Definition:Cauchy Determinant

Theorem
Let $$C_n$$ be a square Cauchy matrix of order $n$ given by:


 * $$\begin{bmatrix}

\dfrac 1 {x_1 + y_1} & \dfrac 1 {x_1 + y_2} & \cdots & \dfrac 1 {x_1 + y_n} \\ \dfrac 1 {x_2 + y_1} & \dfrac 1 {x_2 + y_2} & \cdots & \dfrac 1 {x_2 + y_n} \\ \vdots & \vdots & \ddots & \vdots \\ \dfrac 1 {x_m + y_1} & \dfrac 1 {x_m + y_2} & \cdots & \dfrac 1 {x_m + y_n} \\ \end{bmatrix}$$

Then the determinant of $$C_n$$ is given by:


 * $$\det \left({C_n}\right) = \dfrac {\displaystyle \prod_{1 \le i < j \le n} \left({x_j - x_i}\right) \left({y_j - y_i}\right)} {\displaystyle \prod_{1 \le i, j \le n} \left({x_i + y_j}\right)}$$

If $$C_n$$ is given by:


 * $$\begin{bmatrix}

\dfrac 1 {x_1 - y_1} & \dfrac 1 {x_1 - y_2} & \cdots & \dfrac 1 {x_1 - y_n} \\ \dfrac 1 {x_2 - y_1} & \dfrac 1 {x_2 - y_2} & \cdots & \dfrac 1 {x_2 - y_n} \\ \vdots & \vdots & \ddots & \vdots \\ \dfrac 1 {x_m - y_1} & \dfrac 1 {x_m - y_2} & \cdots & \dfrac 1 {x_m - y_n} \\ \end{bmatrix}$$

then its determinant is given by:


 * $$\det \left({C_n}\right) = \dfrac {\displaystyle \prod_{1 \le i < j \le n} \left({x_j - x_i}\right) \left({y_j - y_i}\right)} {\displaystyle \prod_{1 \le i, j \le n} \left({x_i - y_j}\right)}$$

Proof
Take the version of the Cauchy matrix defined such that $$a_{ij} = \frac 1 {x_i + y_j}$$.

Subtract column 1 from each of columns 2 to $$n$$.

Thus:

$$ $$ $$

From Multiple of Row Added to Row of Determinant this will have no effect on the value of the determinant.

Now:
 * extract the factor $$\frac 1 {x_i + y_1}$$ from each row $$1 \le i \le n$$;
 * extract the factor $$y_1 - y_j$$ from each column $$2 \le j \le n$$.

Thus from Determinant with Row Multiplied by Constant we have the following:


 * $$\det \left({C_n}\right) = \left({\prod_{i = 1}^n \frac 1 {x_i + y_1}}\right) \left({\prod_{j = 2}^n y_1 - y_j}\right) \begin{vmatrix}

1 & \dfrac 1 {x_1 + y_2} & \dfrac 1 {x_1 + y_3} & \cdots & \dfrac 1 {x_1 + y_n} \\ 1 & \dfrac 1 {x_2 + y_2} & \dfrac 1 {x_2 + y_3} & \cdots & \dfrac 1 {x_2 + y_n} \\ 1 & \dfrac 1 {x_3 + y_2} & \dfrac 1 {x_3 + y_3} & \cdots & \dfrac 1 {x_3 + y_n} \\ \vdots & \vdots & \vdots & \ddots & \vdots \\ 1 & \dfrac 1 {x_m + y_2} & \dfrac 1 {x_m + y_3} & \cdots & \dfrac 1 {x_m + y_n} \\ \end{vmatrix}$$

Now subtract rwo 1 from each of rows 2 to $$n$$.

Column 1 will go to zero for all but the first row.

Columns 2 to $$n$$ will become:

$$ $$ $$

From Multiple of Row Added to Row of Determinant this will have no effect on the value of the determinant.

Now:
 * extract the factor $$x_1 - x_i$$ from each row $$2 \le i \le n$$;
 * extract the factor $$\frac 1 {x_1 + y_j}$$ from each column $$2 \le j \le n$$.

Thus from Determinant with Row Multiplied by Constant we have the following:


 * $$\det \left({C_n}\right) = \left({\prod_{i = 1}^n \frac 1 {x_i + y_1}}\right) \left({\prod_{j = 1}^n \frac 1 {x_1 + y_j}}\right) \left({\prod_{i = 2}^n x_1 - x_i}\right) \left({\prod_{j = 2}^n y_1 - y_j}\right) \begin{vmatrix}

1 & 1 & 1 & \cdots & 1 \\ 0 & \dfrac 1 {x_2 + y_2} & \dfrac 1 {x_2 + y_3} & \cdots & \dfrac 1 {x_2 + y_n} \\ 0 & \dfrac 1 {x_3 + y_2} & \dfrac 1 {x_3 + y_3} & \cdots & \dfrac 1 {x_3 + y_n} \\ \vdots & \vdots & \vdots & \ddots & \vdots \\ 0 & \dfrac 1 {x_m + y_2} & \dfrac 1 {x_m + y_3} & \cdots & \dfrac 1 {x_m + y_n} \\ \end{vmatrix}$$

From Determinant with Unit Element in Otherwise Zero Row, and tidying up the products, we get:


 * $$\det \left({C_n}\right) = \frac {\displaystyle \prod_{i = 2}^n \left({x_i - x_1}\right) \left({y_i - y_1}\right)} {\displaystyle \prod_{1 \le i, j \le n} \left({x_i + y_1}\right) \left({x_1 + y_j}\right)}

\begin{vmatrix} \dfrac 1 {x_2 + y_2} & \dfrac 1 {x_2 + y_3} & \cdots & \dfrac 1 {x_2 + y_n} \\ \dfrac 1 {x_3 + y_2} & \dfrac 1 {x_3 + y_3} & \cdots & \dfrac 1 {x_3 + y_n} \\ \vdots & \vdots & \ddots & \vdots \\ \dfrac 1 {x_m + y_2} & \dfrac 1 {x_m + y_3} & \cdots & \dfrac 1 {x_m + y_n} \\ \end{vmatrix}$$

Repeat the process for the remaining rows and columns $$2$$ to $$n$$.

The result follows.

A similar process obtains the result for the $$a_{ij} = \frac 1 {x_i - y_j}$$ form.