Numerical Approximation Algorithm

A Numerical Approximation Algorithm is an Approximation Algorithm that can solve a Numerical Approximation Task.

• AKA: Numerical Analysis Method.
• Context:
  • It can be applied by a Numerical Approximation System, such as a Numerical Approximation Library.
  • It can be represented in a Numerical Approximation Approximation Recipe.
  • ...
• Example(s):
  • an Estimation Algorithm.
  • a Linear Programming Algorithm.
  • a Function Fitting Algorithm.
  • an Integral Approximation Algorithm.
  • …
• Counter-Example(s):
  • a Symbolic Computation Algorithm, such as a Theorem Checking Algorithm or an Equation Solving Algorithm.
• See: Numerical Approximation, Ordinary Differential Equation.

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References

• http://en.wikipedia.org/wiki/List_of_numerical_analysis_topics

2014

• (Wikipedia, 2014) ⇒ http://en.wikipedia.org/wiki/numerical_analysis Retrieved:2014-5-17.
  • Numerical analysis is the study of algorithms that use numerical approximation (as opposed to general symbolic manipulations) for the problems of mathematical analysis (as distinguished from discrete mathematics).

    One of the earliest mathematical writings is a Babylonian tablet from the Yale Babylonian Collection (YBC 7289), which gives a sexagesimal numerical approximation of [math]\displaystyle{ \sqrt{2} }[/math], the length of the diagonal in a unit square. Being able to compute the sides of a triangle (and hence, being able to compute square roots) is extremely important, for instance, in astronomy, carpentry and construction.

    Numerical analysis continues this long tradition of practical mathematical calculations. Much like the Babylonian approximation of [math]\displaystyle{ \sqrt{2} }[/math], modern numerical analysis does not seek exact answers, because exact answers are often impossible to obtain in practice. Instead, much of numerical analysis is concerned with obtaining approximate solutions while maintaining reasonable bounds on errors.

    Numerical analysis naturally finds applications in all fields of engineering and the physical sciences, but in the 21st century also the life sciences and even the arts have adopted elements of scientific computations. Ordinary differential equations appear in celestial mechanics (planets, stars and galaxies); numerical linear algebra is important for data analysis; stochastic differential equations and Markov chains are essential in simulating living cells for medicine and biology.

    Before the advent of modern computers numerical methods often depended on hand interpolation in large printed tables. Since the mid 20th century, computers calculate the required functions instead. These same interpolation formulas nevertheless continue to be used as part of the software algorithms for solving differential equations.

1988

• (Press et al., 1988) ⇒ William H. Press, William T. Vetterling, Saul A . Teukolsky, and Brian P. Flannery. (1988). “Numerical Recipes.” Cambridge University Press.
  • NOTES: Numerical Analysis Recipe Book.

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