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  2. Bairstow's method - Wikipedia

    en.wikipedia.org/wiki/Bairstow's_method

    Points are colored according to the final point of the Bairstow iteration, black points indicate divergent behavior. The first image is a demonstration of the single real root case. The second indicates that one can remedy the divergent behavior by introducing an additional real root, at the cost of slowing down the speed of convergence.

  3. Graeffe's method - Wikipedia

    en.wikipedia.org/wiki/Graeffe's_method

    Graeffe's method works best for polynomials with simple real roots, though it can be adapted for polynomials with complex roots and coefficients, and roots with higher multiplicity. For instance, it has been observed [ 2 ] that for a root x ℓ + 1 = x ℓ + 2 = ⋯ = x ℓ + d {\displaystyle x_{\ell +1}=x_{\ell +2}=\dots =x_{\ell +d}} with ...

  4. Laguerre's method - Wikipedia

    en.wikipedia.org/wiki/Laguerre's_method

    Laguerre's method may even converge to a complex root of the polynomial, because the radicand of the square root may be of a negative number, in the formula for the correction, , given above – manageable so long as complex numbers can be conveniently accommodated for the calculation. This may be considered an advantage or a liability ...

  5. Neville's algorithm - Wikipedia

    en.wikipedia.org/wiki/Neville's_algorithm

    Given a set of n+1 data points (x i, y i) where no two x i are the same, the interpolating polynomial is the polynomial p of degree at most n with the property p(x i) = y i for all i = 0,...,n. This polynomial exists and it is unique. Neville's algorithm evaluates the polynomial at some point x.

  6. Polynomial interpolation - Wikipedia

    en.wikipedia.org/wiki/Polynomial_interpolation

    Choosing the points of intersection as interpolation nodes we obtain the interpolating polynomial coinciding with the best approximation polynomial. The defect of this method, however, is that interpolation nodes should be calculated anew for each new function f ( x ), but the algorithm is hard to be implemented numerically.

  7. Root-finding algorithm - Wikipedia

    en.wikipedia.org/wiki/Root-finding_algorithm

    Graeffe's method – Algorithm for finding polynomial roots; Lill's method – Graphical method for the real roots of a polynomial; MPSolve – Software for approximating the roots of a polynomial with arbitrarily high precision; Multiplicity (mathematics) – Number of times an object must be counted for making true a general formula

  8. Hermite interpolation - Wikipedia

    en.wikipedia.org/wiki/Hermite_interpolation

    In numerical analysis, Hermite interpolation, named after Charles Hermite, is a method of polynomial interpolation, which generalizes Lagrange interpolation.Lagrange interpolation allows computing a polynomial of degree less than n that takes the same value at n given points as a given function.

  9. Collocation method - Wikipedia

    en.wikipedia.org/wiki/Collocation_method

    In mathematics, a collocation method is a method for the numerical solution of ordinary differential equations, partial differential equations and integral equations.The idea is to choose a finite-dimensional space of candidate solutions (usually polynomials up to a certain degree) and a number of points in the domain (called collocation points), and to select that solution which satisfies the ...