This paper proposes an effective approach to the computation of the discrete fractional Fourier transform for an input vector of any length N . This approach uses specific structural properties of the discrete fractional Fourier transformation matrix. Thanks to these properties, the fractional Fourier transformation matrix can be decomposed into a sum of three or two matrices, one of which is a dense matrix, and the rest of the matrix components are sparse matrices. The aforementioned dense matrix has unique structural properties that allow advantageous factorization. This factorization is the main contributor to the reduction in the overall computational complexity of the discrete fractional Fourier transform computation. The remaining calculations do not contribute significantly to the total amount of computation. Thus, the proposed approach allows to reduce the number of arithmetic operations when calculating the discrete fractional Fourier transform.
Abstract. In this paper we introduce an efficient algorithm for the multiplication of Pauli numbers. The direct multiplication of two Pauli numbers requires 64 real multiplications and 56 real additions. More effective solutions still do not exist. We show how to compute a product of the Pauli numbers with 24 conventional multiplications, 8 multiplications by 1/2 and 56 real additions.
In this article we present a common approach for the development of algorithms for calculating products of hypercomplex numbers. The main idea of the proposed approach is based on the representation of hypernumbers multiplying via the matrix-vector products and further creative decomposition of the matrix, leading to the reduction of arithmetical complexity of calculations. The proposed approach allows the construction of sufficiently well algorithms for hypernumbers multiplication with reduced computational complexity. If the schoolbook method requires N 2 real multiplications and N(N-1) real additions, the proposed approach allows to develop algorithms, which take only [N(N-1)/2]+2 real multiplications and 3Nlog 2 N+[N(N-3)+4]/2 real additions. Streszczenie. W artykule zostało przedstawione uogólnione podejście do syntezy algorytmów wyznaczania iloczynów liczb hiperzespolonych. Główna idea proponowanego podejścia polega na reprezentacji operacji mnożenia liczb hiperzespolonych w formie iloczynu wektorowomacierzowego i dalszej możliwości kreatywnej dekompozycji czynnika macierzowego prowadzącej do redukcji złożoności obliczeniowej. Proponowane podejście pozwala zbudować algorytmy wyróżniające się w porównaniu do metody naiwnej zredukowaną złożonością obliczeniową. Jeśli metoda naiwna wymaga wykonania N 2 mnożeń oraz N(N-1) dodawań liczb rzeczywistych to proponowane podejście pozwala syntetyzować algorytmy wymagające tylko [N(N-1)/2]+2 mnożeń oraz 3Nlog 2 N+[N(N-3)+4]/2 dodawań. (Uogólnione podejście do konstruowania zracjonalizowanych algorytmów mnożenia liczb hiperzespolonych-tytuł polski artykułu).
This article offers the strategies for the synthesis of fast algorithms for computing the matrix-vector products. It considers the specific example of synthesis of fast algorithm for matrix by the vector multiplication. The example offered allows tracking all the stages of construction of the algorithm which was rationalized from the point of view of number multiplication minimization.
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