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  2. Numerical error - Wikipedia

    en.wikipedia.org/wiki/Numerical_error

    Time series of the Tent map for the parameter m=2.0 which shows numerical error: "the plot of time series (plot of x variable with respect to number of iterations) stops fluctuating and no values are observed after n=50". Parameter m= 2.0, initial point is random.

  3. Round-off error - Wikipedia

    en.wikipedia.org/wiki/Round-off_error

    In computing, a roundoff error, [1] also called rounding error, [2] is the difference between the result produced by a given algorithm using exact arithmetic and the result produced by the same algorithm using finite-precision, rounded arithmetic. [3]

  4. Lagrange polynomial - Wikipedia

    en.wikipedia.org/wiki/Lagrange_polynomial

    "Lagrange interpolation formula", Encyclopedia of Mathematics, EMS Press, 2001 [1994] ALGLIB has an implementations in C++ / C# / VBA / Pascal. GSL has a polynomial interpolation code in C; SO has a MATLAB example that demonstrates the algorithm and recreates the first image in this article

  5. Truncation error - Wikipedia

    en.wikipedia.org/wiki/Truncation_error

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  6. Chebyshev nodes - Wikipedia

    en.wikipedia.org/wiki/Chebyshev_nodes

    Chebyshev nodes of both kinds from = to =.. For a given positive integer the Chebyshev nodes of the first kind in the open interval (,) are = ⁡ (+), =, …,. These are the roots of the Chebyshev polynomials of the first kind with degree .

  7. Catastrophic cancellation - Wikipedia

    en.wikipedia.org/wiki/Catastrophic_cancellation

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  8. Regula falsi - Wikipedia

    en.wikipedia.org/wiki/Regula_falsi

    The red curve shows the function f and the blue lines are the secants. The convergence rate of the bisection method could possibly be improved by using a different solution estimate. The regula falsi method calculates the new solution estimate as the x-intercept of the line segment joining the endpoints of the function on the current bracketing ...

  9. Stirling's approximation - Wikipedia

    en.wikipedia.org/wiki/Stirling's_approximation

    The full formula, together with precise estimates of its error, can be derived as follows. Instead of approximating n ! {\displaystyle n!} , one considers its natural logarithm , as this is a slowly varying function : ln ⁡ ( n !