# $n+1$ points in $\mathbb{R}^n$ with pairwise rational distances are linearly dependent

Let $v_0$ be the zero vector in $\mathbb{R}^n$ and let $v_1, v_2, . . . , v_{n+1}$ be vectors in $\mathbb{R}^n$ such that the Euclidean norm $|v_i − v_j|$ is rational for every $0 ≤ i, j ≤ n + 1$. Prove that $v_1, . . . , v_{n+1}$ are linearly dependent over the rationals.

I was reading an ingenious proof of this result, an outline of which I present here:

1. "by passing to a subspace", we may assume that $v_1,...,v_n$ are linearly independent over $\mathbb{R}$

2. since $v_1,...,v_{n+1}$ is a family of $n+1$ vectors in the $n$- dimmensional space $\mathbb{R}^n$ (considered over the field $\mathbb{R}$), they are linearly dependent over $\mathbb{R}$. Hence write $v_{n+1}=\sum_{k=1}^n\lambda_kv_k$. The goal is to show that $\lambda_k$ is rational.

3. by the parrallelogram inequality, all the scalar products $<v_i,v_j>$ are rational. Hence the Grammian matrix $G$ of $v_1,..,v_n$ has all entries rational. Furthermore, since $v_1,...,v_n$ are linearly independent $G$ is invertible (well-known property of the Grammian)

4. Let $w$ denote the column vector of size $n$ with coordinate $k$ given by $<v_{n+1},v_k>$. Let $\lambda$ denote the column vector of size $n$ with coordinate $k$ given by $\lambda_k$. Then we have $w=G\lambda$.

5. From the above, $\lambda=G^{-1}w$ which is a product of matrices with rational entries, thus a rational matrix. done.

The only step I don't understand is step 1. Why do we have the right to assume $v_1,...,v_n$ are linearly independent over $\mathbb{R}$, and which subspace are we "passing to"?

• You should first replace $\mathbb{R}^n$ by an arbitrary inner product space $V$ over $\mathbb{R}$. This extra generality is needed for the argument to work. Now, if the span of $v_1, v_2, \ldots, v_{n+1}$ has dimension $< n$, then you can always replace the inner product space $V$ by this span while throwing away as many of your vectors as the dimension you are losing (i.e., if the span has dimension $n-i$, then you throw away the vectors $v_{i+2}, v_{i+3}, \ldots, v_{n+1}$). Thus, you can WLOG assume that the span of $v_1, v_2, \ldots, v_{n+1}$ has dimension $n$. Therefore, ... – darij grinberg Jul 22 '17 at 22:59
• ... some $n$ among the $n+1$ vectors $v_1, v_2, \ldots, v_{n+1}$ are linearly independent. Assume WLOG that the first $n$ vectors $v_1, v_2, \ldots, v_n$ are linearly independent (otherwise, just permute them appropriately). This is what constitutes step 1. – darij grinberg Jul 22 '17 at 23:00
• @darijgrinberg : sorry can you elaborate a bit more I don't understand why we are done if $v_1,...,v_{n+1}$ are not linearly independent. Why can we "throw away" those vectors? – Joshua Benabou Jul 22 '17 at 23:59
• Well, you don't need to care about them. If you can show that $v_1, v_2, \ldots, v_{i+1}$ are linearly dependent, then $v_1, v_2, \ldots, v_{n+1}$ will be linearly dependent a fortiori. – darij grinberg Jul 23 '17 at 0:08
• Sorry, I still don't understand. $v_1,v_2,…,v_{n+1}$ are in any case linearly dependent over $\mathbb{R}$... – Joshua Benabou Jul 23 '17 at 0:15

If $v_1, \dots v_n$ are linearly dependent, then instead of working in $\mathbb{R}^n$ we can work in $\text{span}(v_1, \dots v_n)$, which is some real inner product space of dimension $\le n-1$. So we can argue by induction on $n$: if we've already proven the result for inner product spaces of dimension $\le n-1$, then we can apply it to $v_1, \dots v_n$. Of course it would be nice to be more explicit about the base case here.