Bisection Method Civil Engineering Majors Author(s): Autar Kaw, Jai Paul http://numericalmethods.eng.usf.edu Transforming Numerical Methods Education for STEM Undergraduates Bisection Method http://numericalmethods.eng.usf.edu Basis of Bisection Method Theorem: An equation f(x)=0, where f(x) is a real continuous function, has at least one root between x and xu if f(xl) f(xu) < 0. f(x) x xu 3 x http://numericalmethods.eng.usf.edu Theorem If function f(x) in f(x)=0 does not change sign between two points, roots may still exist between the two points. f(x) x 4 xu x http://numericalmethods.eng.usf.edu Theorem If the function f(x) in f(x)=0 does not change sign between two points, there may not be any roots between the two points. f(x) f(x) x x 5 xu x xu x http://numericalmethods.eng.usf.edu Theorem If the function f(x) in f(x)=0 changes sign between two points, more than one root may exist between the two points. f(x) xu x 6 x http://numericalmethods.eng.usf.edu Algorithm for Bisection Method 7 http://numericalmethods.eng.usf.edu Step 1 Choose x and xu as two guesses for the root such that f(x) f(xu) < 0, or in other words, f(x) changes sign between x and xu. f(x) x xu 8 x http://numericalmethods.eng.usf.edu Step 2 Estimate the root, xm of the equation f (x) = 0 as the mid-point between x and xu as f(x) x x u xm = 2 x xu 9 x http://numericalmethods.eng.usf.edu Step 3 Now check the following f(x) If f(x) f(xm) < 0, then the root lies between x and xm; then x = x ; xu = xm. x xm xu If f(x ) f(xm) > 0, then the root lies between xm and xu; then x = xm; x xu = xu. If f(x) f(xm) = 0; then the root is xm. Stop the algorithm if this is true. 10 http://numericalmethods.eng.usf.edu Step 4 New estimate x x u xm = 2 Absolute Relative Approximate Error a old x new x m m x new m 100 xmold previousestimateof root xmnew currentestimateof root 11 http://numericalmethods.eng.usf.edu Step 5 Check if absolute relative approximate error is less than prespecified tolerance or if maximum number of iterations is reached. 12 Yes Stop No Using the new upper and lower guesses from Step 3, go to Step 2. http://numericalmethods.eng.usf.edu Example 1 You are making a bookshelf to carry books that range from 8 ½ ” to 11” in height and would take 29”of space along length. The material is wood having Young’s Modulus 3.667 Msi, thickness 3/8 ” and width 12”. You want to find the maximum vertical deflection of the bookshelf. The vertical deflection of the shelf is given by v( x) 0.42493 10-4 x3 0.13533 10-8 x5 0.66722 10-6 x4 0.018507x where x is the position along the length of the beam. Hence to find the dv maximum deflection we need to find where f ( x) 0 and conduct the dx second derivative test. 13 http://numericalmethods.eng.usf.edu Example 1 Cont. x Books Bookshelf Figure 5 A loaded bookshelf. The equation that gives the position x where the deflection is maximum is given by f(x) 0.6766510-8 x4 0.2668910-5 x3 0.1274810-3 x2 0.018507 0 Use the bisection method of finding roots of equations to find the position x where the deflection is maximum. Conduct three iterations to estimate the root of the above equation. Find the absolute relative approximate error at the end of each iteration and the number of significant digits at least correct at the end of each iteration. 14 http://numericalmethods.eng.usf.edu Example 1 Cont. 0.01883 0.02 0.01 0 f ( x) 0 0.01 0.01851 0.02 0 5 0 10 15 20 x 25 30 29 f(x) Figure 6 Graph of the function f(x). f(x) 0.67665108 x 4 0.26689105 x3 0.12748103 x2 0.018507 0 15 http://numericalmethods.eng.usf.edu Example 1 Cont. Solution From the physics of the problem, the maximum deflection would be between x 0 and x L , where L lengthof the bookshelf that is 0 xL 0 x 29 Let us assume xl 0, xu 29 16 http://numericalmethods.eng.usf.edu Example 1 Cont. Check if the function changes sign between xl and xu . f xl f 0 0.67665108 0 0.26689105 0 0.12748103 0 0.018507 0.018507 f xu f 29 4 3 2 0.67665108 29 0.26689105 29 0.12748103 29 0.018507 0.018826 4 3 2 Hence f xl f xu f 0 f 29 0.018507 0.018826 0 So there is at least one root between xl and xu that is between 0 and 29. 17 http://numericalmethods.eng.usf.edu Example 1 Cont. Entered functi on o n gi ven i nterval wi th ini ti al upper and l ower guesses 0.01883 0.02 0.01 f ( x) f ( x) 0 0 f ( x) 0.01 0.01851 0.02 0 5 1 10 15 20 25 x x u x l 30 29 f(x) xu (u pp er g u ess) xl (l ow er g u ess) Figure 7 Checking the validity of the bracket. 18 http://numericalmethods.eng.usf.edu Example 1 Cont. Iteration 1 The estimate of the root is f xm f 14.9 xm xl xu 0 29 14.5 2 2 0.67665108 14.5 0.26689105 14.5 0.12748103 14.5 0.018507 4 1.4007104 3 2 f xm f xu f 14.5 f 29 1.4007104 0.018826 0 The root is bracketed between xm and xu . The lower and upper limits of the new bracket are xl 14.5, xu 29 The absolute relative approximate error a cannot be calculated as we do not have a previous approximation. 19 http://numericalmethods.eng.usf.edu Example 1 Cont. 0.01883 0.02 0.01 f ( x) f ( x) f ( x) 0 0 f ( x) 0.01 0.01851 0.02 0 5 1 10 15 x x u x l x r 20 25 30 29 f(x) xu (u pp er g u ess) xl (l ow er g u ess) n ew g uess Figure 8 Graph of the estimate of the root after Iteration 1. 20 http://numericalmethods.eng.usf.edu Example 1 Cont. Iteration 2 xl xu 14.5 29 x 21.75 The estimate of the root is m 2 2 f xm f 21.75 0.67665108 21.75 0.26689105 21.75 0.12748103 21.75 0.018507 0.012824 4 3 2 f xm f xu f 14.5 f 21.75 1.4007104 0.012824 0 The root is bracketed between xl and xm . The lower and upper limits of the new bracket are xl 14.5, xu 21.75 21 http://numericalmethods.eng.usf.edu Example 1 Cont. 0.01883 0.02 0.01 f ( x) f ( x) f ( x) 0 0 f ( x) 0.01 0.01851 0.02 0 5 10 15 x x u x l x r 0 20 25 30 29 f(x) xu (u pp er g uess) xl (l ow er g ues s) n ew g uess Figure 9 Graph of the estimate of the root after Iteration 2. 22 http://numericalmethods.eng.usf.edu Example 1 Cont. The absolute relative approximate error at the end of Iteration 2 is xmnew xmold a 100 new xm 21.75 14.5 100 21.75 33.333% None of the significant digits are at least correct in the estimated root xm 21.75 as the absolute relative approximate error is greater than 5%. 23 http://numericalmethods.eng.usf.edu Example 1 Cont. Iteration 3 xl xu 14.5 21.75 x 18.125 The estimate of the root is m 2 f xm f 18.125 2 0.67665108 18.125 0.26689105 18.125 0.12748103 18.125 0.018507 4 3 2 6.7502103 f xl f xm f 14.5 f 18.125 1.4007104 6.7502103 0 The root is bracketed between xl and xm . The lower and upper limits of the new bracket are xl 14.5, xu 18.125 24 http://numericalmethods.eng.usf.edu Example 1 Cont. 0.01883 0.02 0.01 f ( x) f ( x) f ( x) 0 0 f ( x) 0.01 0.01851 0.02 0 5 10 15 x x u x l x r 0 20 25 30 29 f(x) xu (u pp er g u ess) xl (l ow er g u ess) n ew g uess Figure 10 Graph of the estimate of the root after Iteration 3. 25 http://numericalmethods.eng.usf.edu Example 1 Cont. The absolute relative approximate error at the end of Iteration 3 is xmnew xmold a 100 new xm 18.125 21.75 100 18.125 20% Still none of the significant digits are at least correct in the estimated root as the absolute relative approximate error is greater than 5%. Seven more iterations were conducted and these iterations are shown in Table 1. 26 http://numericalmethods.eng.usf.edu Example 1 Cont. Table 1 Root of f x 0 as function of number of iterations for bisection method. 27 Iteration x xu xm a % f xm 1 2 3 4 5 6 7 8 9 10 0 14.5 14.5 14.5 14.5 14.5 14.5 14.5 14.5 14.5566 29 29 21.75 18.125 16.313 15.406 14.953 14.727 14.613 14.613 14.5 21.75 18.125 16.313 15.406 14.953 14.727 14.613 14.557 14.585 ---------33.333 20 11.111 5.8824 3.0303 1.5385 0.77519 0.38911 0.19417 -1.3992 10 0.012824 3 6.7502 10 3 3.3509 10 3 1.6099 10 4 7.3521 10 4 2.9753 10 5 7.8708 10 −3.0688105 2.4009 105 4 http://numericalmethods.eng.usf.edu Example 1 Cont. At the end of the 10th iteration, a 0.19417% Hence the number of significant digits at least correct is given by the largest value of m for which a 0.5 102 m 0.19417 0.5 102 m 0.38835 102 m log0.38835 2 m So m 2 log0.38835 2.4108 m2 The number of significant digits at least correct in the estimated root 14.585 is 2. 28 http://numericalmethods.eng.usf.edu Advantages 29 Always convergent The root bracket gets halved with each iteration - guaranteed. http://numericalmethods.eng.usf.edu Drawbacks Slow convergence 30 http://numericalmethods.eng.usf.edu Drawbacks (continued) 31 If one of the initial guesses is close to the root, the convergence is slower http://numericalmethods.eng.usf.edu Drawbacks (continued) If a function f(x) is such that it just touches the x-axis it will be unable to find the lower and upper guesses. f(x) f x x 2 x 32 http://numericalmethods.eng.usf.edu Drawbacks (continued) Function changes sign but root does not exist f(x) 1 f x x x 33 http://numericalmethods.eng.usf.edu Additional Resources For all resources on this topic such as digital audiovisual lectures, primers, textbook chapters, multiple-choice tests, worksheets in MATLAB, MATHEMATICA, MathCad and MAPLE, blogs, related physical problems, please visit http://numericalmethods.eng.usf.edu/topics/bisection_ method.html THE END http://numericalmethods.eng.usf.edu