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Mastering SciPy

You're reading from   Mastering SciPy Implement state-of-the-art techniques to visualize solutions to challenging problems in scientific computing, with the use of the SciPy stack

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Product type Paperback
Published in Nov 2015
Publisher
ISBN-13 9781783984749
Length 404 pages
Edition 1st Edition
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Authors (2):
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Francisco Javier Blanco-Silva Francisco Javier Blanco-Silva
Author Profile Icon Francisco Javier Blanco-Silva
Francisco Javier Blanco-Silva
Francisco Javier B Silva Francisco Javier B Silva
Author Profile Icon Francisco Javier B Silva
Francisco Javier B Silva
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Toc

Integration

To achieve a definite integration of functions on suitable domains, we have mainly two methods—Numerical integration and Symbolic integration.

Numerical integration refers to the approximation of a definite integral by a quadrature process. Depending on how the function f(x) is given, the domain of integration, the knowledge of its singularities, and the choice of quadrature, we have different ways to attack this problem:

  • For univariate polynomials, exact integration is achieved algebraically on each finite interval
  • For functions given as a finite set of samples over their domain:
    • The composite trapezoidal rule
    • Simpson's trapezoidal rules
    • Romberg integration scheme
  • For generic univariate functions given as Python functions, on finite intervals:
    • Fixed-order Gaussian quadrature
    • Fixed-tolerance Gaussian quadrature
    • Simple non-adaptive quadrature, by applying 21-, 43- and 87-point Gauss-Kronron rules
    • Simple adaptive quadrature, by subdivision and quadrature on each subinterval...
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