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[1] [7]: 620 A sequence () that converges to is said to converge at least R-linearly if there exists an error-bounding sequence () such that | | and () converges Q-linearly to zero; analogous definitions hold for R-superlinear convergence, R-sublinear convergence, R-quadratic convergence, and so on.
The following iterates are 1.0103, 1.00093, 1.0000082, and 1.00000000065, illustrating quadratic convergence. This highlights that quadratic convergence of a Newton iteration does not mean that only few iterates are required; this only applies once the sequence of iterates is sufficiently close to the root. [16]
Polynomial contrasts are a special set of orthogonal contrasts that test polynomial patterns in data with more than two means (e.g., linear, quadratic, cubic, quartic, etc.). [9] Orthonormal contrasts are orthogonal contrasts which satisfy the additional condition that, for each contrast, the sum squares of the coefficients add up to one. [7]
In mathematics (including combinatorics, linear algebra, and dynamical systems), a linear recurrence with constant coefficients [1]: ch. 17 [2]: ch. 10 (also known as a linear recurrence relation or linear difference equation) sets equal to 0 a polynomial that is linear in the various iterates of a variable—that is, in the values of the elements of a sequence.
Convergence is quadratic for well-behaved functions—if the test points are within of the correct result, they will be approximately within of the correct result after the next round. Remez's algorithm is typically started by choosing the extrema of the Chebyshev polynomial T N + 1 {\displaystyle T_{N+1}} as the initial points, since the final ...
In numerical analysis, a quasi-Newton method is an iterative numerical method used either to find zeroes or to find local maxima and minima of functions via an iterative recurrence formula much like the one for Newton's method, except using approximations of the derivatives of the functions in place of exact derivatives.
If we use a polynomial fit to remove the quadratic part of the finite difference used in the secant method, so that it better approximates the derivative, we obtain Steffensen's method, which has quadratic convergence, and whose behavior (both good and bad) is essentially the same as Newton's method but does not require a derivative.
Bairstow's algorithm inherits the local quadratic convergence of Newton's method, except in the case of quadratic factors of multiplicity higher than 1, when convergence to that factor is linear. A particular kind of instability is observed when the polynomial has odd degree and only one real root.
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