Let $R$ be a commutative ring. Let $f(x_1, \dots, x_n), g(x_1, \dots, x_n)$ be two multidimensional polynomials in $R$ with maximal total degree $\delta$. How fast can we compute the product of $f$ and $g$, i.e. the resulting coefficients of each term?

I have only been able to find a non-trivial algorithm for when $R$ is a field of characteristic 0, but not for $R$ being a general commutative ring.

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    $\begingroup$ The coefficients can be computed by convolutions, and if $R$ is a field, then you can use FFTs for computing the convolutions. The multiplication of two univariate polynomials is quasi equivalent to the computation of the convolution of their coefficients. So is this question basically just inquiring whether there exists a fast algorithm for computing convolutions in general commutative rings? $\endgroup$ Jun 13, 2015 at 18:28
  • $\begingroup$ Yes, but in this case a multidimensional convolution. For one dimensional convolutions one can use variants of schonhage strassen to achieve $O(\delta \log \delta \log \log \delta)$. $\endgroup$
    – chtenb
    Jun 13, 2015 at 18:32

1 Answer 1


There is a general method for efficient multiplication in any ring. This would cover the case where the degree is bounded in each dimension separately, because a polynomial $p\in R[x,y]$ in variables $x$, $y$ over the ring $R$ can be interpreted as a polynomial $p\in R[x][y]$ in the variable $y$ over the ring $R[x]$. This is essentially the case I thought about in my comment above. However, the total degree limitation from the question requires a different method. Such total degree limitations occur for determinants and permanents, but have little to do with convolution. It is not even covered in the general overview of multiplication algorithms. At least we may assume WLOG that the ring has has characteristic $p^m$ or 0, because it is easy and canonical to decompose a ring into simpler rings (and because factoring the characteristic is not part of the cost of multiplying two polynomials, even if it might be more expensive than the multiplication itself).

For $n\geq 2\delta$, the resulting polynomial $fg$ can have up to $2^{2\delta}$ monomial terms. For $R=\mathbb Z$, the coefficients of the monomial terms can all be different, so we can't do better than $O(2^{2\delta})$. The question had probably something like $O(N^2)$ versus $O(N \log N \log \log N)$ in mind. But if $N$ would measure the number of coefficients (of monomial terms) of the input polynomials $f$ and $g$, then $O(N^2)$ would be the optimal worst case bound. But what should $N$ measure instead?

Let's assume that $fg$ is determined uniquely by its values $fg(\underline{x}^1),\ldots,fg(\underline{x}^N)$ for suitable points $\underline{x}^1,\ldots,\underline{x}^N$. Then a possible way to compute $fg$ is to evaluate $f$ and $g$ at the points $\underline{x}^1,\ldots,\underline{x}^N$, multiply these values pointwise, and then determine $fg$ from these products. This is more or less also what happens when the FFT is used to compute a convolution.

The paper On Zeros of a Polynomial in a Finite Grid cited in the recent answer by Anurag Bishnoi gives sharp bounds on the number of zeros of a polynomial on a finite grid satisfying condition (D). A nonempty subset $S \subset R$ is said to satisfy Condition (D) if for all $x\neq y\in S$, the element $x−y\in R$ is not a zero divisor. For $n \in \mathbb Z^+$ we put $[n] = \{1,2,\ldots,n\}$. We have

Theorem 1.2 (Generalized Alon-Füredi Theorem). Let $R$ be a ring, and let $A_1, \ldots, A_n$ be non-empty finite subsets of $R$ that satisfy Condition (D). For $i \in [n]$, let $b_i$ be an integer such that $1 \leq b_i \leq \#A_i$. Let $f \in R[\underline{t}] = R[t_1,\ldots,t_n]$ be a non-zero polynomial such that $\deg_{t_i} f \leq \# A_i − b_i$ for all $i \in [n]$. Let $\mathfrak U_A = \{x \in A : f(x)\neq 0\}$ where $A=A_1 \times\dots\times A_n \subseteq R^n$. Then we have (see §2.1 for the notation) $$\# \mathfrak U_A \geq \mathfrak m(\#A_1,\ldots,\#A_n;b_1,\ldots, b_n;􏰊\sum_{i=1}^n\#A_i −\deg f).$$ Moreover, this bound is sharp in all cases.


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