Dixon's algorithm

In number theory, Dixon's factorization method (also Dixon's random squares method or Dixon's algorithm) is a general-purpose integer factorization algorithm; it is the prototypical factor base method. Unlike for other factor base methods, its run-time bound comes with a rigorous proof that does not rely on conjectures about the smoothness properties of the values taken by a polynomial.

The algorithm was designed by John D. Dixon, a mathematician at Carleton University, and was published in 1981.

Basic idea

Dixon's method is based on finding a congruence of squares modulo the integer N which is intended to factor. Fermat's factorization method finds such a congruence by selecting random or pseudo-random ''x'' values and hoping that the integer ''x''² mod N is a perfect square (in the integers):

:$x^2\equiv y^2\quad(\hboxN),\qquad x\not\equiv\pm y\quad(\hboxN).$

For example, if , (by starting at 292, the first number greater than and counting up) the is 256, the square of 16. So . Computing the greatest common divisor of and ''N'' using Euclid's algorithm gives 163, which is a factor of ''N''.

In practice, selecting random ''x'' values will take an impractically long time to find a congruence of squares, since there are only squares less than ''N''.

Dixon's method replaces the condition "is the square of an integer" with the much weaker one "has only small prime factors"; for example, there are 292 squares smaller than 84923; 662 numbers smaller than 84923 whose prime factors are only 2,3,5 or 7; and 4767 whose prime factors are all less than 30. (Such numbers are called '' B-smooth'' with respect to some bound ''B''.)

If there are many numbers $a_1 \ldots a_n$ whose squares can be factorized as $a_i^2 \mod N = \prod_^m b_j^$ for a fixed set $b_1 \ldots b_m$ of small primes, linear algebra modulo 2 on the matrix $e_$ will give a subset of the $a_i$ whose squares combine to a product of small primes to an even power — that is, a subset of the $a_i$ whose squares multiply to the square of a (hopefully different) number mod N.

Method

Suppose the composite number ''N'' is being factored. Bound ''B'' is chosen, and the '' factor base'' is identified (which is called ''P''), the set of all primes less than or equal to ''B''. Next, positive integers ''z'' are sought such that ''z''² mod ''N'' is ''B''-smooth. Therefore we can write, for suitable exponents ''a_i'',

: $z^2 \text N = \prod_ p_i^$

When enough of these relations have been generated (it is generally sufficient that the number of relations be a few more than the size of ''P''), the methods of linear algebra, such as Gaussian elimination, can be used to multiply together these various relations in such a way that the exponents of the primes on the right-hand side are all even:

: $$

This yields a congruence of squares of the form which can be turned into a factorization of ''N'', This factorization might turn out to be trivial (i.e. ), which can only happen if in which case another try must be made with a different combination of relations; but if a nontrivial pair of factors of ''N'' is reached, the algorithm terminates.

Pseudocode

This section is taken directly from Dixon (1981).

Dixon's algorithm

''Initialization.'' Let ''L'' be a list of integers in the range , ''n'' and let ''P'' = be the list of the ''h'' primes ≤ ''v''. Let ''B'' and ''Z'' be initially empty lists (''Z'' will be indexed by ''B'').

Step 1. If ''L'' is empty, exit (algorithm unsuccessful). Otherwise, take the first term ''z'' from ''L'', remove it from ''L'', and proceed to Step 2.

Step 2. Compute ''w'' as the least positive remainder of ''z² mod n''. Factor ''w'' as:

$w = w' \prod_i p_i^$

where ' has no factor in ''P''. If ' = 1, proceed to Step 3; otherwise, return to Step 1.

Step 3. Let ''a'' ← (''a''₁, ..., ''a''_h). Add ''a'' to ''B'' and ''z'' to ''Z''. If ''B'' has at most ''h'' elements, return to Step 1; otherwise, proceed to Step 4.

Step 4. Find the first vector ''c'' in ''B'' that is linearly dependent (mod 2) on earlier vectors in ''B''. Remove ''c'' from ''B'' and $z_c$ from ''Z''. Compute coefficients $f_b$ such that:

$\mathbf \equiv \sum_ f_b \mathbf \pmod$

Define:

$\mathbf = (d_1, \dots, d_n) \gets \frac \left(\mathbf + \sum f_b \mathbf \right)$

Proceed to Step 5.

Step 5. Compute:

$x \gets z_c \prod_b z_b^, \quad y \gets \prod_i p_i^$

so that:

$x^2 \equiv \prod_i p_i^ = y^2 \mod n.$

If $x \equiv y$ or $x \equiv -y \pmod$, return to Step 1. Otherwise, return:

$\gcd(n, x + y)$

which provides a nontrivial factor of ''n'', and terminate successfully.

Step-by-step example : factorizing (''n'' = 84923) using Dixon's algorithm

This example is lifted directly from the LeetArxiv substack.Kibicho, Murage (2025)
''Hand-Written Paper Implementation''
Asymptotically Fast Factorization of Integers. Credit is given to the original author.
* Initialization:
** Define a list of numbers ''L'', ranging from 1 to 84923:
::: $L = \$
:* Define a value ''v'', which is the smoothness factor:
::: $v = 7$
:* Define a list ''P'' containing all the prime numbers less than or equal to ''v'':
::: $P =$
:* Define ''B'' and ''Z'', two empty lists. ''B'' is a list of powers, while ''Z'' is a list of accepted integers:
::: B = /math>
::: Z = /math>

* Step 1: Iterating $z$ values
** Write a for loop that indexes the list $L$. Each element in $L$ is indexed as $z$. The for loop exits at the end of the list.
::
int n = 84923;
for (int i = 1; i <= n; i++)

* Step 2: Computing $z^2 \mod n$ and v-smooth Prime Factorization
** To proceed, compute $z^2 \mod 84923$ for each ''z'', then express the result as a prime factorization.
:: $1^2 \mod 84923 \equiv 1 \mod 84923 = 2^0 \cdot 3^0 \cdot 5^0 \cdot 7^0 \mod 84923$
:: $\vdots$
:: $513^2 \mod 84923 = 8400 \mod 84923 = 2^4 \cdot 3^1 \cdot 5^2 \cdot 7^1 \mod 84923$
::$\vdots$
::$537^2 \mod 84923 = 33600 \mod 84923 = 2^6 \cdot 3^1 \cdot 5^2 \cdot 7^1 \mod 84923$
::$538^2 \mod 84923 = 34675 \mod 84923 = 5^2 \cdot 19^1 \cdot 73^1 \mod 84923$

::This step continues for all values of ''z'' in the range.

* Step 3: If $z^2 \mod 84923$ is ''7-smooth'', then append its powers to list $B$ and append $z$ to list $Z$.
::$Z = \$
::$B = \$

* Step 4: This step is split into two parts.
:: Part 1: Find $B$ modulo 2.
::$B = \begin 0 & 0 & 0 & 0 \\ 4 & 1 & 2 & 1 \\ 6 & 1 & 2 & 1 \end \mod 2 \equiv B = \begin 0 & 0 & 0 & 0 \\ 0 & 1 & 0 & 1 \\ 0 & 1 & 0 & 1 \end$

:: Part 2: Check if any row combinations of $B$ sum to even numbers

:::For example, summing Row $2$ and Row $3$ gives us a vector of even numbers.

:::$R_2 = \$ and $R_3 = \$

:::then
:::$R_2 + R_3 = \ + \$
:::$R_2 + R_3 = \$.

* Step 5 : This step is split into four parts.
** Part 1. (Finding x): Multiply the corresponding $z$ values for the rows found in Step 4. Then find the square root. This gives us $x$.
*** For Row 2, we had $2^4 * 3^1 * 5^2 * 7^1$.
*** For Row 3, we had $2^6 * 3^1 * 5^2 * 7^1$.
*** Thus, we find $x$ :

::::$\begin (513 \cdot 537) ^ 2 \mod 84923 = y ^ 2 \\ \\ \text \quad x^2 \mod 84923 = (513 \cdot 537) ^ 2 \mod 84923 \\ \\ \text \quad x = (513 \cdot 537) \mod 84923 \\ \\ \text \quad x = 275481 \mod 84923\\ \\ \text \quad x = 20712 \mod 84923\\ \end$

:* Part 2. (Finding y): Multiply the corresponding smooth factorizations for the rows found in Step 4. Then find the square root. This gives us $y$.
:::$\begin y^2 = (2^4 \cdot 3^1 \cdot 5^2 \cdot 7^1) \times (2^6 \cdot 3^1 \cdot 5^2 \cdot 7^1) \\ \\ \text \\ y^2 = 2^ \cdot 3^ \cdot 5^ \cdot 7^ \\ \\ \text \\ y^2 = 2^ \cdot 3^2 \cdot 5^4 \cdot 7^2 \\ \\ \text \\ y = 2^5 \cdot 3^1 \cdot 5^2 \cdot 7^1 \\ \\ \text \\ y = 32 \times 3 \times 25 \times 7 \\ \\ \text \\ y = 16800 \end$

:* Part 3. (Find x + y and x - y) where ''x = 20712'' and ''y = 16800''.
::: $x + y = 20712 + 16800 = 37512$
::: $x - y = 20712 - 16800 = 3912$

:* Part 4. Compute GCD(x+y, n) and GCD(x-y, n), where ''n = 84923'', ''x+y = 292281'' and ''x-y = 258681''

:::$\begin \gcd(37512, 84923) = 521 \\ \gcd(3912, 84923) = 163 \end$

Quick check shows $84923 = 521 \times 163$.

Optimizations

The quadratic sieve is an optimization of Dixon's method. It selects values of ''x'' close to the square root of such that ''x²'' modulo ''N'' is small, thereby largely increasing the chance of obtaining a smooth number.

Other ways to optimize Dixon's method include using a better algorithm to solve the matrix equation, taking advantage of the sparsity of the matrix: a number ''z'' cannot have more than $\log_2 z$ factors, so each row of the matrix is almost all zeros. In practice, the block Lanczos algorithm is often used. Also, the size of the factor base must be chosen carefully: if it is too small, it will be difficult to find numbers that factorize completely over it, and if it is too large, more relations will have to be collected.

A more sophisticated analysis, using the approximation that a number has all its prime factors less than $N^$ with probability about $a^$ (an approximation to the Dickman–de Bruijn function), indicates that choosing too small a factor base is much worse than too large, and that the ideal factor base size is some power of $\exp\left(\sqrt\right)$.

The optimal complexity of Dixon's method is
:$O\left(\exp\left(2 \sqrt 2 \sqrt\right)\right)$
in big-O notation, or
:L_n /2, 2 \sqrt 2/math>
in L-notation.

References

{{Number theoretic algorithms

Integer factorization algorithms
Squares in number theory