Wilson Matrix
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Wilson matrix is the following 4\times 4 matrix having integers as elements: ::W = \begin5&7&6&5 \\ 7&10&8&7 \\ 6&8&10&9 \\ 5&7&9&10\end This is the coefficient matrix of the following system of linear equations considered in a paper by J. Morris published in 1946: :: \text\quad \begin 5x+7y+6z+5u & = 23\\ 7x+10y+8z+7u & = 32\\ 6x+8y+10z+9u&=33\\ 5x+7y+9z+10u&=31 \end Morris ascribes the source of the set of equations to one T. S. Wilson but no details about Wilson have been provided. The particular system of equations was used by Morris to illustrate the concept of ill-conditioned system of equations. The matrix W has been used as an example and for test purposes in many research papers and books over the years. John Todd has referred to W as “the notorious matrix W of T. S. Wilson”.


Properties

#W is a symmetric matrix. #W is positive definite. #The determinant of W is 1. #The
inverse Inverse or invert may refer to: Science and mathematics * Inverse (logic), a type of conditional sentence which is an immediate inference made from another conditional sentence * Additive inverse (negation), the inverse of a number that, when ad ...
of W is W^ = \begin 68 & -41 & -17 & 10\\ -41 & 25 & 10 & -6 \\ -17 & 10 & 5 &- 3 \\ 10 & -6 & -3 & 2 \end #The
characteristic polynomial In linear algebra, the characteristic polynomial of a square matrix is a polynomial which is invariant under matrix similarity and has the eigenvalues as roots. It has the determinant and the trace of the matrix among its coefficients. The chara ...
of W is \lambda^4-35 \lambda^3+146 \lambda^2-100 \lambda+1. #The
eigenvalues In linear algebra, an eigenvector () or characteristic vector of a linear transformation is a nonzero vector that changes at most by a scalar factor when that linear transformation is applied to it. The corresponding eigenvalue, often denoted b ...
of W are \quad 0.01015004839789187,\quad 0.8431071498550294,\quad 3.858057455944953,\quad 30.28868534580213. #Since W is symmetric, the 2-norm
condition number In numerical analysis, the condition number of a function measures how much the output value of the function can change for a small change in the input argument. This is used to measure how sensitive a function is to changes or errors in the input ...
of W is \kappa_2(W)= (\text)/(\text)=30.28868534580213/0.01015004839789187 = 2984.09270167549. #The solution of the system of equations (S1) is x=y=z=u=1. #The Cholesky factorisation of W is W= R^TR where R =\begin \sqrt & \frac & \frac & \sqrt \\ 0 & \frac & -\frac & 0 \\ 0 & 0 & \sqrt & \frac \\ 0 & 0 & 0 & \frac\end. #W has the factorisation W= LDL^T where L = \begin 1 & 0 & 0 & 0 \\ \frac & 1 & 0 & 0 \\ \frac & -2 & 1 & 0 \\ 1 & 0 & \frac & 1 \end, \quad D=\begin 5 & 0 & 0 & 0 \\ 0 & \frac & 0 & 0 \\ 0 & 0 & 2 & 0 \\ 0 & 0 & 0 & \frac \end . #W has the factorisation W=Z^TZ with Z as the integer matrix Z= \begin 2 & 3 & 2 & 2 \\ 1 & 1 & 2 & 1 \\ 0 & 0 & 1 & 2 \\ 0 & 0 & 1 & 1 \end .


Research problems spawned by Wilson matrix

A consideration of the condition number of the Wilson matrix has spawned several interesting research problems relating to condition numbers of matrices in certain special classes of matrices having some or all the special features of the Wilson matrix. In particular, the following special classes of matrices have been studied: #S= the set of 4 \times 4 nonsingular, symmetric matrices with integer entries between 1 and 10. # P = the set of 4 \times 4 positive definite, symmetric matrices with integer entries between 1 and 10. An exhaustive computation of the condition numbers of the matrices in the above sets has yielded the following results: #Among the elements of S, the maximum condition number is 7.6119\times 10^4 and this maximum is attained by the matrix \begin 2 & 7 & 10 & 10 \\ 7 & 10 & 10 & 9 \\ 10 & 10 & 10 & 1 \\ 10 & 9 & 1 & 10 \end . #Among the elements of P, the maximum condition number is 3.5529 \times 10^4 and this maximum is attained by the matrix \begin 9 & 1 & 1 & 5 \\ 1 & 10 & 1 & 9 \\ 1 & 1 & 10 & 1 \\ 5 & 9 & 1 & 10 \end .


References

{{reflist Matrices