In mathematics, the bisection method is a root-finding method that applies to any continuous function for which one knows two values with opposite signs. The method consists of repeatedly bisecting the interval defined by these values and then selecting the subinterval in which the function changes sign, and therefore must contain a root. It is a very simple and robust method, but it is also relatively slow. Because of this, it is often used to obtain a rough approximation to a solution which is then used as a starting point for more rapidly converging methods. The method is also called the interval halving method, the binary search method, or the dichotomy method.
For polynomials, more elaborate methods exist for testing the existence of a root in an interval (Descartes' rule of signs, Sturm's theorem, Budan's theorem). They allow extending the bisection method into efficient algorithms for finding all real roots of a polynomial; see Real-root isolation.
This formula can be used to determine, in advance, an upper bound on the number of iterations that the bisection method needs to converge to a root to within a certain tolerance.The number n of iterations needed to achieve a required tolerance ε (that is, an error guaranteed to be at most ε), is bounded by
However, despite the bisection method being optimal with respect to worst case performance under absolute error criteria it is sub-optimal with respect to average performance under standard assumptions  as well as asymptotic performance. Popular alternatives to the bisection method, such as the secant method, Ridders' method or Brent's method (amongst others), typically perform better since they trade-off worst case performance to achieve higher orders of convergence to the root. And, a strict improvement to the bisection method can be achieved with a higher order of convergence without trading-off worst case performance with the ITP Method.
In geometry, bisection is the division of something into two equal or congruent parts (having the same shape and size). Usually it involves a bisecting line, also called a bisector. The most often considered types of bisectors are the segment bisector (a line that passes through the midpoint of a given segment) and the angle bisector (a line that passes through the apex of an angle, that divides it into two equal angles).
An exhaustive qualitative (vote-counting) review is conducted of the literature concerning visual and non-visual line bisection in neurologically normal subject populations. Although most of these studies report a leftward bisection error (i.e., pseudoneglect), considerable between-study variability and inconsistency characterize this literature. A meta-analysis of this same literature is performed in which the total quantitative data set, comprising 73 studies (or sub-studies) and 2191 subjects, is analyzed with respect to 26 performance factors. The meta-analytic results indicate a significant leftward bisection error in neurologically normal subjects, with an overall effect size of between -0.37 and -0.44 (depending on integration method), which is significantly modulated to varying degrees by a number of additional task or subject variables. For example, visual bisection tasks, midsagittal-pointing tasks and tactile bisection tasks all lead to leftward errors, while kinesthetic tasks result in rightward errors. Tachistoscopic forced-choice testing methods reveal much greater estimates of bisection error (effect size = -1.32) than do manual method-of-adjustment procedures (effect size= -0.40). Subject age significantly modulates line bisection performance such that older subjects err significantly rightward compared to younger subjects, and to veridical line midpoint. Male subjects make slightly larger leftward errors than do female subjects. Handedness has a small effect on bisection errors, with dextrals erring slightly further to the left than sinistral subjects. The hand used to perform manual bisection tasks modulated performance, where use of the left hand lead to greater leftward errors than those obtained using the right hand. One of the most significant factors modulating bisection error is the direction in which subjects initiate motor scanning (with either eye or hand), where a left-to-right scan pattern leads to large leftward errors while a right-to-left scan pattern leads to rightward errors.
A simple bisection procedure for iteratively converging on a solution which is known to lie inside some interval proceeds by evaluating the function in question at the midpoint of the original interval and testing to see in which of the subintervals or the solution lies. The procedure is then repeated with the new interval as often as needed to locate the solution to the desired accuracy.
Inspired by recent work of Byers we establish a simple connection between the singular values of a transfer matrix evaluated along the imaginary axis and the imaginary eigenvalues of a related Hamiltonian matrix. We give a simple linear algebraic proof of this connection, and also a more intuitive explanation based on a certain indefinite quadratic optimal control problem and the work of Willems. This result yields a simple bisection algorithm to compute the H_infinity norm of a transfer matrix. The bisection method is far more efficient than algorithms which involve a search over frequencies, and of course the usual problems associated with such methods (such as determining how fine the search should be) do not arise. The method is readily extended to compute other quantities of system-theoretic interest, e.g. the minimum dissipation of a transfer matrix. A variation of the method can be used to solve the H_infinity Armijo line search problem with no more computation than is required to compute a single H_infinity norm.
The Line Bisection Test is a test is a quick measure to detect the presence of unilateral spatial neglect (USN). To complete the test, one must place a mark with a pencil through the center of a series of horizontal lines. Usually, a displacement of the bisection mark towards the side of the brain lesion is interpreted as a symptom of neglect.
The relationship between abnormal line bisection and visual neglect has been observed for over a century (e.g. Axenfeld, 1894; Liepmann & Kalmus, 1900). In 1980, Schenkenberg, Bradford, and Ajax formally evaluated this method of detecting the presence of visual neglect in patients with lesions of the non-dominant hemisphere, and are thought to be the first to statistically evaluate this method.
Scoring:The test is scored by measuring the deviation of the bisection from the true center of the line. A deviation of more than 6 mm from the midpoint indicates USN. Omission of two or more lines on one half of the page indicates USN.
We consider a single-product revenue management problem with an inventory constraint and unknown, noisy, demand function. The objective of the firm is to dynamically adjust the prices to maximize total expected revenue. We restrict our scope to the nonparametric approach where we only assume some common regularity conditions on the demand function instead of a specific functional form. We propose a family of novel pricing heuristics that successfully balance the tradeoff between exploration and exploitation. The idea is to generalize the classic bisection search method to a problem that is affected both by stochastic noise and an inventory constraint. Our algorithm extends the bisection method to produce a sequence of pricing intervals that converge to the optimal static price with high probability. Using regret (the relative revenue loss compared to the optimal dynamic pricing solution for a clairvoyant) as the performance metric, we show that one of our heuristics exactly matches the theoretical asymptotic lower bound that has been previously shown to hold for any feasible pricing heuristic. Although the results are presented in the context of revenue management problems, our analysis of the bisection technique for stochastic optimization with learning can be potentially applied to other application areas.
The bisection algorithm, in spite of being the simplest root-finding algorithm, is very robust, because convergence is guaranteed when very basic conditions hold. For this reason, it is used as the basis of more advanced algorithms, that will also converge to a root much faster.
This module provides support for maintaining a list in sorted order withouthaving to sort the list after each insertion. For long lists of items withexpensive comparison operations, this can be an improvement over the more commonapproach. The module is called bisect because it uses a basic bisectionalgorithm to do its work. The source code may be most useful as a workingexample of the algorithm (the boundary conditions are already right!).
You may often find that during a bisect session you want to havetemporary modifications (e.g. s/#define DEBUG 0/#define DEBUG 1/ in aheader file, or "revision that does not have this commit needs thispatch applied to work around another problem this bisection is notinterested in") applied to the revision being tested.
1)We define $g(x):= f(x) - c$, which introduces two roots (possibly more). We can find these by bisection (not easy, as you need $b$ (or $a$) to be located between these two roots). Then we can try to estimate the original root $x_0$. That works well, if $f(x)$ is not "wild" around $x_0$.
2)We know that if $f$ touches the $x$-axis in $x_0$, then $f'$ crosses the $x$-axis in $x_0$. So we could use bisection on $f'$. On the other hand: If we know $f'$, we would rather use Newton's method. 041b061a72