Efficient Algorithms for Injectivity and Bounded Surjectivity of One-dimensional Nonlinear Cellular Automata

TL;DR

This paper presents optimized algorithms for determining injectivity and surjectivity in one-dimensional nonlinear cellular automata, significantly reducing computational costs and extending applicability to complex automata.

Contribution

It introduces new algorithms and theorems that improve the efficiency of property determination in nonlinear cellular automata over existing methods.

Findings

Optimized surjectivity algorithm with higher efficiency

Extended applicability to various boundary conditions

New injectivity algorithms outperform Amoroso's in time and space

Abstract

Nonlinear cellular automata are extensively used in simulations, image processing, cryptography, and so on. The determination of their fundamental properties, injectivity and surjectivity, related to information loss during the evolution, is necessary in various applications. Currently, people still use Amoroso's algorithms for injectivity and surjectivity determinations, but this incurs significant computational costs when applied to complex nonlinear cellular automata. We have optimized Amoroso's surjectivity algorithm, improving its operational efficiency greatly and extended its applicability to various boundaries. Furthermore, we have introduced new theorems and algorithms for determining injectivity, which offer substantial improvements over Amoroso's algorithm in both time and space. With these new algorithms, we are equipped to determine the properties of larger and more complex cellular automata, thereby employing more advanced cellular automata to achieve increasingly complex functionalities.