Home
Biographies in Optimization
Contact Us
Calculus of Inequalities
Rick Chartrand
Chromosome Structure EDM
Compressed Sensing
Conic Independence
Convex Optimization
Convex Optimization Group
Convex Cones
Convex, Affine, Conic Hulls
Convex Functions
Convex Geometry
Convex Iteration
Distance Geometry
Distance Matrix Cone
Dual Cones
Duality Gap
Eigenvalues/Eigenvectors
Elliptope and Fantope
Euclidean Distance Matrices
EDM cone faces
Extreme Directions
Face Recognition
Farkas Lemma
Fifth Metric Property
Jensen's Inequality
Jobs in Optimization
Kissing Number
Linear Algebra
Linear Matrix Inequality
Manifold Learning
MATLAB for Optimization
Matrix Calculus
Molecular Conformation
Isaac Newton
Angelia Nedic
Open Problems
Positive Semidefinite Cone
Projection
Projection on Cone
Proximity Problems
Quasiconvex Functions
Rank Constraint
Rockafellar
Justin Romberg
Michael Saunders
Schoenberg Criterion
Semidefinite Programming
Sensor Network Localization
SEO Consultant
Smallest Simplex
Software Download
Talks on Optimization
Video
Wikimization
Wikimization     Meboo     Video     Download     Contact     
Felice crystal
Home
Olifilth Oli Filth Wikipedia Wiki

Chien's search Oli Filth Olifilth Wikipedia, Oliver Charlesworth

by Wikipedia editor/administrator Olifilth

a.k.a

This e-mail address is being protected from spam bots, you need JavaScript enabled to view it  (nonworking)

Oli Filth

Oliver Charlesworth
8 Moncrieff Close - 2nd floor
Cambridge  CB4 2UY  United Kingdom (UK)

auto: AF03 BXL


Contact Author: Oli Filth Olifilth

phone: (01223) 696552
From the US:
01144 (1223) 696552 


Oliver Charlesworth
British Telephone Company
Olifilth Oli Filth Wikipedia Wiki

 
Introduction

In abstract algebra, Chien's search is a recursive algorithm

for determining roots of polynomials defined over a finite field.

The most typical use of Chien's search is in finding the roots of

error-locator polynomials encountered in decoding

Reed-Solomon codes and BCH codes.

 

Algorithm

We denote the polynomial whose roots we wish to determine as

\ \Lambda(x) = \lambda_0 + \lambda_1 x + \lambda_2 x^2 + \ldots + \lambda_t x^t

Conceptually, we may evaluate \ \Lambda(\beta) for each

non-zero \ \beta in our finite field GF(q).

Those resulting in 0 are roots of the polynomial.

Chien's search is based on two observations:

  • Each non-zero \ \beta may be expressed as \ \alpha^{i_\beta} for some \ i_\beta
  • where \ \alpha is the primitive element of GF(q).
  •  
  • Therefore, the powers \ \alpha^i for \ 0 \leq i < (N-1)
  • cover the entire field (excluding the zero element).
  •  
  • The following relationship exists:

\begin{array}{lllllllllll}
 \Lambda(\alpha^i) &=& \lambda_0 &+& \lambda_1 \alpha^i &+& \lambda_2 (\alpha^i)^2 &+& \ldots &+& \lambda_t (\alpha^i)^t  \\
                   &\triangleq& \gamma_{0,i} &+& \gamma_{1,i} &+& \gamma_{2,i} &+& \ldots &+& \gamma_{t,i}
\end{array}

\begin{array}{lllllllllll}
 \Lambda(\alpha^{i+1}) &=& \lambda_0 &+& \lambda_1 \alpha^{i+1} &+& \lambda_2 (\alpha^{i+1})^2 &+& \ldots &+& \lambda_t (\alpha^{i+1})^t  \\
                       &=& \lambda_0 &+& \lambda_1 (\alpha^i)\,\alpha &+& \lambda_2 (\alpha^i)^2\,\alpha^2 &+& \ldots &+& \lambda_t (\alpha^i)^t\,\alpha^t  \\
                       &=& \gamma_{0,i} &+& \gamma_{1,i}\,\alpha &+& \gamma_{2,i}\,\alpha^2 &+& \ldots &+& \gamma_{t,i}\,\alpha^t \\
                   &\triangleq& \gamma_{0,i+1} &+& \gamma_{1,i+1} &+& \gamma_{2,i+1} &+& \ldots &+& \gamma_{t,i+1}
\end{array}
     

In other words, we may define each \ \Lambda(\alpha^i)

as the sum of a set of terms \ \{\gamma_{j,i} | 0\leq j \leq t\},

from which the next set of coefficients may be derived thus:

\ \gamma_{j,i+1} = \gamma_{j,i}\,\alpha^i

In this way, we may start at \ i = 0 with \ \gamma_{j,0} = \lambda_j

and iterate through each value of \ i up to \ (N-1).

If at any stage the resultant summation is zero, i.e.

\ \sum_{j=0}^t \gamma_{j,i} = 0

then \ \Lambda(\alpha^i) = 0 also, so \ \alpha_i is a root.

In this way, we check every element in the field.

When implemented in hardware,

this approach significantly reduces the complexity,

as all multiplications consist of one variable and one constant,

rather than two variables as in the brute-force approach.

References

  • R.T. Chien, "Cyclic Decoding Procedure for the
  • Bose-Chaudhuri-Hocquenghem Codes,"
  • IEEE Transactions on Information Theory,
  • Vol. IT-10, pp. 357-363, October 1964.
  •  
  • S. Lin and D. Costello. Error Control Coding:
  • Fundamentals and Applications.
  • Prentice-Hall, Englewood Cliffs, NJ, 2004.
 
 

Course,   Videos,
To Download page

Convex Optimization

by Stephen Boyd 
& L. Vandenberghe 


To Download page

Dattorro

by Dattorro


Course

Bertsekas

by Dimitri Bertsekas 


See Inside

Hiriart-Urruty & Lemaréchal

by Hiriart-Urruty
& Lemaréchal


See Inside

Rockafellar

by Rockafellar

Optimization Newsletter
Subscription:

Email:

Receive HTML mailings?
Subscribe Unsubscribe