An Algorithm for Fast Multiplication of Kaluza Numbers

This paper presents a new algorithm for multiplying two Kaluza numbers. Performing this operation directly requires 1024 real multiplications and 992 real additions. We presented in a previous paper an effective algorithm that can compute the same result with only 512 real multiplications and 576 real additions. More effective solutions have not yet been proposed. Nevertheless, it turned out that an even more interesting solution could be found that would further reduce the computational complexity of this operation. In this article, we propose a new algorithm that allows one to calculate the product of two Kaluza numbers using only 192 multiplications and 384 additions of real numbers.

[1]  Hasan Al-Marzouqi,et al.  Metacognitive Sedenion-Valued Neural Network and its Learning Algorithm , 2020, IEEE Access.

[2]  Titouan Parcollet,et al.  A survey of quaternion neural networks , 2019, Artificial Intelligence Review.

[3]  C. Eddie Moxey,et al.  Hypercomplex correlation techniques for vector images , 2003, IEEE Trans. Signal Process..

[4]  Reza Ghorbani,et al.  Cognitive Quaternion Valued Neural Network and some applications , 2017, Neurocomputing.

[5]  Eduardo Bayro-Corrochano,et al.  Multi-resolution image analysis using the quaternion wavelet transform , 2005, Numerical Algorithms.

[6]  Steven G. Krantz The Number Systems , 2002 .

[7]  Thomas Bülow,et al.  Hypercomplex signals-a novel extension of the analytic signal to the multidimensional case , 2001, IEEE Trans. Signal Process..

[8]  Suhas N. Diggavi,et al.  Construction and analysis of a new quaternionic space-time code for 4 transmit antennas , 2005, Commun. Inf. Syst..

[9]  Marcos Eduardo Valle,et al.  A General Framework for Hypercomplex-valued Extreme Learning Machines , 2021, ArXiv.

[10]  Aleksandr Cariow,et al.  An algorithm for multipication of Kaluza numbers , 2015, ArXiv.

[11]  Danilo Comminiello,et al.  Compressing deep quaternion neural networks with targeted regularization , 2019, CAAI Trans. Intell. Technol..

[12]  Jiasong Wu,et al.  Deep Octonion Networks , 2019, Neurocomputing.

[13]  Aleksandr Cariow,et al.  Strategies for the Synthesis of Fast Algorithms for the Computation of the Matrix-vector Products , 2014 .

[14]  Özgür Ertug Communication over Hypercomplex Kähler Manifolds: Capacity of Multidimensional-MIMO Channels , 2007, Wirel. Pers. Commun..

[15]  Jinde Cao,et al.  Constrained Quaternion-Variable Convex Optimization: A Quaternion-Valued Recurrent Neural Network Approach , 2020, IEEE Transactions on Neural Networks and Learning Systems.

[16]  Marcos Eduardo Valle,et al.  A Broad Class of Discrete-Time Hypercomplex-Valued Hopfield Neural Networks , 2019, Neural Networks.

[17]  Panos Liatsis,et al.  Traffic flow prediction using Deep Sedenion Networks , 2020, ArXiv.

[18]  Lyes Saad Saoud,et al.  Metacognitive Octonion-Valued Neural Networks as They Relate to Time Series Analysis , 2020, IEEE Transactions on Neural Networks and Learning Systems.