Freivalds' algorithm

Freivalds' algorithm (named after Rūsiņš Mārtiņš Freivalds) is a probabilistic randomized algorithm used to verify matrix multiplication. Given three n × n matrices $A$, $B$, and $C$, a general problem is to verify whether ${\displaystyle A\times B=C}$. A naïve algorithm would compute the product $A\times B$ explicitly and compare term by term whether this product equals $C$. However, the best known matrix multiplication algorithm runs in ${\displaystyle O(n^{2.3729})}$ time.^[1] Freivalds' algorithm utilizes randomization in order to reduce this time bound to $O(n^{2})$ ^[2] with high probability. In $O(kn^{2})$ time the algorithm can verify a matrix product with probability of failure less than $2^{{-k}}$.

The algorithm

Input

Three n × n matrices $A$, $B$, and $C$.

Output

Yes, if ${\displaystyle A\times B=C}$; No, otherwise.

Procedure

1. Generate an n × 1 random 0/1 vector ${\vec {r}}$.
2. Compute $\vec{P} = A \times (B \vec{r}) - C\vec{r}$.
3. Output "Yes" if $\vec{P} = (0,0,\ldots,0)^T$; "No," otherwise.

Error

If ${\displaystyle A\times B=C}$, then the algorithm always returns "Yes". If $A \times B \neq C$, then the probability that the algorithm returns "Yes" is less than or equal to one half. This is called one-sided error.

By iterating the algorithm k times and returning "Yes" only if all iterations yield "Yes", a runtime of $O(kn^{2})$ and error probability of ${\displaystyle \leq 1/2^{k}}$ is achieved.

Example

Suppose one wished to determine whether:

$AB = \begin{bmatrix} 2 & 3 \\ 3 & 4 \end{bmatrix} \begin{bmatrix} 1 & 0 \\ 1 & 2 \end{bmatrix} \stackrel{?}{=} \begin{bmatrix} 6 & 5 \\ 8 & 7 \end{bmatrix} = C.$

A random two-element vector with entries equal to 0 or 1 is selected — say $\vec{r} = \begin{bmatrix}1 \\ 1\end{bmatrix}$ — and used to compute:

$\begin{align} A \times (B \vec{r}) - C\vec{r} & = \begin{bmatrix} 2 & 3 \\ 3 & 4 \end{bmatrix} \left( \begin{bmatrix} 1 & 0 \\ 1 & 2 \end{bmatrix} \begin{bmatrix}1 \\ 1\end{bmatrix} \right) - \begin{bmatrix} 6 & 5 \\ 8 & 7 \end{bmatrix} \begin{bmatrix}1 \\ 1\end{bmatrix} \\ & = \begin{bmatrix} 2 & 3 \\ 3 & 4 \end{bmatrix} \begin{bmatrix}1 \\ 3\end{bmatrix} - \begin{bmatrix}11 \\ 15\end{bmatrix} \\ & = \begin{bmatrix}11 \\ 15\end{bmatrix} - \begin{bmatrix}11 \\ 15\end{bmatrix} \\ & = \begin{bmatrix}0 \\ 0\end{bmatrix}. \end{align}$

This yields the zero vector, suggesting the possibility that AB = C. However, if in a second trial the vector $\vec{r} = \begin{bmatrix}1 \\ 0\end{bmatrix}$ is selected, the result becomes:

$A \times (B \vec{r}) - C\vec{r} = \begin{bmatrix} 2 & 3 \\ 3 & 4 \end{bmatrix} \left( \begin{bmatrix} 1 & 0 \\ 1 & 2 \end{bmatrix} \begin{bmatrix}1 \\ 0\end{bmatrix} \right) - \begin{bmatrix} 6 & 5 \\ 8 & 7 \end{bmatrix} \begin{bmatrix}1 \\ 0\end{bmatrix} = \begin{bmatrix}-1 \\ -1\end{bmatrix}.$

The result is nonzero, proving that in fact AB ≠ C.

There are four two-element 0/1 vectors, and half of them give the zero vector in this case ($\vec{r} = \begin{bmatrix}0 \\ 0\end{bmatrix}$ and $\vec{r} = \begin{bmatrix}1 \\ 1\end{bmatrix}$), so the chance of randomly selecting these in two trials (and falsely concluding that AB=C) is 1/2² or 1/4. In the general case, the proportion of r yielding the zero vector may be less than 1/2, and a larger number of trials (such as 20) would be used, rendering the probability of error very small.

Error analysis

Let p equal the probability of error. We claim that if A × B = C, then p = 0, and if A × B ≠ C, then p ≤ 1/2.

Case A × B = C

$\begin{align} \vec{P} &= A \times (B \vec{r}) - C \vec{r}\\ &= (A \times B)\vec{r} - C\vec{r}\\ &= (A \times B - C)\vec{r}\\ &= \vec{0} \end{align}$

This is regardless of the value of ${\vec {r}}$, since it uses only that $A \times B - C = 0$. Hence the probability for error in this case is:

$\Pr[\vec{P} \neq 0] = 0$

Case A × B ≠ C

Let

$\vec{P} = D \times \vec{r} = (p_1, p_2, \dots, p_n)^T$

Where

$D = A \times B - C = (d_{ij})$.

Since $A \times B \neq C$, we have that some element of $D$ is nonzero. Suppose that the element $d_{ij} \neq 0$. By the definition of matrix multiplication, we have:

$p_i = \sum_{k = 1}^n d_{ik}r_k = d_{i1}r_1 + \cdots + d_{ij}r_j + \cdots + d_{in}r_n = d_{ij}r_j + y$.

For some constant $y$. Using Bayes' Theorem, we can partition over $y$:

$\Pr[p_i = 0] = \Pr[p_i = 0 | y = 0]\cdot \Pr[y = 0]\, +\, \Pr[p_i = 0 | y \neq 0] \cdot \Pr[y \neq 0]$

(1)

We use that:

$\Pr[p_i = 0 | y = 0] = \Pr[r_j = 0] = \frac{1}{2}$

$\Pr[p_i = 0 | y \neq 0] = \Pr[r_j = 1 \and d_{ij}=-y] \leq \Pr[r_j = 1] = \frac{1}{2}$

Plugging these in the equation (1), we get:

$\begin{align} \Pr[p_i = 0] &\leq \frac{1}{2}\cdot \Pr[y = 0] + \frac{1}{2}\cdot \Pr[y \neq 0]\\ &= \frac{1}{2}\cdot \Pr[y = 0] + \frac{1}{2}\cdot (1 - \Pr[y = 0])\\ &= \frac{1}{2} \end{align}$

Therefore,

${\displaystyle \Pr[{\vec {P}}=0]=\Pr[p_{1}=0\land \dots \land p_{i}=0\land \dots \land p_{n}=0]\leq \Pr[p_{i}=0]\leq {\frac {1}{2}}.}$

This completes the proof.

Ramifications

Simple algorithmic analysis shows that the running time of this algorithm is O(n²), beating the classical deterministic algorithm's bound of O(n³). The error analysis also shows that if we run our algorithm k times, we can achieve an error bound of less than ${\frac {1}{2^{k}}}$, an exponentially small quantity. The algorithm is also fast in practice due to wide availability of fast implementations for matrix-vector products. Therefore, utilization of randomized algorithms can speed up a very slow deterministic algorithm. In fact, the best known deterministic matrix multiplication verification algorithm known at the current time is a variant of the Coppersmith–Winograd algorithm with an asymptotic running time of O(n^2.3729).^[1]

Freivalds' algorithm frequently arises in introductions to probabilistic algorithms due to its simplicity and how it illustrates the superiority of probabilistic algorithms in practice for some problems.

See also

• Schwartz–Zippel lemma

References

1. Virginia Vassilevska Williams. "Breaking the Coppersmith-Winograd barrier" (PDF).
2. Raghavan, Prabhakar (1997). "Randomized algorithms". ACM Computing Surveys. 28. doi:10.1145/234313.234327. Retrieved 2008-12-16.

• Freivalds, R. (1977), “Probabilistic Machines Can Use Less Running Time”, IFIP Congress 1977, pp. 839–842.