Introduction to Numerical Methods/Roots of Equations
- find solutions of quadratic and cubic equations
- derive the formula and follow the algorithms for the solutions of non-linear equations using the following methods:
- book chapters on various methods
- Root Finding Algorithms
- Bisection Method
- Newton's Method
- Secant Method
- False-Position Method
Roots (or Zeros) of a function f(x) are values of x that produces an output of 0. Roots can be real or complex numbers. Finding the root of is the same as solving the equation . Solving an equation is finding the values that satisfy the condition specified by the equation.
Lower degree (quadratic, cubic, and quartic) polynomials have closed-form solutions, but numerical methods may be easier to use. To solve a quadratic equation we can use the quadratic formula:
There are many root-find algorithms for solving equations numerically.
The bisection method starts with two guesses and uses a binary search algorithm to improve the answers. If a function is continuous between the two initial guesses, the bisection method is guaranteed to converge. Here is a picture that illustrates the idea:
The advantages of bisection method include guaranteed convergence on continuous functions and the error is bounded.
The disadvantages of bisection method include relatively slow convergence and non convergence on certain functions.
Newton's Method (a.k.a Newton-Raphson Method) is an open method for solving non-linear equations. Contrary to a bracketing-method (e.g. bisection method) Newton's method needs one initial guess but it doesn't guarantee to converge.
The basic idea of Newton's method is as follows:
- Given a function f of "x" and a initial guess for the root of this function, a better guess is
- This is the Newton-Raphson formula.
The following animation illustrates the method:
Lets solve the following equations using Newton's method:
from sympy import Symbol, diff, sin x = Symbol('x') y = sin(x) derivative = diff(y, x) ''' Solve f with initial guess g using Newton's method. Stop when the absolute relative approximate error is smaller than the tolerance or the max # iterations is reached. ''' def newton(f, derivative, g, tolerance, max_iteration): x_previous = g for i in range(max_iteration): x_current = x_previous - \ y.subs(x, x_previous).evalf()/derivative.subs(x, x_previous).evalf() error = abs((x_current-x_previous)/x_current) print "current x:", x_current, " error:", error x_previous = x_current if error < tolerance: break; newton(y, derivative, 5, 0.005, 15)
$ python newton.py current x: 8.38051500624659 error: 0.403377955140806 current x: 10.1008867567293 error: 0.170318883075941 current x: 9.29864101772707 error: 0.0862755898924158 current x: 9.42545121429349 error: 0.0134540186653470 current x: 9.42477796066766 error: 7.14344283379346e-5
Limitations of Newton's MethodEdit
Division by ZeroEdit
Because a division is involved in Newton's formula when the denominator becomes zero the method won't continue correctly. The following program demonstrates this issue.
from sympy import Symbol, diff x = Symbol('x') y = 4 - 1.0/x derivative = diff(y, x) ''' Solve f with initial guess g using Newton's method. Stop when the absolute relative approximate error is smaller than the tolerance or the max # iterations is reached. ''' def newton(f, derivative, g, tolerance, max_iteration): x_previous = g for i in range(max_iteration): x_current = x_previous - \ y.subs(x, x_previous)/derivative.subs(x, x_previous) error = abs((x_current-x_previous)/x_current) print "current x:", x_current, " error:", error x_previous = x_current if error < tolerance: break; newton(y, derivative, 0.5, 0.005, 15)
$ python newton.py current x: 0 error: oo current x: nan error: nan current x: nan error: nan current x: nan error: nan current x: nan error: nan current x: nan error: nan ...
Divergence at Inflection PointsEdit
When the initial guess is near a inflection point the method may diverge from the desired root.
The following code using the same implementation of Newton's method (assume the newton function is imported) demonstrates divergence at an inflection point (x=0).
from sympy import Symbol, diff x = Symbol('x') y = (x-1)**3 + 0.5 derivative = diff(y, x) newton(y, derivative, 5, 0.005, 15)
$ python newton.py current x: 3.65625000000000 error: 0.367521367521368 current x: 2.74721164936563 error: 0.330894909696631 current x: 2.11021215705302 error: 0.301865141940137 current x: 1.60492272680972 error: 0.314837232847788 current x: 0.947823175470885 error: 0.693272298403532 current x: -60.2548036313237 error: 1.01573025084058 current x: -39.8365801731819 error: 0.512549605648313 current x: -26.2244867245776 error: 0.519060433539279 current x: -17.1498826852607 error: 0.529135050417335 current x: -11.1004277326071 error: 0.544974941360464 current x: -7.06809009701998 error: 0.570498901434099 current x: -4.38128712811798 error: 0.613245124168828 current x: -2.59328016409484 error: 0.689476975445585 current x: -1.40842833626215 error: 0.841258157995587 current x: -0.634351912031382 error: 1.22026340513730
Secant method is similar to Newton's method in that it is an open method and use a intersection to get the improved estimate of the root. Secant method avoids calculating the first derivatives by estimating the derivative values using the slope of a secant line.
The formula for secant method is as follows:
The false-position method is similar to the bisection method in that it requires two initial guesses (bracketing method). Instead of using the midpoint as the improved guess, the false-position method use the root of secant line that passes both end points. The following figure illustrates the idea.
The formula for false-position method is as follows: