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  2. Newton's method - Wikipedia

    en.wikipedia.org/wiki/Newton's_method

    This x-intercept will typically be a better approximation to the original function's root than the first guess, and the method can be iterated. x n+1 is a better approximation than x n for the root x of the function f (blue curve) If the tangent line to the curve f(x) at x = x n intercepts the x-axis at x n+1 then the slope is

  3. List of trigonometric identities - Wikipedia

    en.wikipedia.org/wiki/List_of_trigonometric...

    The Chebyshev method is a recursive algorithm for finding the n th multiple angle formula knowing the ...

  4. Polynomial root-finding - Wikipedia

    en.wikipedia.org/wiki/Polynomial_root-finding

    For finding one root, Newton's method and other general iterative methods work generally well. For finding all the roots, arguably the most reliable method is the Francis QR algorithm computing the eigenvalues of the companion matrix corresponding to the polynomial, implemented as the standard method [1] in MATLAB.

  5. Remez algorithm - Wikipedia

    en.wikipedia.org/wiki/Remez_algorithm

    The Remez algorithm or Remez exchange algorithm, published by Evgeny Yakovlevich Remez in 1934, is an iterative algorithm used to find simple approximations to functions, specifically, approximations by functions in a Chebyshev space that are the best in the uniform norm L ∞ sense. [1] It is sometimes referred to as Remes algorithm or Reme ...

  6. Calculus - Wikipedia

    en.wikipedia.org/wiki/Calculus

    The sum of all such rectangles gives an approximation of the area between the axis and the curve, which is an approximation of the total distance traveled. A smaller value for Δx will give more rectangles and in most cases a better approximation, but for an exact answer, we need to take a limit as Δx approaches zero. [47]: 512–522

  7. Taylor series - Wikipedia

    en.wikipedia.org/wiki/Taylor_series

    The Taylor polynomials for ln(1 + x) only provide accurate approximations in the range −1 < x ≤ 1. For x > 1, Taylor polynomials of higher degree provide worse approximations. The Taylor approximations for ln(1 + x) (black). For x > 1, the approximations diverge. Pictured is an accurate approximation of sin x around the point x = 0. The ...

  8. Runge–Kutta methods - Wikipedia

    en.wikipedia.org/wiki/Runge–Kutta_methods

    The stability function of an explicit Runge–Kutta method is a polynomial, so explicit Runge–Kutta methods can never be A-stable. [32] If the method has order p, then the stability function satisfies () = + (+) as . Thus, it is of interest to study quotients of polynomials of given degrees that approximate the exponential function the best.

  9. LU decomposition - Wikipedia

    en.wikipedia.org/wiki/LU_decomposition

    By Banachiewicz (1938, hereafter B38) LU decomposition method calculates such! triangles L=G^T, and U=H that square B=A^T=G^TH=LU. Partial pivoting ! by column permutation IP(:) is modern addition. ! Within the code a, g correspond to B38 A^T and G^T, so that a=gh holds. ! !