Numerical Integration: Gaussian Quadrature

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Discussion Overview

The discussion revolves around the properties of Gaussian Quadrature, specifically focusing on why the sum of the weights in the numerical integration formula over the interval [-1, 1] equals 2. Participants explore the implications of symmetry in the weights and their relationship to the integration process.

Discussion Character

  • Technical explanation
  • Conceptual clarification

Main Points Raised

  • One participant questions why the sum of the weights \( \sum_{j} a_{j} = 2 \) in the Gaussian Quadrature formula.
  • Another participant suggests that the weights add up to 2 due to their symmetry about the midpoint of the integration range, noting that in a sampling of points, positive and negative weights balance out.
  • A third participant confirms the symmetry of the weights when graphed against their corresponding abscissas.
  • A participant references a historical source for further information on Gaussian Quadrature, indicating its continued relevance.

Areas of Agreement / Disagreement

Participants express different perspectives on the reasoning behind the sum of the weights, with some agreeing on the symmetry aspect while others focus on different interpretations. The discussion does not reach a consensus on a definitive explanation.

Contextual Notes

The discussion does not clarify specific assumptions about the weights or the conditions under which the sum equals 2, leaving some aspects unresolved.

Somefantastik
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\int^{1}_{-1}f(x)dx = \sum^{n}_{j=-n}a_{j}f(x_{j})

Why does \sum_{j}a_{j} = 2 ?

I know that the aj's are weights, and in the case of [-1,1], they are calculated using the roots of the Legendre polynomial, but I don't understand why they all add up to 2.
 
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I believe that they add up to 2 because they are symmetric about the midpoint of the integration range of [-1,1]. For instance if you used a sampling of 10 points (on the same interval of integration) 5 would be positive and 5 would be negative. If you summed up the 5 positive points you would you arrive at 2.0

Thanks
Matt
 
Last edited:
yeah when graphed out weights vs. abscissa, it looks like the attached photo, which is symmetric.
 

Attachments

  • Gaussian Weights 300.jpg
    Gaussian Weights 300.jpg
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For more information on Gaussian Quadrature see,

"Gaussian Quadrature Formulas" by Stroud and Secrest.

This was printed in 1966 but it is still accurate for today.

Thanks
Matt
 

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