Improved Deterministic Length Reduction

TL;DR

This paper introduces a deterministic length reduction technique that accelerates sparse convolution algorithms to match the best randomized times, with improved preprocessing efficiency.

Contribution

The paper presents a new deterministic length reduction method that improves sparse convolution time complexity to match randomized algorithms, with faster preprocessing.

Findings

Achieves $O(n_1 \log^2 n_1)$ convolution time deterministically.

Maintains $O(n_1^2)$ preprocessing time for the technique.

Reduces preprocessing time for polynomial reduction from $O(n_1^4)$ to $O(n_1^3 ext{polylog}(n_1))$.

Abstract

This paper presents a new technique for deterministic length reduction. This technique improves the running time of the algorithm presented in \cite{LR07} for performing fast convolution in sparse data. While the regular fast convolution of vectors $V_1,V_2$ whose sizes are $N_1,N_2$ respectively, takes $O(N_1 \log N_2)$ using FFT, using the new technique for length reduction, the algorithm proposed in \cite{LR07} performs the convolution in $O(n_1 \log^3 n_1)$, where $n_1$ is the number of non-zero values in $V_1$. The algorithm assumes that $V_1$ is given in advance, and $V_2$ is given in running time. The novel technique presented in this paper improves the convolution time to $O(n_1 \log^2 n_1)$ {\sl deterministically}, which equals the best running time given achieved by a {\sl randomized} algorithm. The preprocessing time of the new technique remains the same as the preprocessing time of \cite{LR07}, which is $O(n_1^2)$. This assumes and deals the case where $N_1$ is polynomial in $n_1$. In the case where $N_1$ is exponential in $n_1$, a reduction to a polynomial case can be used. In this paper we also improve the preprocessing time of this reduction from $O(n_1^4)$ to $O(n_1^3{\rm polylog}(n_1))$.