Rate Of Convergence In Numerical Analysis

Rate Of Convergence In Numerical AnalysisIn numerical analysis, the speed at which a playnt status approaches its limit is c solelyed the target of convergence. Strictly speaking, a limit does not give information about any finite first part of the sequence this concept is of practical splendour if we deal with a sequence of successive ideas for a iterative method, as typically fewer iterations be needed to output a useful approximation if the rate of convergence is higher. This may even make the difference between needing ten or a million iterations.Similar concepts are utilise for discretization methods. The origin of the discretized problem converges to the solution of the continuous problem as the grid size goes to zero, and the speed of convergence is one of the factors of the efficiency of the method. However, the terminology in this case is divergent from the terminology for iterative methods.Convergence speed for iterative methodsBasic definitionSuppose that the seq uence xk converges to the number L.We say that this sequence converges linearly to L, if thither exists a number (0, 1) such(prenominal) thatThe number is called the rate of convergence.If the above holds with = 0, then the sequence is said to converge superlinearly. One says that the sequence converges sublinearly if it converges, but =1.The next definition is used to distinguish superlinear rates of convergence. We say that the sequence converges with social club q for q 1 to L ifIn particular, convergence with order 2 is called quadratic convergence, and convergence with order 3 is called cubic convergence.This is nearlytimes called Q-linear convergence, Q-quadratic convergence, etc., to distinguish it from the definition below. The Q stands for quotient, because the definition uses the quotient between two successive terms.Extended definitionThe drawback of the above definitions is that these do not catch several(prenominal) sequences which still converge reasonably fast , but whose speed is variable, such as the sequence bk below. in that respectfore, the definition of rate of convergence is sometimes extended as follows.Under the new definition, the sequence xk converges with at least order q if there exists a sequence k such thatand the sequence k converges to zero with order q according to the above simple definition. To distinguish it from that definition, this is sometimes called R-linear convergence, R-quadratic convergence, etc.ExamplesConsider the following sequencesThe sequence ak converges linearly to 0 with rate 1/2. More generally, the sequence Ck converges linearly with rate if CONVERGENCE SPEED FOR DISCRETIZATION orderSA similar situation exists for discretization methods. Here, the important parameter is not the iteration number k but the number of grid points, here denoted n. In the simplest situation (a uniform one-dimensional grid), the number of grid points is inversely proportional to the grid spacing.In this case, a sequenc e xn is said to converge to L with order p if there exists a constant C such that xn L This is written as xn L = O(n-p) using the big O notation.This is the relevant definition when discussing methods for numerical quadrature or the solution of ordinary differential equations.ExamplesThe sequence dk with dk = 1 / (k+1) was introduced above. This sequence converges with order 1 according to the convention for discretization methods.The sequence ak with ak = 2-k, which was also introduced above, converges with order p for every number p. It is said to converge exponentially using the convention for discretization methods. However, it single converges linearly (that is, with order 1) using the convention for iterative methods.RATE OF CONVERGENCE OF BISECTION METHODIf f is a continuous function on the interval a, b and f(a)f(b) The bisection method gives only a range where the theme exists, rather than a single estimate for the roots location. Without using any other information, t he best estimate for the location of the root is the midpoint of the smallest bracket found. In that case, the absolute error after n steps is at mostIf either endpoint of the interval is used, then the maximum absolute error isthe entire length of the interval.These formulas can be used to determine in advance the number of iterations that the bisection method would need to converge to a root to within a certain tolerance. For, using the second formula for the error, the number of iterations n has to satisfyto ensure that the error is smaller than the tolerance .If f has several simple roots in the interval a,b, then the bisection method will find one of them.RATE OF CONVERGENCE OF FALSE-POSITION METHODIf the sign end-points a0 and b0 are chosen such that f(a0) and f(b0) are of the opposite signs, then one of the end-points will converge to a root of f. The other end-point will remain fixed for all subsequent iterations while the converging endpoint becomes updated. Unlike the bi section method, the width of the bracket does not tend to zero. As a consequence, the linear approximation to f(x), which is used to calve the out of true position, does not improve in its quality.One example of this phenomenon is the function,f(x) = 23 42 + 3xon the initial bracket 1,1. The left end, 1, is never replaced and thus the width of the bracket never falls below 1. Hence, the right endpoint approaches 0 at a linear rate.While it is false to think that the method of false position is a good method, it is equally a mistake to think that it is unsalvageable. The failure mode is easy to detect and easily remedied by next weft a modified false position, such asordown-weighting one of the endpoint values to force the next ck to occur on that side of the function. There are other ways to pick the rescaling which give even better convergence rates.RATE OF CONVERGENCE OF SECANT METHODThe iterates xn of the secant method converge to a root of f, if the initial values x0 and x1 are sufficiently close to the root. The order of convergence is , whereis the golden ratio. In particular, the convergence is superlinear.This result only holds under some technical conditions, to wit that f be double continuously differentiable and the root in question be simple (i.e., with multiplicity 1).If the initial values are not close to the root, then there is no guarantee that the secant method converges. The right-most quantity above may be verbalised assince . Then, from a Taylor expansion of about one findsfor some . SimilarlyPlacing these quantities into Equation 4.9 will result in some cancellation,orThe approximation expressed in Equation 4.11 can be explicitly quantified by recognizing that for some . Hence This completes the analysis of the final term in Equation 4.8. The first term in Equation 4.8 can be analyzed similarly, to obtainHence, the error given in the secant method is roughly given asA more careful investigation and analysis produces the exact expr essionfor some . To generate a complete convergence analysis, assume that f(x) is bounded and in some neighborhood of . These assumptions imply that sufficiently close to . Further, assume that the initial values and are chosen sufficiently close to to satisfyfor some KThe exponents on K form the Fibonacci sequence, . The Fibonacci sequence is defined inductively, asThe general error term is then given to beThe Fibonacci number have an explicit formula, namelywith . Note that , and since K At this point, we haveWhile somewhat complex-looking, the equation above actually produces the convergence rate that we seek.RATE OF CONVERGENCE OF NEWTON RAPHSON METHODSuppose that the function has a zero at , i.e., () = 0.If f is continuously differentiable and its differential gear is nonzero at , then there exists a neighbourhood of such that for all starting values x0 in that neighbourhood, the sequence xn will converge to .If the function is continuously differentiable and its derivative i s not 0 at and it has a second derivative at then the convergence is quadratic or faster. If the second derivative is not 0 at then the convergence is unless quadratic. If the third derivative exists and is bounded in a neighbourhood of , thenwhereIf the derivative is 0 at , then the convergence is usually only linear. Specifically, if is twice continuously differentiable, () = 0 and () 0, then there exists a neighbourhood of such that for all starting values x0 in that neighbourhood, the sequence of iterates converges linearly, with rate log10 2 (Sli Mayers, Exercise 1.6). Alternatively if () = 0 and (x) 0 for x 0, x in a neighbHYPERLINK http//en.wikipedia.org/wiki/Topological_neighborhoodourhood U of , being a zero of multiplicity r, and if Cr(U) then there exists a neighbourhood of such that for all starting values x0 in that neighbourhood, the sequence of iterates converges linearly.However, even linear convergence is not guaranteed in pathological situations.I n practice these results are local and the neighbourhood of convergence are not known a priori, but there are also some results on global convergence, for instance, given a right neighbourhood U+ of , if f is twice differentiable in U+ and if , in U+, then, for each x0 in U+ the sequence xk is monotonically decreasing to .Proof of quadratic convergence for Newtons iterative methodAccording to TaylorHYPERLINK http//en.wikipedia.org/wiki/Taylors_theoremHYPERLINK http//en.wikipedia.org/wiki/Taylors_theorems theorem, any function f(x) which has a continuous second derivative can be represented by an expansion about a point that is close to a root of f(x).