2003
DOI: 10.1090/gsm/057
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Concise Numerical Mathematics

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Cited by 41 publications
(60 citation statements)
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“…For this, first the residual being calculated in step one is removed, so in order to run the algorithm, = 0 is taken as a fixed point. In this step algorithm converges to a fixed point following the fixed point theorem ( [15], pp.109-111). However as stated earlier the algorithm gives the worst estimation in terms of variance at this point.…”
Section: Minimum Variance Shift Methodsmentioning
confidence: 99%
“…For this, first the residual being calculated in step one is removed, so in order to run the algorithm, = 0 is taken as a fixed point. In this step algorithm converges to a fixed point following the fixed point theorem ( [15], pp.109-111). However as stated earlier the algorithm gives the worst estimation in terms of variance at this point.…”
Section: Minimum Variance Shift Methodsmentioning
confidence: 99%
“…It should be pointed out that for arbitrary collocation parameters {c i }, the precision of the interpolatory m-point quadrature formula over [0, 1] is m − 1 generally. But if the orthogonality condition (3.20) holds, then the corresponding precision will attain m at least (see [20]). Using the assumed regularity of the given functions, the orthogonality condition of the collocation points, and the fact that For the second term I 2 , we set…”
Section: Optimal Global Superconvergence Order Of U Itmentioning
confidence: 99%
“…The standard error representation for polynomial interpolation (see e.g., [18]) gives |P(0) − ψ(0)| ≤ max 0≤x≤2h |ψ (x)|h 2 . In addition, an expansion of P ε (0) − P(0) in terms of Lagrange basis polynomials L n ∈ 1 , n = 1, 2, with respect to the two grid points h, 2h gives |P ε (0) − P(0)| ≤ cε with c = |L 1 (0)| + |L 2 (0)| = 3.…”
Section: Lemmamentioning
confidence: 99%