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However, most root-finding algorithms do not guarantee that they will find all roots of a function, and if such an algorithm does not find any root, that does not necessarily mean that no root exists. Most numerical root-finding methods are iterative methods, producing a sequence of numbers that ideally converges towards a root as a limit.
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:
Then for each interval (A(x), M(x)) in the list, the algorithm remove it from the list; if the number of sign variations of the coefficients of A is zero, there is no root in the interval, and one passes to the next interval. If the number of sign variations is one, the interval defined by () and () is an isolating interval.
In this case a and b are said to bracket a root since, by the intermediate value theorem, the continuous function f must have at least one root in the interval (a, b). At each step the method divides the interval in two parts/halves by computing the midpoint c = (a+b) / 2 of the interval and the value of the function f(c) at that point.
b k is the current iterate, i.e., the current guess for the root of f. a k is the "contrapoint," i.e., a point such that f(a k) and f(b k) have opposite signs, so the interval [a k, b k] contains the solution. Furthermore, |f(b k)| should be less than or equal to |f(a k)|, so that b k is a better guess for the unknown solution than a k.
Linear search problem — find a point on a line by moving along the line; Low-rank approximation — find best approximation, constraint is that rank of some matrix is smaller than a given number; Meta-optimization — optimization of the parameters in an optimization method; Multidisciplinary design optimization
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Finding roots in a specific region of the complex plane, typically the real roots or the real roots in a given interval (for example, when roots represents a physical quantity, only the real positive ones are interesting). For finding one root, Newton's method and other general iterative methods work generally well.
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