"Lanczos approximation"의 두 판 사이의 차이

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* ID :  [https://www.wikidata.org/wiki/Q6483801 Q6483801]
 
* ID :  [https://www.wikidata.org/wiki/Q6483801 Q6483801]
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===Spacy 패턴 목록===
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* [{'LOWER': 'lanczos'}, {'LEMMA': 'approximation'}]

2021년 2월 17일 (수) 00:48 기준 최신판

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말뭉치

  1. The Lanczos approximation is a particularly effective method for computing values of the Gamma function to high precision.[1]
  2. To facilitate an elegant C++ implementation of the Lanczos approximation, I have written a partial C++ wrapper for the floating point functions of GMP.[1]
  3. The Lanczos approximation has fewer of these problems, provided that the coefficients are computed accurately enough.[2]
  4. The Lanczos Approximation as implemented in the Boost C++ Libraries.[2]
  5. We report that for fixed real parts of the free parameter that using complex coefficients both increases the computational cost of the Lanczos approximation while drecreasing the accuracy...[3]
  6. In mathematics, the Lanczos approximation is a method for computing the Gamma function numerically, published by Cornelius Lanczos in 1964.[4]
  7. For a well-chosen constant g and a small number of terms in the series, the Lanczos approximation is accurate to within floating-point precision.[5]
  8. In mathematics, the Lanczos approximation is a method for computing the gamma function numerically, published by Cornelius Lanczos in 1964.[6]
  9. Among others, the Padé-via-Lanczos approximation provides an efficient solution.[7]
  10. The Lanczos approximation is used to calculate Gamma function numerically.[8]
  11. The Lanczos approximation is a method for computing the Gamma function numerically, published by Cornelius Lanczos in 1964.[8]
  12. Using Lanczos Approximation.[9]
  13. In this paper, we use the idea of thick-restart and adaptive preconditioning for solving linear systems to improve convergence of the Lanczos approximation.[10]
  14. 𝑣 ℓ + 1 for the Lanczos approximation, so that the vectors 𝑟 ℓ ≡ 𝑟 ℓ ( 0 ) and 𝑟 ℓ ( 𝑡 ) are aligned; however their magnitudes are different.[10]
  15. It is also worth pointing out that the Lanczos approximation for this preconditioned matrix function reduces much more rapidly than for the case where preconditioning (dotted line) is not used.[10]
  16. The Lanczos approximation in this example again lies almost entirely on the curve that represents the optimal approximation obtainable from the Krylov subspace.[10]

소스

  1. ↑ 1.0 1.1 The Lanczos approximation of the Gamma function
  2. ↑ 2.0 2.1 Coefficients for the Lanczos Approximation to the Gamma Function at MROB
  3. ↑ The Lanczos Approximation for the $\Gamma$-Function with Complex Coefficients
  4. ↑ JavaScript Math: Calculate Lanczos approximation gamma
  5. ↑ jcboyd/study-no-1: An implementation of the Lanczos approximation in C with Python wrapper
  6. ↑ Lanczos approximation
  7. ↑ EFFICIENT SOUND POWER COMPUTATION OF OPEN STRUCTURES WITH INFINITE/FINITE ELEMENTS AND BY MEANS OF THE PADÉ-VIA-LANCZOS ALGORITHM
  8. ↑ 8.0 8.1 Online calculator: Gamma function
  9. ↑ (PDF) Julia and Mandelbrot Sets of the Gamma Function Using Lanczos Approximation
  10. ↑ 10.0 10.1 10.2 10.3 A Numerical Solution Using an Adaptively Preconditioned Lanczos Method for a Class of Linear Systems Related with the Fractional Poisson Equation

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Spacy 패턴 목록

  • [{'LOWER': 'lanczos'}, {'LEMMA': 'approximation'}]
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