A Non-linear Generalization of Singular Value Decomposition and its Application to Cryptanalysis

Prabhakar G. Vaidya; Sajini Anand P. S; Nithin Nagaraj

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arXiv · 0711.4910

A Non-linear Generalization of Singular Value Decomposition and its Application to Cryptanalysis

Prabhakar G. Vaidya·Sajini Anand P. S·Nithin Nagaraj

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Abstract

Singular Value Decomposition (SVD) is a powerful tool in linear algebra.We propose an extension of SVD for both the qualitative detection and quantitative determination of nonlinearity in a time series. The paper illustrates nonlinear SVD with the help of data generated from nonlinear maps and flows (differential equations).

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Prabhakar G. Vaidya, Sajini Anand P. S, Nithin Nagaraj. 2009-02-11. A Non-linear Generalization of Singular Value Decomposition and its Application to Cryptanalysis. https://arxiv.org/abs/0711.4910

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