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The Vector-Radix FFT algorithm, is a multidimensional Fast Fourier transform (FFT) algorithm, which is a generalization of the ordinary Cooley–Tukey FFT algorithm that divides the transform dimensions by arbitrary radices. It breaks a multidimensional (MD) discrete Fourier transform (DFT) down into successively smaller MD DFTs until, ultimately, only trivial MD DFTs need to be evaluated.
The most common multidimensional FFT algorithm is row-column algorithm, which means transforming the array first in one index and then in the other, see more in FFT. Then a radix-2 direct 2-D FFT has been developed, and it can eliminate 25% of the multiplies by the conventional row-column approach. And this algorithm has been extended to rectangular arrays and arbitrary radices, which is the general Vector-Radix algorithm. Perhaps it is the simplest non-row-column FFT algorithm.
Vector-radix FFT algorithm can reduce the number of complex multiplications significantly, compared to row-vector algorithm. For example, for a $N^{M}$ time matrix (M dimensions, and size N on each dimension), the number of complex multiples of vector-radix FFT algorithm is ${\frac {2^{M}-1}{2^{M}}}N^{M}log_{2}N$, meanwhile, for row-column algorithm, it is ${\frac {MN^{M}}{2}}log_{2}N$. And generally, even larger savings in multiplies are obtained when this algorithm is operated on larger radices and on higher dimensional arrays.

2-D DIT case

As with Cooley–Tukey FFT algorithm, two dimensional vector-radix FFT is derived by decomposing the regular 2-D DFT into sums of smaller DFT's multiplied by "twiddle" factor.
A decimation-in-time (DIT) algorithm means the decomposition is based on time domain $x$, see more in Cooley–Tukey FFT algorithm.

We suppose the 2-D DFT

$X(k_{1},k_{2})=\sum _{n_{1}=0}^{N_{1}-1}\sum _{n_{2}=0}^{N_{2}-1}x[n_{1},n_{2}]\cdot W_{N_{1}}^{k_{1}n_{1}}W_{N_{2}}^{k_{2}n_{2}},$

where $k_{1}=0,\dots ,N_{1}-1$,and $k_{2}=0,\dots ,N_{2}-1$, and $x[n_{1},n_{2}]$ is a $N_{1}\times N_{2}$ matrix, and $W_{N}^{kn}=exp(-j2\pi /N)$.

For simplicity, let us assume that $N_{1}=N_{2}=N$, and radix-$(r\times r)$($N/r$ are integers).

Using the change of variables:

• $n_{i}=rp_{i}+q_{i}$, where $p_{i}=0,\cdots ,(N/r)-1;q_{i}=0,\cdots ,r-1;$
• $k_{i}=u_{i}+v_{i}N/r$, where $u_{i}=0,\cdots ,(N/r)-1;v_{i}=0,\cdots ,r-1;$

where $i=1$ or $2$.

Then, the two dimensional DFT can be written as:

$X(u_{1}+v_{1}N/r,u_{2}+v_{2}N/r)=\sum _{q_{1}=0}^{r-1}\sum _{q_{2}=0}^{r-1}\left[\sum _{p_{1}=0}^{N/r-1}\sum _{p_{2}=0}^{N/r-1}x[rp_{1}+q_{1},rp_{1}+q_{1}]W_{N/r}^{p_{1}u_{1}}W_{N/r}^{p_{2}u_{2}}\right]\cdot W_{N}^{q_{1}u_{1}+q_{2}u_{2}}W_{r}^{q_{1}v_{1}}W_{r}^{q_{2}v_{2}},$

One stage "butterfly" for DIT vector-radix 2x2 FFT

The equation above defines the basic structure of the 2-D DIT radix-$(r\times r)$ "butterfly". (See 1-D "butterfly" in Cooley–Tukey FFT algorithm)

When $r=2$, the equation can be broken into four summations: one over those samples of x for which both $n_{1}$ and $n_{2}$ are even, one for which $n_{1}$ is even and $n_{2}$ is odd, one of which $n_{1}$ is odd and $n_{2}$ is even, and one for which both $n_{1}$ and $n_{2}$ are odd, and this leads to:

$X(k_{1},k_{2})=S_{00}(k_{1},k_{2})+S_{01}(k_{1},k_{2})W_{N}^{k_{2}}+S_{10}(k_{1},k_{2})W_{N}^{k_{1}}+S_{11}(k_{1},k_{2})W_{N}^{k_{1}+k_{2}}$,

where $S_{ij}(k_{1},k_{2})=\sum _{n_{1}=0}^{N/2-1}\sum _{n_{2}=0}^{N/2-1}x[2n_{1}+i,2n_{2}+j]\cdot W_{N/2}^{n_{1}k_{1}}W_{N/2}^{n_{2}k_{2}}$.

2-D DIF case

Similarly, a decimation-in-frequency (DIF, also called the Sande-Tukey algorithm) algorithm means the decomposition is based on frequency domain $X$, see more in Cooley–Tukey FFT algorithm.
Using the change of variables:

• $n_{i}=p_{i}+q_{i}N/r$, where $p_{i}=0,\cdots ,(N/r)-1;q_{i}=0,\cdots ,r-1;$
• $k_{i}=ru_{i}+v_{i}$, where $u_{i}=0,\cdots ,(N/r)-1;v_{i}=0,\cdots ,r-1;$

where $i=1$ or $2$.
And the DFT equation can be written as:

$X(ru_{1}+v_{1},ru_{2}+v_{2})=\sum _{p_{1}=0}^{N/r-1}\sum _{p_{2}=0}^{N/r-1}\left[\sum _{q_{1}=0}^{r-1}\sum _{q_{2}=0}^{r-1}x[p_{1}+q_{1}N/r,p_{1}+q_{1}N/r]W_{r}^{q_{1}v_{1}}W_{r}^{q_{2}v_{2}}\right]\cdot W_{N}^{p_{1}v_{1}+p_{2}v_{2}}W_{N/r}^{p_{1}u_{1}}W_{N/r}^{p_{2}u_{2}},$

Other approaches

The Split-radix FFT algorithm has been proved to be a useful method for 1-D DFT. And this method has been applied to the vector-radix FFT to obtain a split vector-radix FFT.

In conventional 2-D vector-radix algorithm, we decompose the incident $k_{1},k_{2}$ into 4 groups:
${\begin{array}{lcl}X(2k_{1},2k_{2})&:&even-even\\X(2k_{1},2k_{2}+1)&:&even-odd\\X(2k_{1}+1,2k_{2})&:&odd-even\\X(2k_{1}+1,2k_{2}+1)&:&odd-odd\end{array}}$

By the split vector-radix algorithm, the first three groups remain unchanged, the fourth odd-odd group is further decomposed into another four sub-groups, and seven groups in total:
${\begin{array}{lcl}X(2k_{1},2k_{2})&:&even-even\\X(2k_{1},2k_{2}+1)&:&even-odd\\X(2k_{1}+1,2k_{2})&:&odd-even\\X(4k_{1}+1,4k_{2}+1)&:&odd-odd\\X(4k_{1}+1,4k_{2}+3)&:&odd-odd\\X(4k_{1}+3,4k_{2}+1)&:&odd-odd\\X(4k_{1}+3,4k_{2}+3)&:&odd-odd\end{array}}$

That means the fourth term in 2-D DIT radix-$(2\times 2)$ equation, $S_{11}(k_{1},k_{2})W_{N}^{k_{1}+k_{2}}$ becomes:

$A_{11}(k_{1},k_{2})W_{N}^{k_{1}+k_{2}}+A_{13}(k_{1},k_{2})W_{N}^{k_{1}+3k_{2}}+A_{31}(k_{1},k_{2})W_{N}^{3k_{1}+k_{2}}+A_{33}(k_{1},k_{2})W_{N}^{3(k_{1}+k_{2})}$,

where $A_{ij}(k_{1},k_{2})=\sum _{n_{1}=0}^{N/4-1}\sum _{n_{2}=0}^{N/4-1}x[4n_{1}+i,4n_{2}+j]\cdot W_{N/4}^{n_{1}k_{1}}W_{N/4}^{n_{2}k_{2}}$

The 2-D N by N DFT is then obtained by successive use of the above decomposition, up to the last stage.
It has been shown that the split vector radix algorithm has saved about 30% of the complex multiplications and about the same number of the complex additions for typical $1024\times 1024$ array, compared with the vector-radix algorithm.