Early termination in sparse interpolation algorithms

Abstract

A probabilistic strategy, early termination, enables different interpolation algorithms to adapt to the degree or the number of terms in the target polynomial when neither is supplied in the input. In addition to dense algorithms, we implement this strategy in sparse interpolation algorithms. Based on early termination, racing algorithms execute simultaneously dense and sparse algorithms. The racing algorithms can be embedded as the univariate interpolation substep within Zippel’s multivariate method. In addition, we experimentally verify some heuristics of early termination, which make use of thresholds and post-verification.