Morgenstern first defines the notion of a linear algorithm. A linear algorithm gets as input $x_1,\ldots,x_n$ and its goal is to compute some $y_1,\ldots,y_m$, each of which is a (specific) linear combination of $x_i$s. The algorithm proceeds in steps, starting with step $n+1$. At step $t$, the algorithm computes $x_t = \lambda_t x_i + \mu_t x_j$ for some $i,j < t$. At the end of the computation, for each $i$, $y_i = x_j$ for some $j$.
For example, here is an algorithm computing the unnormalized DFT on 2 variables:
$$
x_3 \gets x_1 + x_2 \\
x_4 \gets x_1 - x_2
$$
Similarly, the unnormalized two dimensional DFT on $2^2$ variables is computed by:
$$
x_5 \gets x_1 + x_2 \\
x_6 \gets x_1 - x_2 \\
x_7 \gets x_3 + x_4 \\
x_8 \gets x_3 - x_4 \\
x_9 \gets x_5 + x_7 \\
x_{10} \gets x_5 - x_7 \\
x_{11} \gets x_6 + x_8 \\
x_{12} \gets x_6 - x_8
$$
We can view each $x_t$ as an $n$-dimensional vector which gives the linear combination of $x_1,\ldots,x_n$ producing $x_t$; call this vector $v_t$. The vectors $v_1,\ldots,v_n$ are just the $n$ basis vectors.
Morgenstern defines the quantity $\Delta_t$, which is the maximum magnitude of the determinant of any square submatrix of the matrix $V_t$ whose rows are $v_1,\ldots,v_t$.
Lemma. Let $c \geq 1/2$. If $|\lambda_s|,|\mu_s| \leq c$ for all $s$ then $\Delta_{n+t} \leq (2c)^t$.
Proof. The proof is by induction on $t$. When $t = 0$, this is easy to verify directly since $V_n$ is just the identity matrix. Consider now any $t > 0$. Every square submatrix of $V_t$ is either a square submatrix of $V_{t-1}$, in which case its determinant is at most $(2c)^{t-1} \leq (2c)^t$ by induction, or it involves the new row $v_t = \lambda_t v_i + \mu_t v_j$. In the latter case, we can write the square submatrix $A$ as $A = \lambda_t B + \mu_t C$, where $B,C$ are square submatrices of $V_{t-1}$ (replace the relevant part of $v_t$ by the corresponding parts of $v_i$ and $v_j$). Since the determinant is a linear function of any of its rows, $\det(A) = \lambda_t \det(B) + \mu_t(C)$. By induction, $|\det(B)|,|\det(C)| \leq (2c)^{t-1}$, and so $$|\det(A)| \leq |\lambda_t| |\det(B)| + |\mu_t| |\det(C)| \leq c(2c)^{t-1} + c(2c)^{t-1} = (2c)^t.$$
Corollary. Computing the DFT on $n$ variables using a linear algorithm with bounded coefficients requires $\Omega(n\log n)$ steps.
Proof. The determinant of the DFT matrix is $n^{n/2}$. Hence any linear algorithm computing the DFT in $t$ steps satisfies $\Delta_t \geq n^{n/2}$. If the bound on the coefficients is $c$, then the lemma shows that $(2c)^t \geq n^{n/2}$ and so $t = \Omega(n\log n)$.
Remark. Strassen has shown that any algebraic algorithm (algorithm involving $+,-,\cdot,/$) for computing the DFT can be transformed to a linear algorithm using the same number of steps.
