Vector-radix FFT algorithm: Difference between revisions

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Vector-radix FFT algorithm can reduce the number of complex multiplications significantly, compared to row-vector algorithm. For example, for a <math>N^M</math> element matrix (M dimensions, and size N on each dimension), the number of complex multiples of vector-radix FFT algorithm for radix-2 is <math>\frac{2^M -1}{2^M} N^M \log_2 N</math>, meanwhile, for row-column algorithm, it is <math>\frac{M N^M} 2 \log_2 N</math>. And generally, even larger savings in multiplies are obtained when this algorithm is operated on larger radices and on higher dimensional arrays.<ref name=Harris77/>

Overall, the vector-radix algorithm significantly reduces the structural complexity of the traditional DFT having a better indexing scheme, at the expense of a slight increase in arithmetic operations. So this algorithm is widely used for many applications in engineering, science, and mathematics, for example, implementations in image processing,<ref name="Buijs74">{{cite journal|last1=Buijs|first1=H.|last2=Pomerleau|first2=A.|last3=Fournier|first3=M.|last4=Tam|first4=W.|title=Implementation of a fast Fourier transform (FFT) for image processing applications|journal=IEEE Transactions on Acoustics, Speech, and Signal Processing|date=Dec 1974|volume=22|issue=6|pages=420–424|doi=10.1109/TASSP.1974.1162620}}</ref> and high speed FFT processor designing.<ref name="Badar15">{{cite journal|last1=Badar|first1=S.|last2=Dandekar|first2=D.|title=High speed FFT processor design using radix −4 pipelined architecture|journal=2015 International Conference on Industrial Instrumentation and Control (ICIC), Pune, 2015|pages=1050–1055|doi=10.1109/IIC.2015.7150901|url=httphttps://ieeexplore.ieee.org/stampdocument/stamp.jsp?tp7150901|year=&arnumber=7150901&isnumber2015|isbn=7150576978-1-4799-7165-7}}</ref>

== 2-D DIT case ==