# Binary search with just one residual intervals

Newton-like methods with higher orders of convergence are the Householder's methods. In general, numerical algorithms are not guaranteed to find all the roots of a function, so failing to find a root does not prove that there is no root. Accelerated algorithms for multi-point evaluation and interpolation similar to the fast Fourier transform can binary search with just one residual intervals speed them up for large degrees of the polynomial. They generally use the intermediate value theoremwhich asserts that if a continuous function has values of opposite signs at the end points of an interval, then the function has at least one root in the interval.

From Wikipedia, the free encyclopedia. Many methods compute subsequent values by evaluating an auxiliary function on the preceding values. This consists in using the last computed approximate values of the root for approximating the function by a polynomial of low degree, which takes the same values at these approximate roots.

However, these will be reliably detected in the following transformation and refinement of the interval. Since the binary search with just one residual intervals must be stopped at some point these methods produce an approximation to the root, not an exact solution. The test based on Sturm chains is computationally more involved but gives always the exact number of real roots in the interval. This iterative scheme is numerically unstable; the approximation errors accumulate during the successive factorizations, so that the last roots are determined with a polynomial that deviates widely from a factor of the original polynomial.

If the given polynomial only has real coefficients, one may wish to avoid computations with complex numbers. Newton's method is also important because it readily generalizes to higher-dimensional problems. The Lehmer—Schur algorithm uses the Schur—Cohn test for circles, Wilf's global bisection algorithm uses a winding number computation for rectangular regions in the complex plane.

For example, many algorithms use the derivative of the input function, while others work on every continuous function. Although the bisection method is robust, it gains one and only one bit of accuracy with each iteration. This gives a faster convergence with a similar robustness.

Bracketing methods determine successively smaller intervals brackets that contain a root. Bairstow's method Jenkins—Traub method. CS1 French-language sources fr Wikipedia articles needing clarification from May All articles with vague or ambiguous time Vague or ambiguous time from February Pages using div col with deprecated parameters. See Budan's theorem for a description of the historical background of these methods.

In principle, one can use any eigenvalue algorithm to find the roots of the polynomial. The inverse power method with shifts, which finds some smallest root first, is what drives the complex cpoly variant of the Jenkins—Traub method and gives it its numerical stability. The false position binary search with just one residual intervals can be faster than the bisection method and will never diverge like the secant method; however, it may fail to converge in some naive implementations due to roundoff errors. In general, numerical algorithms are not guaranteed to find all the roots of a function, so failing to find a root does not prove that there is no root.