A New Reduction Method from Multivariate Polynomials to Univariate Polynomials
<|reference_start|>A New Reduction Method from Multivariate Polynomials to Univariate Polynomials: Polynomial multiplication is a fundamental problem in symbolic computation. There are efficient methods for the multiplication of two univariate polynomials. However, there is rarely efficiently nontrivial method for the multiplication of two multivariate polynomials. Therefore, we consider a new multiplication mechanism that involves a) reversibly reducing multivariate polynomials into univariate polynomials, b) calculating the product of the derived univariate polynomials by the Toom-Cook or FFT algorithm, and c) correctly recovering the product of multivariate polynomials from the product of two univariate polynomials. This work focuses on step a), expecting the degrees of the derived univariate polynomials to be as small as possible. We propose iterative Kronecker substitution, where smaller substitution exponents are selected instead of standard Kronecker substitution. We also apply the Chinese remainder theorem to polynomial reduction and find its advantages in some cases. Afterwards, we provide a hybrid reduction combining the advantages of both reduction methods. Moreover, we compare these reduction methods in terms of lower and upper bounds of the degree of the product of two derived univariate polynomials, and their computational complexities. With randomly generated multivariate polynomials, experiments show that the degree of the product of two univariate polynomials derived from the hybrid reduction can be reduced even to approximately 3% that resulting from the standard Kronecker substitution, implying an efficient subsequent multiplication of two univariate polynomials.<|reference_end|>
@article{wang2024a,
    title={A New Reduction Method from Multivariate Polynomials to Univariate
  Polynomials},
    author={Cancan Wang, Ming Su, Gang Wang, Qingpo Zhang},
    journal={arXiv preprint arXiv:2403.12716},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.12716},
    primaryClass={cs.CC}
}