Value of Vandermonde Determinant/Formulation 1/Proof 1
Theorem
Let $V_n$ be the Vandermonde determinant of order $n$ defined as the following formulation:
- $V_n = \begin {vmatrix} 1 & x_1 & {x_1}^2 & \cdots & {x_1}^{n - 2} & {x_1}^{n - 1} \\ 1 & x_2 & {x_2}^2 & \cdots & {x_2}^{n - 2} & {x_2}^{n - 1} \\ \vdots & \vdots & \vdots & \ddots & \vdots & \vdots \\ 1 & x_n & {x_n}^2 & \cdots & {x_n}^{n - 2} & {x_n}^{n - 1} \end {vmatrix}$
Its value is given by:
- $\ds V_n = \prod_{1 \mathop \le i \mathop < j \mathop \le n} \paren {x_j - x_i}$
Proof
Let:
- $V_n = \begin{vmatrix} 1 & x_1 & {x_1}^2 & \cdots & {x_1}^{n - 2} & {x_1}^{n - 1} \\ 1 & x_2 & {x_2}^2 & \cdots & {x_2}^{n - 2} & {x_2}^{n - 1} \\ 1 & x_3 & {x_3}^2 & \cdots & {x_3}^{n - 2} & {x_3}^{n - 1} \\ \vdots & \vdots & \vdots & \ddots & \vdots & \vdots \\ 1 & x_{n - 1} & {x_{n - 1} }^2 & \cdots & {x_{n - 1} }^{n - 2} & {x_{n - 1} }^{n - 1} \\ 1 & x_n & {x_n}^2 & \cdots & {x_n}^{n - 2} & {x_n}^{n - 1} \end{vmatrix}$
By Multiple of Row Added to Row of Determinant, we can subtract row 1 from each of the other rows and leave $V_n$ unchanged:
- $V_n = \begin{vmatrix} 1 & x_1 & {x_1}^2 & \cdots & {x_1}^{n - 2} & {x_1}^{n - 1} \\ 0 & x_2 - x_1 & {x_2}^2 - {x_1}^2 & \cdots & {x_2}^{n - 2} - {x_1}^{n - 2} & {x_2}^{n - 1} - {x_1}^{n - 1} \\ 0 & x_3 - x_1 & {x_3}^2 - {x_1}^2 & \cdots & {x_3}^{n - 2} - {x_1}^{n - 2} & {x_3}^{n - 1} - {x_1}^{n - 1} \\ \vdots & \vdots & \vdots & \ddots & \vdots & \vdots \\ 0 & x_{n-1} - x_1 & {x_{n - 1} }^2 - {x_1}^2 & \cdots & {x_{n - 1} }^{n - 2} - {x_1}^{n - 2} & {x_{n - 1} }^{n - 1} - {x_1}^{n - 1} \\ 0 & x_n - x_1 & {x_n}^2 - {x_1}^2 & \cdots & {x_n}^{n - 2} - {x_1}^{n - 2} & {x_n}^{n - 1} - {x_1}^{n - 1} \end{vmatrix}$
Similarly without changing the value of $V_n$, we can subtract, in order:
and so on, till we subtract:
The first row will vanish all apart from the first element $a_{11} = 1$.
On all the other rows, we get, with new $i$ and $j$:
- $a_{i j} = \paren {x_i^{j - 1} - x_1^{j - 1} } - \paren {x_1 x_i^{j - 2} - x_1^{j - 1} } = \paren {x_i - x_1} x_i^{j - 2}$:
- $V_n = \begin {vmatrix} 1 & 0 & 0 & \cdots & 0 & 0 \\ 0 & x_2 - x_1 & \paren {x_2 - x_1} x_2 & \cdots & \paren {x_2 - x_1} {x_2}^{n - 3} & \paren {x_2 - x_1} {x_2}^{n - 2} \\ 0 & x_3 - x_1 & \paren {x_3 - x_1} x_3 & \cdots & \paren {x_3 - x_1} {x_3}^{n - 3} & \paren {x_3 - x_1} {x_3}^{n - 2} \\ \vdots & \vdots & \vdots & \ddots & \vdots & \vdots \\ 0 & x_{n - 1} - x_1 & \paren {x_{n - 1} - x_1} x_{n - 1} & \cdots & \paren {x_{n - 1} - x_1} {x_{n - 1} }^{n - 3} & \paren {x_{n - 1} - x_1} {x_{n - 1} }^{n - 2} \\ 0 & x_n - x_1 & \paren {x_n - x_1} x_n & \cdots & \paren {x_n - x_1} {x_n}^{n - 3} & \paren {x_n - x_1} {x_n}^{n - 2} \end {vmatrix}$
For all rows apart from the first, the $k$th row has the constant factor $\paren {x_k - x_1}$.
So we can extract all these as factors, and from Determinant with Row Multiplied by Constant, we get:
- $\ds V_n = \prod_{k \mathop = 2}^n \paren {x_k - x_1} \begin {vmatrix} 1 & 0 & 0 & \cdots & 0 & 0 \\ 0 & 1 & x_2 & \cdots & {x_2}^{n - 3} & {x_2}^{n - 2} \\ 0 & 1 & x_3 & \cdots & {x_3}^{n - 3} & {x_3}^{n - 2} \\ \vdots & \vdots & \vdots & \ddots & \vdots & \vdots \\ 0 & 1 & x_{n - 1} & \cdots & {x_{n - 1} }^{n - 3} & {x_{n - 1} }^{n - 2} \\ 0 & 1 & x_n & \cdots & {x_n}^{n - 3} & {x_n}^{n - 2} \end {vmatrix}$
From Determinant with Unit Element in Otherwise Zero Row, we can see that this directly gives us:
- $\ds V_n = \prod_{k \mathop = 2}^n \paren {x_k - x_1} \begin {vmatrix} 1 & x_2 & \cdots & {x_2}^{n - 3} & {x_2}^{n - 2} \\ 1 & x_3 & \cdots & {x_3}^{n - 3} & {x_3}^{n - 2} \\ \vdots & \vdots & \ddots & \vdots & \vdots \\ 1 & x_{n - 1} & \cdots & {x_{n - 1} }^{n - 3} & {x_{n - 1} }^{n - 2} \\ 1 & x_n & \cdots & {x_n}^{n - 3} & {x_n}^{n - 2} \end{vmatrix}$
and it can be seen that:
- $\ds V_n = \prod_{k \mathop = 2}^n \paren {x_k - x_1} V_{n - 1}$
$V_2$, by the time we get to it (it will concern elements $x_{n - 1}$ and $x_n$), can be calculated directly using the formula for calculating a Determinant of Order 2:
- $V_2 = \begin {vmatrix} 1 & x_{n - 1} \\ 1 & x_n \end {vmatrix} = x_n - x_{n - 1}$
The result follows.
$\blacksquare$
Sources
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- 1997: Donald E. Knuth: The Art of Computer Programming: Volume 1: Fundamental Algorithms (3rd ed.) ... (previous) ... (next): $\S 1.2.3$: Sums and Products: Exercise $37$