Jervis Muindi
Numerical Algorithms and Complexity
Jenkins-Traub Algorithm
May 2013

Introduction

This project contains code that provides an implementation of the Jenkin-Traub Algorithm.

This algorithm is a faster than Newton method (which has order 2, i.e. quadratic, convergence) for finding the roots of polynomials. It can handle complex coefficients and roots and does not suffer from stability issues.

For an overview of the algorithm, the Wikipedia article on the algorithm is a good starting point. More complete details can be found in the original Paper written by Jenkins and Traub in 1968 and published in 1970.

Requirements

This was tested on Python 2.7+

How to Run

The program accepts two parameters one of which is required. These are described below.

   python main.py -p [Polynomial] -e [error]
         Where:
         [Polynomial] Required Parameter. This is a listing of all coefficients of the polynomial.
                      The listing goes from the highest degree term and proceed to lower degree terms.
                      All coefficients of all terms must be explicitly listed.
                      The symbol "j" must be used to denote a complex number coefficent. Example: 1+2j
                      Lastly, remember to use a space to separate the numbers.
         [error] Error bound to use on the roots. This is an optional parameter.
                 Tt defaults to 10^(-6) if not specified

For Example:
To find the roots of the polynomial x^5 - 9.01*x^4 + (27.08)*x^3 -(41.19)*x^2 + (32.22)*x + - 10.1 You can do the following python main.py -p 1 -9.01 27.08 -41.19 32.22 -10.1

Sample Output

Below is some sample output obtained from running the program on the suggested polynomial above: Polynomial = x^5 - 9.01*x^4 + (27.08)*x^3 -(41.19)*x^2 + (32.22)*x + - 10.1

Finding Roots for the Polynomial:
 (1.0+(0.0*I))*x^5 + (-9.01+(0.0*I))*x^4 + (27.08+(0.0*I))*x^3 + (-41.
19+(0.0*I))*x^2 + (32.22+(0.0*I))*x^1 + (-10.1+(0.0*I))*x^0

Using Error Value of: 1e-06

Starting Root Search ...
Success Stage Two terminated correctly at L = 1
Stage 3 was successful
Root is estimated to be at (0.999853310377+5.99742837711e-05j)
Success Stage Two terminated correctly at L = 1
Stage 3 was successful
Root is estimated to be at (1.01014824065-5.93584772307e-05j)
Success Stage Two terminated correctly at L = 3
Stage 3 was successful
Root is estimated to be at (0.999999646176+1.0000002704j)
Success Stage Two terminated correctly at L = 1
Stage 3 was successful
Root is estimated to be at (0.999998897167-1.00000085204j)
Success Stage Two terminated correctly at L = 1
Stage 3 was successful
Root is estimated to be at (4.99999990563-3.41668142667e-08j)
Root Search Complete

*********************

For The Polynomial
(1.0+(0.0*I))*x^5 + (-9.01+(0.0*I))*x^4 + (27.08+(0.0*I))*x^3 + (-41.1
9+(0.0*I))*x^2 + (32.22+(0.0*I))*x^1 + (-10.1+(0.0*I))*x^0
The roots found in order of increasing magnitude are:
1) (0.999853310377+5.99742837711e-05j)
2) (1.01014824065-5.93584772307e-05j)
3) (0.999999646176+1.0000002704j)
4) (0.999998897167-1.00000085204j)
5) (4.99999990563-3.41668142667e-08j)

Testing

A unit testing methodology was followed in the development of this program. Tests written can be found in the poly_test.py file. You can run all the tests by executing that file and ensuring that everything is working as expected.

$ python poly_test.py