Determinant of a special $4\times 4$ matrix Let $f(x)=\sum_{k=1}^{4}a_{k}x^{k},\varepsilon =\cos\frac{\pi}{2}+i\sin\frac{\pi}{2}.$   
$\qquad\qquad 4\times 4$ matrix $$T=\begin{bmatrix}
 1&  a_{2}&  a_{3}& a_{4}\\ 
 1&  a_{1}&  a_{2}& a_{3}\\ 
 1&  a_{4}&  a_{1}& a_{2}\\ 
 0&  \varepsilon^{2}&  \varepsilon& 
1\end{bmatrix}$$
Show that $$\det(T)=f(\varepsilon^{2})f(\varepsilon^{3})$$

Further I can generalize this question :
Let $f(x)=\sum_{k=1}^{n}a_{k}x^{k},\varepsilon =\cos\frac{2\pi}{n}+i\sin\frac{2\pi}{n}.$   
$\qquad\qquad n\times n$ matrix $$T=\begin{bmatrix}
 1&  a_{2}&  a_{3} & \cdots & a_{n}\\ 
 1&  a_{1}&  a_{2}&\cdots & a_{n-1}\\ 
 \cdots&   \cdots&   \cdots&\cdots &  \cdots\\
 1&  a_{4}&  a_{5}& \cdots &a_{2}\\ 
 0&  \varepsilon^{n-2}&  \varepsilon^{n-3}& \cdots&
1\end{bmatrix}$$
Show that $$\det(T)=f(\varepsilon^{2})f(\varepsilon^{3}) \cdots f(\varepsilon^{n-1})$$

Let $$A=\begin{bmatrix} 
 1& 0&  a_{3}&  a_{4}& a_{1}\\
 0& 1&  a_{2}&  a_{3}& a_{4}\\ 
 0& 1&  a_{1}&  a_{2}& a_{3}\\ 
 0& 1&  a_{4}&  a_{1}& a_{2}\\ 
 0& 0&  \varepsilon^{2}&  \varepsilon& 
1\end{bmatrix}$$ then $\det(T)=\det(A)$.  Now add $\varepsilon^{2}$ of row 4 to row 1, add $\varepsilon^{4}$ of row 3 to row 1, add $\varepsilon^{6}$ of row 2 to row 1, we get
$$A=\begin{bmatrix} 
 1& 0&  a_{3}&  a_{4}& a_{1}\\
 0& 1&  a_{2}&  a_{3}& a_{4}\\ 
 0& 1&  a_{1}&  a_{2}& a_{3}\\ 
 0& 1&  a_{4}&  a_{1}& a_{2}\\ 
 0& 0&  \varepsilon^{2}&  \varepsilon& 
1\end{bmatrix}\longrightarrow \begin{bmatrix} 
 1& 0&  \varepsilon^{4}f(\varepsilon^{2})&  \varepsilon^{2}f(\varepsilon^{2})& f(\varepsilon^{2})\\
 0& 1&  a_{2}&  a_{3}& a_{4}\\ 
 0& 1&  a_{1}&  a_{2}& a_{3}\\ 
 0& 1&  a_{4}&  a_{1}& a_{2}\\ 
 0& 0&  \varepsilon^{2}&  \varepsilon& 
1\end{bmatrix}=A_{1}$$
How can I separate $f(\varepsilon^{2})$ from $\det(A_{1})$?
If you have another proof to my question,please give me some hints. Any help would be appreciated
 A: As we're looking for the determinant we may as well think about the transpose
$$ 
T^T=\begin{bmatrix}
      1&          1&          1&     \cdots&        1&     0\\ 
  a_{2}&      a_{1}&      a_{n}&     \cdots&    a_{4}&     \varepsilon^{n-2}\\ 
  a_{3}&      a_{2}&      a_{1}&     \cdots&    a_{5}&     \varepsilon^{n-3}\\ 
 \vdots&     \vdots&     \vdots&     \ddots&   \vdots&     \vdots\\
  a_{n}&    a_{n-1}&    a_{n-2}&     \cdots&    a_{2}&     1
\end{bmatrix} 
$$
Change the last column of $T^T$ to define $C$ as follows
$$
C=\begin{bmatrix}
      1&          1&          1&     \cdots&        1&     1\\ 
  a_{2}&      a_{1}&      a_{n}&     \cdots&    a_{4}&     a_{3}\\ 
  a_{3}&      a_{2}&      a_{1}&     \cdots&    a_{5}&     a_{4}\\ 
 \vdots&     \vdots&     \vdots&     \ddots&   \vdots&     \vdots\\
  a_{n}&    a_{n-1}&    a_{n-2}&     \cdots&    a_{2}&     a_{1}
\end{bmatrix}
$$
Now take a look at the system of linear equations in $x_j$
$$
Cx=\begin{bmatrix}
      1&          1&          1&     \cdots&        1&     1\\ 
  a_{2}&      a_{1}&      a_{n}&     \cdots&    a_{4}&     a_{3}\\ 
  a_{3}&      a_{2}&      a_{1}&     \cdots&    a_{5}&     a_{4}\\ 
 \vdots&     \vdots&     \vdots&     \ddots&   \vdots&     \vdots\\
  a_{n}&    a_{n-1}&    a_{n-2}&     \cdots&    a_{2}&     a_{1}
\end{bmatrix}
\begin{bmatrix}
x_1\\
x_2\\
x_3\\
\vdots\\
x_n
\end{bmatrix}=
\begin{bmatrix}
0\\
\varepsilon^{n-2}\\
\varepsilon^{n-3}\\
\vdots\\
1
\end{bmatrix}:=b
$$
By Cramer's rule
$$
\color{red}{x_n\det(C)=\det(T^T)=\det(T)}
$$
For $y_j=\varepsilon^{-j}$ we have $(Cy)_1=0$ and for $k>1$
$$
(Cy)_k=(a_k\varepsilon^{-1}+a_{k-1}\varepsilon^{-2}+\cdots+a_1\varepsilon^{-k})+(a_n\varepsilon^{-k-1}+a_{n-1}\varepsilon^{-k-2}+\cdots+a_{k+1}\varepsilon^{-n})\\
=\varepsilon^{-k-1}f(\varepsilon)=\varepsilon^{n-k}{f(\varepsilon)\over\varepsilon}=b_k{f(\varepsilon)\over\varepsilon}\\
\therefore C\left({\varepsilon\over f(\varepsilon)}y\right)=b
$$
So we can choose ( in case $C$ is invertible we have only one choice )
$$
\color{red}{x_n={\varepsilon\over f(\varepsilon)}}
$$
Now the row operation $R_1\sum_{k=1}^na_k-\sum_{k=2}^nR_k$ on $C$ gives us
$$
\left(\sum_{k=1}^na_k\right)C=f(1)C=\begin{bmatrix}
  a_{1}&      a_{n}&    a_{n-1}&     \cdots&    a_{3}&     a_{2}\\ 
  a_{2}&      a_{1}&      a_{n}&     \cdots&    a_{4}&     a_{3}\\ 
  a_{3}&      a_{2}&      a_{1}&     \cdots&    a_{5}&     a_{4}\\ 
 \vdots&     \vdots&     \vdots&     \ddots&   \vdots&     \vdots\\
  a_{n}&    a_{n-1}&    a_{n-2}&     \cdots&    a_{2}&     a_{1}
\end{bmatrix}
$$
The matrix $f(1)C$ is the circulant matrix whose associated polynomial is $f(x)/x$ and therefore its determinant is
$$
\prod_{j=1}^nf(\varepsilon^j)\varepsilon^{-j}=\begin{cases}\prod_{j=1}^nf(\varepsilon^j) & n\text{ is odd }\\
\varepsilon^{-{n\over2}}\prod_{j=1}^nf(\varepsilon^j) & n\text{ is even }
\end{cases}
$$
So assuming $f(1),f(\varepsilon)\neq0$,
$$
\color{blue}{\det(T)=\begin{cases}
\varepsilon\prod_{j=2}^{n-1}f(\varepsilon^j) & n\text{ is odd }\\
\varepsilon^{1-{n\over2}}\prod_{j=2}^{n-1}f(\varepsilon^j) & n\text{ is even }
\end{cases}}
$$
Edit: As the original problem asks to prove a different result, I checked $\det(T)$ for $2\le n\le10$ and the formula works. Easiest way to prove that $f(\varepsilon^j)\varepsilon^{-j}$ are eigenvalues of $f(1)C$ is to realize $f(1)C=g(P)$ where $g(x)=f(x)/x,\;P$ is the permutation matrix defined as
$$
P_{ij}=\delta_{i(j+1\mod n)}
$$
and the eigenvalues of $P$ are $n$-th roots of unity. This proof clearly works for any circulant matrix.
