# Agarwal-Cooley Convolution Algorithm

## Abstract

The cyclic convolution algorithms of chapter 6 are efficient for special small block lengths, but as the size of the block length increases, other methods are required. First as discussed in chapter 6, these algorithms keep the number of required multiplications small, but can require many additions. Also, each size requires a different algorithm. There is no uniform structure that can be repeatedly called upon. In this chapter, a technique similar to the Good-Thomas PFA will be developed to decompose a large size cyclic convolution into several small size cyclic convolutions which in turn can be evaluated using the Winograd cyclic convolution algorithm. These ideas were introduced by Agarwal and Cooley [1] in 1977. As in the Good-Thomas PFA, the CRT is used to define an indexing of data. This indexing changes a one-dimensional cyclic convolution into a two-dimensional cyclic convolution. We will see how to compute a two-dimensional cyclic convolution by ‘nesting’ a fast algorithm for one-dimensional cyclic convolution inside another fast algorithm for one-dimensional cyclic convolution. There are several two-dimensional cyclic convolution algorithms which although important will not be discussed. These can be found in [2].

## Keywords

Discrete Fourier Transform Fast Algorithm Permutation Matrix Circulant Matrix Convolution Theorem## Preview

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## References

- [1]Agarwal, R. C. and Cooley, J. W. “New Algorithms for Digital Convolution”, IEEE Trans. ASSP-25 (1977):pp.392-410.Google Scholar
- [2]Blahut, R. E.
*Fast Algorithms for Digital Signal Processing*, Chapter 7. Addison-Wesley, 1985.Google Scholar - [3]Nussbaumer, H. J.
*Fast Fourier Transform and Convolution Algorithms*, Second Edition, Chapter 6, Springer-Verlag, 1981.Google Scholar - [4]Arambepola, B. and Rayner, P. J. “Efficient Transforms for Multidimensional Convolutions”, Electron. Lett. 15, (1979):pp.189–190.MathSciNetCrossRefGoogle Scholar