the Lower Triangular Matrix $\mathbb{B}(r_1,\dots , r_4; s_1, \dots, s_{6})$ over $c_0$ are discussed.
Ayse Kiper - One of the best experts on this subject based on the ideXlab platform.
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an efficient parallel Triangular inversion by gauss elimination with sweeping
European Conference on Parallel Processing, 1998Co-Authors: Ayse KiperAbstract:A parallel computation model to invert a Lower Triangular Matrix using Gauss elimination with sweeping technique is presented. Performance characteristics that we obtain are O(n) time and O(n 2) processors leading to an efficiency of O(1/n). A comparative performance study with the available fastest parallel Matrix inversion algorithms is given. We believe that the method presented here is superior over the existing methods in efficiency measure and in processor complexity.
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Euro-Par - An Efficient Parallel Triangular Inversion by Gauss Elimination with Sweeping
Euro-Par’98 Parallel Processing, 1998Co-Authors: Ayse KiperAbstract:A parallel computation model to invert a Lower Triangular Matrix using Gauss elimination with sweeping technique is presented. Performance characteristics that we obtain are O(n) time and O(n 2) processors leading to an efficiency of O(1/n). A comparative performance study with the available fastest parallel Matrix inversion algorithms is given. We believe that the method presented here is superior over the existing methods in efficiency measure and in processor complexity.
