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Lehmer matrix

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In mathematics, particularly matrix theory, the n×ばつn Lehmer matrix (named after Derrick Henry Lehmer) is the constant symmetric matrix defined by

A i j = { i / j , j i j / i , j < i . {\displaystyle A_{ij}={\begin{cases}i/j,&j\geq i\\j/i,&j<i.\end{cases}}} {\displaystyle A_{ij}={\begin{cases}i/j,&j\geq i\\j/i,&j<i.\end{cases}}}

Alternatively, this may be written as

A i j = min ( i , j ) max ( i , j ) . {\displaystyle A_{ij}={\frac {{\mbox{min}}(i,j)}{{\mbox{max}}(i,j)}}.} {\displaystyle A_{ij}={\frac {{\mbox{min}}(i,j)}{{\mbox{max}}(i,j)}}.}

Properties

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As can be seen in the examples section, if A is an n×ばつn Lehmer matrix and B is an ×ばつm Lehmer matrix, then A is a submatrix of B whenever m>n. The values of elements diminish toward zero away from the diagonal, where all elements have value 1.

The inverse of a Lehmer matrix is a tridiagonal matrix, where the superdiagonal and subdiagonal have strictly negative entries. Consider again the n×ばつn A and ×ばつm B Lehmer matrices, where m>n. A rather peculiar property of their inverses is that A−1 is nearly a submatrix of B−1, except for the A−1n,n element, which is not equal to B−1n,n.

A Lehmer matrix of order n has trace n.

Examples

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The ×ばつ2, ×ばつ3 and ×ばつ4 Lehmer matrices and their inverses are shown below.

A 2 = ( 1 1 / 2 1 / 2 1 ) ; A 2 1 = ( 4 / 3 2 / 3 2 / 3 4 / 3 ) ; A 3 = ( 1 1 / 2 1 / 3 1 / 2 1 2 / 3 1 / 3 2 / 3 1 ) ; A 3 1 = ( 4 / 3 2 / 3 2 / 3 32 / 15 6 / 5 6 / 5 9 / 5 ) ; A 4 = ( 1 1 / 2 1 / 3 1 / 4 1 / 2 1 2 / 3 1 / 2 1 / 3 2 / 3 1 3 / 4 1 / 4 1 / 2 3 / 4 1 ) ; A 4 1 = ( 4 / 3 2 / 3 2 / 3 32 / 15 6 / 5 6 / 5 108 / 35 12 / 7 12 / 7 16 / 7 ) . {\displaystyle {\begin{array}{lllll}A_{2}={\begin{pmatrix}1&1/2\1円/2&1\end{pmatrix}};&A_{2}^{-1}={\begin{pmatrix}4/3&-2/3\\-2/3&{\color {Brown}{\mathbf {4/3} }}\end{pmatrix}};\\\\A_{3}={\begin{pmatrix}1&1/2&1/3\1円/2&1&2/3\1円/3&2/3&1\end{pmatrix}};&A_{3}^{-1}={\begin{pmatrix}4/3&-2/3&\\-2/3&32/15&-6/5\\&-6/5&{\color {Brown}{\mathbf {9/5} }}\end{pmatrix}};\\\\A_{4}={\begin{pmatrix}1&1/2&1/3&1/4\1円/2&1&2/3&1/2\1円/3&2/3&1&3/4\1円/4&1/2&3/4&1\end{pmatrix}};&A_{4}^{-1}={\begin{pmatrix}4/3&-2/3&&\\-2/3&32/15&-6/5&\\&-6/5&108/35&-12/7\\&&-12/7&{\color {Brown}{\mathbf {16/7} }}\end{pmatrix}}.\\\end{array}}} {\displaystyle {\begin{array}{lllll}A_{2}={\begin{pmatrix}1&1/2\1円/2&1\end{pmatrix}};&A_{2}^{-1}={\begin{pmatrix}4/3&-2/3\\-2/3&{\color {Brown}{\mathbf {4/3} }}\end{pmatrix}};\\\\A_{3}={\begin{pmatrix}1&1/2&1/3\1円/2&1&2/3\1円/3&2/3&1\end{pmatrix}};&A_{3}^{-1}={\begin{pmatrix}4/3&-2/3&\\-2/3&32/15&-6/5\\&-6/5&{\color {Brown}{\mathbf {9/5} }}\end{pmatrix}};\\\\A_{4}={\begin{pmatrix}1&1/2&1/3&1/4\1円/2&1&2/3&1/2\1円/3&2/3&1&3/4\1円/4&1/2&3/4&1\end{pmatrix}};&A_{4}^{-1}={\begin{pmatrix}4/3&-2/3&&\\-2/3&32/15&-6/5&\\&-6/5&108/35&-12/7\\&&-12/7&{\color {Brown}{\mathbf {16/7} }}\end{pmatrix}}.\\\end{array}}}

See also

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References

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  • Newman, M.; Todd, J. (1958). "The evaluation of matrix inversion programs". Journal of the Society for Industrial and Applied Mathematics. 6 (4): 466–476. doi:10.1137/0106030. JSTOR 2098717.
Matrix classes
Explicitly constrained entries
Constant
Conditions on eigenvalues or eigenvectors
Satisfying conditions on products or inverses
With specific applications
Used in statistics
Used in graph theory
Used in science and engineering
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