1. ROOTS OF EQUATIONS Francy Guerrero Zabala Escuela de ingenieria de PetróleosUniversidad Industrial de Santander

2. INTRODUCTION Years ago , we learn to use the cuadratic formula to solve F(x). The values calculated with this equation are called the ‘’roots’’. They represents the values of x that make f(x) equal to zero. Thus we can define the roots of an equation as the value of x that makes f(x)=0. For this reason , roots are sometimes called the zeros of the equation

3. GRAPHICAL METHODS A simple method for obtaining an estimate of the root of the equation f(x)=0 is to make a plot of the function and observe where it crosses the x axis. this point which represents the x value for which f(x)=0, provides a rough approximation of the root Graphical techniques are of limited practical value because they are not precise. However, graphical methods can be utilized to obtain rough estimates of roots.

4. THE BISECTION METHOD The bisection method, which is alternatively called binary chopping, interval halving, or Bolzano's method, is one type of incremental search method in which the interval is always divided in half. If a function changes sign over interval, the function value at the midpoint is evaluated. The location of the root is then determinate as lying at the midpoint of the subinternal within which the sign change occurs .The process is repeated to obtain refined estimates.

5. THE BISECTION METHOD A simple algorithm for the calculation is shown in the figure ( CHAPRA, source information)

6. THE FALSE POSITION METHOD

7. THE FALSE POSITION METHOD Although bisection is a perfectly valid technique for determining roots, its ‘’brute-force’’ approach is relatively inefficient . False position is an alternative based on a graphical insight. A shortcoming of the bisection methods is that , in dividing the interval from xl to xa into equal halves, no account is taken of the magnitudes of f(xl) and F(xa). For example f(xl) is much closer to zero than f(xa), it is likely that the root is closer to xl than to xa. An alternative method that exploits this graphical insight is to join f(xl) and F(xa) by a straight line The fact that the replacement of the curve by a straight line gives a false position of the root is the origin of the name, method of false position, it is also called the liner interpolation method

8. INCREMENTAL SEARCHES AND DETERMINING INITIANAL GUESSE Cases where roots could be missed because the increment length of the search procedure is too large. note that the last root on the right is multiple and would be missed regardless of increment length

9. INCREMENTAL SEARCHES AND DETERMINING INITINAL GUESSE Another option to determinate all possible root is incorporate an incremental search at the beginning of the computer program. This consists of starting at one end of the region of interest and then making function evaluations at small increments across the region. When the function changes sign, it is assumed that the root falls within the increment. A potential problem with an incremental search is the choice of the incremental length If the length is too small, the search can be very time consuming. On the other hand , if the length is too great , there is a possibility that closely space roots might be missed . The problem is compounded by the possible existence of multiple roots. A partial remedy for such cases is to computed the first derivate changes sign, it suggests that a minimum or maximum may have occurred and the interval should be examined more closely for the existence of a possible root

10. OPEN METHODS

11. THE NEWTON RAPHSON METHOD Perhaps the most widely use of all root- locating formulas is the Newton – Raphson equation . if the initial guess at the root is xi , a tangent can be extended from the point ( xi, f(xi)). the point where this tangent crosses the x axis usually represents an improved estimate of the root

12. THE NEWTON RAPHSON METHOD The Newton-Raphson method can be derived on the basis on the basis of this geometrical interpretation., the first derivative at x is equivalent to slope: Which cab be rearranged to yield Newton – Raphson formula

13. THE SECANT METHOD Graphical depiction of the secant method. This technique is similar to the Newton-Raphson .technique in the sense that an estimate of the root is predicted by extrapolating a tangent of the function to the x axis . However , The secant method uses a difference rather than a derivative to estimate the slope

14. THE SECANT METHOD A potential problem in implementing the Newton –Raphson method is the evaluation of the derivate Although this is not inconvenient for polynomials and many others functions, there are certain functions whose derivates may be extremely difficult or inconvenient to evaluate . For these cases, the derivate can be approximated by a backward finite divided difference: This approximation can be substituted in to the equation to yield the following iterative equation: Secant method formula

15. MULTIPLED ROOTS A multiple root corresponds to a point where a function is tangent to the x axis. For example, a double root results from The equation has a double root because one value of x makes two terms in 1st equation equal to zero. Graphically, this corresponds to the curve touching the x axis tangentially at the double root. Examine Fig 6.10a at x=1. Notice that the function touches the axis but does not cross it at the root. A triple root corresponds to the case where one x value makes three terms in a equation equal to zero as in Notice that the graphical depiction ( fig6.10B) again indicates that the function is tangent to the axis at the root , but that for this case the axis is crossed. In general, odd multiplied roots cross the axis, whereas even ones do not. For example, the quadruple root in fig 6.10c does not cross the axis

16. This information has been taken , studied and read from: NUMERICAL METHODS FOR ENGINEERS, FIFTH EDITION, STEVEN C. CHAPRA AND RAYMOND P. CANALE.