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It says that, if the topological degree of a function f on a rectangle is non-zero, then the rectangle must contain at least one root of f. This criterion is the basis for several root-finding methods, such as those of Stenger [6] and Kearfott. [7] However, computing the topological degree can be time-consuming.
A root-phi rectangle divides into a pair of Kepler triangles (right triangles with edge lengths in geometric progression). The root-φ rectangle is a dynamic rectangle but not a root rectangle. Its diagonal equals φ times the length of the shorter side. If a root-φ rectangle is divided by a diagonal, the result is two congruent Kepler triangles.
Given a continuous function defined from [,] to such that () (), where at the cost of one query one can access the values of () on any given .And, given a pre-specified target precision >, a root-finding algorithm is designed to solve the following problem with the least amount of queries as possible:
For orientation, the root function and the fixed-point functions are simply related by = + , where is some scalar constant small enough in magnitude to make stable under iteration, but large enough for the non-linearity of the function to be appreciable.
The red curve shows the function f, and the blue lines are the secants. For this particular case, the secant method will not converge to the visible root. In numerical analysis, the secant method is a root-finding algorithm that uses a succession of roots of secant lines to better approximate a root of a function f.
Finding the root of a linear polynomial (degree one) is easy and needs only one division: the general equation + = has solution = /. For quadratic polynomials (degree two), the quadratic formula produces a solution, but its numerical evaluation may require some care for ensuring numerical stability.
Rectangular function with a = 1. The rectangular function (also known as the rectangle function, rect function, Pi function, Heaviside Pi function, [1] gate function, unit pulse, or the normalized boxcar function) is defined as [2]
Muller's method is a recursive method that generates a new approximation of a root ξ of f at each iteration using the three prior iterations. Starting with three initial values x 0, x −1 and x −2, the first iteration calculates an approximation x 1 using those three, the second iteration calculates an approximation x 2 using x 1, x 0 and x −1, the third iteration calculates an ...