# Solving nonlinear equations in one variable

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1 Chapter Solving nonlinear equations in one variable Contents.1 Bisection method Fixed point iteration Newton-Raphson method Secant method Rates of convergence Evaluation of the error In this section we shall be concerned with nding a root of the equation f(x) = 0, (.1) where f is a nonlinear function of x if it is linear (ax+b = 0) we can solve trivially. We shall only be concerned with nding real roots of (.1) though, of course, there is no guarantee that all, or indeed any, of the roots have to be real. For example, we might wish to solve equations of the form x x = 0, e x x +3x = 0, xcosx+x +3x = 0. None of these equations have solutions that can be written in a nice closed form, and so numerical approach is the only way. In this section, we shall describe iterative methods, i.e. methods that generate successive approximations to the exact solution, by applying a numerical algorithm to the previous approximation..1 Bisection method Suppose f(x) is a continuous function on some interval [a,b] with f(a) and f(b) of opposite sign 1. Then, by the intermediate value theorem (IVT), p (a,b) with f(p) = 0. (For 1 The product of two non-zero numbers f(a)f(b) < 0 if f(a) and f(b) are of opposite signs; f(a)f(b) > 0 if f(a) has the same sign as f(b). 7

2 8. Fixed point iteration simplicity, let's assume there exists only one root in (a,b).) f(b) f(p ) a p 1 p p b f(a) f(p 1 ) Bisection gives a simple and robust method for bracketing the root p. The algorithm works as follows: Take the midpoint p 1 = (a+b)/ as a rst guess and calculate f(p 1 ). If f(p 1 ) and f(b) are of opposite sign then the root lies in the interval [p 1,b]. (Conversely, if f(p 1 ) has the opposite sign to f(a) then the root lies in the interval [a,p 1 ].) This procedure can be repeated iteratively, e.g. taking a second guess p = (p 1 + b)/ from the example above. At each iteration we halve the size of the interval [a n,b n ] that contains the root p and after n iterations of the procedure we have reduced the uncertainty in p down to, E n = p p n b a n. (.) Example.1 Advantages of the method : i. provided the function is continuous on an interval [a,b], with f(a)f(b) < 0, bisection is guaranteed to work (up to round-o error); ii. the number of iterations needed to achieve a specic accuracy is known in advance. Disadvantage of the method : i. the method is slow to converge. (Reducing the error interval to10 4 requires 16 iterations in the example.1); ii. the errors in p n and in f(p n ) do not necessarily decrease between iterations. Note that, in the example.1, p = 1.5 is closer to p (and f(p n ) closer to 0) than the next 3 iterates. Also, no advantage is taken of intermediate good approximations.. Fixed point iteration A xed point of a function g(x) is a value p such that g(p) = p.

3 Chapter Solving nonlinear equations in one variable 9 Fixed point iteration procedure. Let g be a continuous function. If the sequence p n = g(p n 1 ) converges to p as n then g(p) = p. So, p is a stable xed point of g. Thus, provided p 0 is suciently close to p, we can use the simple iteration to nd stable xed points. p n = g(p n 1 ) (.3) Root nding. A root nding problem, f(x) = 0, can be easily converted to a xed point iteration by choosing, e.g., g(x) = x f(x), g(x) = x+3f(x), or g(x) = x +f(x), etc. It is important to note that there exists an innite number of such functions g but the precise form of the g we choose will prove crucial for the convergence of the xed point iteration. Example. Example.3 Consider an iterate close to p, p n = p+ε, say. Then, p n+1 = g(p n ) = g(p+ε) = g(p)+εg (p)+ O(ε ) = p+εg (p)+o(ε ) (using Taylor series). Thus p p n = ε and p p n+1 ε g (p), so if g (p) > 1 then p n+1 is further away from p than p n ; there is no convergence. Theorem.1 (Fixed point theorem) If g C[a,b] (i.e. is a continuous function on the interval [a,b]) and, x [a,b],g(x) [a,b] then g has a xed point in [a,b] (existence). If, in addition, g (x) exits on (a,b) and there exists a positive constant K < 1 such that, x (a,b), g (x) K then i. the xed point is unique (uniqueness), ii. for anyp 0 [a,b] the sequencep n = g(p n 1 ) converges to this unique xed pointp [a,b] (stability). Proof. The proof is in three parts. First, we prove that a xed point exists. Second we prove that it is unique and third that the sequence must converge. Existence. If g(a) = a or g(b) = b then a or b, respectively, is a xed points. If not, then g(a) > a and g(b) < b. Dene h(x) = g(x) x. Then h C[a,b] with h(a) = g(a) a > 0 and h(b) = g(b) b < 0. Therefore, the intermediate value theorem implies that there exists p (a,b) such that h(p) = 0 i.e. g(p) = p, so p is a xed point. Uniqueness. Suppose that p and q are two distinct xed points, i.e. h(p) = h(q) = 0, with p < q. Then by Rolle's theorem, c (p,q) such that h (c) = 0. But h (x) = g (x) 1 K 1 < 0 since g (x) K < 1. This is a contradiction. Since the only assumption we have made is that p q, then it follows that this assumption must be wrong. Thus, p = q i.e. the xed point is unique. Stability. p n p = g(p n 1 ) g(p). But the mean value theorem implies that ξ in the interval between p n 1 and p, subset of (a,b), such that g(p n 1 ) g(p) = g (ξ) (p n 1 p) K (p n 1 p).

4 10.3 Newton-Raphson method Therefore, p n p K (p n 1 p) K (p n p)... K n (p 0 p), with K < 1. Hence, p n p K n (p 0 p) 0 when n and the sequence (p n ) converges to the unique xed point p. It is important to note that this theorem gives sucient conditions for convergence, but they are not necessary. In the example., when g(x) = x x /+1, g (x) = 1 x. So, x [1,1.5], g (x) 0.5 and g(x) [1.375,1.5] [1,1.5] (subset). Hence, all the conditions for the xed point theorem are satised and the iteration converges. However, if g(x) = x + 3x / 3 then g (x) = 1 + 3x > 1 when x > 0. So, the xed point x = is unstable..3 Newton-Raphson method There are various ways of deriving this iterative procedure for solving nonlinear equations f(x) = 0 (see appendix C), but the graphical method is particularly instructive. y y = f(x) p 0 p p p 3 p 1 x Suppose we have an initial guess p o where f and f are known. Then, p 1, the intersection of the x-axis with the tangent to f in p 0, is an improved estimate of the root. f (p 0 ) = f(p 0) p 0 p 1 p 1 = p 0 f(p 0) f (p 0 ). Repeating this procedure leads to the general iterative scheme: Obviously it is vital to have f (p n 1 ) 0. p n = p n 1 f(p n 1) f, n 1. (.4) (p n 1 ) Relation with xed point iteration. The Newton-Raphson method is a particular case of the xed point iteration algorithm, with the iterative map p n = g(p n 1 ), where the mapping function g(x) = x f(x)/f (x). Example.4 Advantages of Newton's method. i. It is fast. (We shall quantify this later.) ii. It can be extended to multidimensional problem of nding, e.g. f 1 (x,y) = f (x,y) = 0 for which bisection method cannot be used.

5 Chapter Solving nonlinear equations in one variable 11 Disadvantages of Newton's method. i. It requires evaluating the derivative, f (x), at each iteration. ii. It can fail if the initial guess is not suciently close to the solution, particularly if f (x) = 0. For instance, in the example.4 convergence is guaranteed. (Iterations converge to + if p 0 > 0 and to if p 0 < 0.) However if f has the form y p 1 p p 0 a y = f(x) b x even though the initial guess p 0 is closest to the root x = a, (p n ) will converge to x = b. Even more troublesome, a function of the form y y = f(x) p p 0 p 1 x has a root at x = p but with the initial guess p 0 as shown, the iterations get caught in an endless loop, never converging..4 Secant method A solution to avoid the calculation of the derivative for Newton-Raphson method is to use the last two iterates to evaluate f (x). By denition, f f(x) f(p n 1 ) (p n 1 ) = lim, x p n 1 x p n 1 but letting x = p n leads to the approximation f (p n 1 ) f(p n ) f(p n 1 ) p n p n 1. Rather than (.4), the iteration scheme for the secant method is p n 1 p n p n = p n 1 f(p n 1 ) f(p n 1 ) f(p n ). (.5) Unlike Newton-Raphson, this method is a two-point method. (Iteration p n uses both, p n 1 and p n.) So, initially, two guesses are needed, p 0 and p 1. Example.5 Secant method is slightly slower than Newton's method, but it does not require the evaluation of a derivative. It does need two initial points but these do not have to straddle the root.

6 1.5 Rates of convergence.5 Rates of convergence We mentioned, that the bisection method converges slowly, whilst, when the Newton-Raphson algorithm converges, it is fast. In this section we shall quantify the convergence rate of iteration schemes. Suppose a numerical method producing a sequence of iterations (p n ) that converges to p. The method has order of convergence α if p n+1 p K p n p α (i.e. p n+1 p is asymptotic to K p n p α ), for some K > 0. Or equivalently if p n+1 p lim n p n p α = K. When α = 1 the convergence is linear, and when α = it is quadratic. If the error of an iterative algorithm is O(ε) at iteration n then, for a linear methods, it remains O(ε) at iteration n + 1, but for a quadratic method the error becomes O(ε ). Thus, higher order methods generally converge more rapidly than lower order ones..5.1 Fixed point iteration Let us consider a xed point iterationp n+1 = g(p n ) producing a sequence of iterations(p n ) p as n, such that p = g(p) (since g is continuous). Expand g(p n ) as a Taylor series in powers of (p n p). For some ξ n in the interval between p and p n, g(p n ) = g(p)+(p n p)g (ξ n ) p n+1 = p+(p n p)g (ξ n ). Thus, p n+1 p = g (ξ n ) p n p, and p n+1 p lim = g (p) p n+1 p g (p) p n p. (.6) n p n p So, for 0 < g (p) < 1 xed point iteration is linearly convergent..5. Newton-Raphson Equation (.6) shows that the converge of xed point iterations is best when g (p) is as small as possible, and preferably zero. Newton-Raphson, which is a xed point iteration method with the mapping function g(x) = x f(x)/f (x), achieves this. g (x) = 1 f (x) f(x)f (x) f (x) = f(x)f (x) f (x). Thus, provided f (p) 0 (i.e. p is a simple root, i.e. of multiplicity one), g (p) = 0 since, by denition, f(p) = 0. Expand g(p n ) as a Taylor series in powers of (p n p), up to second order. For some ξ n in the interval between p and p n, g(p n ) = g(p)+(p n p)g (p)+ (p n p) g (ξ n ) p n+1 = p+ (p n p) g (ξ n ), since p n+1 = g(p n ), g(p) = p and g (p) = 0. Thus, p n+1 p = g (ξ n ) p n p

7 Chapter Solving nonlinear equations in one variable 13 implies p n+1 p lim n p n p = g (p) p n+1 p g (p) p n p, (.7) so the convergence of Newton's method is quadratic. (Note that g (p) = f (p)/f (p).) However, in the case when g (p) 0 (i.e. if f (p) = 0), Thus, g(p n ) = g(p)+(p n p)g (ξ n ) p n+1 p = (p n p)g (ξ n ). p n+1 p lim = g (p), n p n p So, if g (p) 0 the convergence of Newton's methods is not quadratic but linear (i.e. the same as the general form of xed point iteration)..5.3 Other methods Bisection. At each step, the size of the interval that contains the root is halved, somax(e n ) = max(e n 1 )/, but the error does not necessarily decrease monotonically. However, if we regard the upper bounds of the errors as an estimate of the error, then bisection is linearly convergent. Secant. This method requires two steps and is harder to analyse. However, it can be shown in a strict mathematical sens (see appendix D) that p n+1 p = K p n p p n 1 p, giving an order of convergence of (1+ 5)/ 1.6 (golden ratio). The convergence is super-linear, i.e. better than linear but poorer than quadratic..6 Evaluation of the error Generally, the exact value of the root of a function (or the exact value of a xed point) is unknown. So, it is impossible the evaluate the error of iterative methods, E n = p p n, but we can nd an upper bound on the error using the dierence between two iterates. A sequence (p n ) is called contractive if there exists a positive constant K < 1 such that Theorem. If (p n ) is contractive with constant K then, p n+ p n+1 K p n+1 p n, n N. i. (p n ) is convergent, lim p n = p. (.8) n ii. p p n is bounded above, p p n K 1 K p n p n 1. (.9) Hence, for K given, p n p n 1 provides an the upper bound on E n = p p n, the error at iteration n.

8 14.6 Evaluation of the error

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