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\lecture{12: Berlekamp's Algorithm}{CS681}{Piyush P Kurur}{Ramprasad Saptharishi}

\section*{Overview}

Last class we say a randomized algorithm for factoring univariate
polynomials over a finite field. This class we shall look at another
algorithm for factoring. This was given by Berlekamp.

\section{Berlekamp's Algorithm}

We are given a polynomial $f(X) \in \F_p[X].$ As in all factoring
algorithms, the first thing to do is make $f$ square free. Once we
have done this, the polynomial is of the form
$$f = f_1f_2\cdots f_m$$
where each $f_i$ is a distinct irreducible factor of $f.$ Then,
chinese remaindering tells us that
$$R = \F_p[X]/(f) = \inparen{\F_p[X]/(f_1)}\times\inparen{\F_p[X]/(f_2)}\times \cdots \times \inparen{\F_p[X]/(f_m)}$$

Let the degree of $f_i$ be $d_i$ and the degree of $f$ be $n.$

\subsection{The Frobenius Map}

Here enters the frobenius map again. Consider the following function
from $R$ to itself.

\bea{
T:R& \longrightarrow & R\\
a & \mapsto & a^p
}

The first thing to note here is that all elements of $\F_p$ are
fixed in this map because we know that elements $\F_p$ satisfy $X^p - X=0.$ And further, we also saw the special case of binomial theorem
that said $(X+Y)^p = X^p + Y^p.$

To understand this map $T$ better, let us understand $R.$ We have
defined $R = \F_p[X]/(f)$ which is basically polynomials over $\F_p$
modulo $f.$ And clearly, every element of $R$ has degree atmost $n-1$
and therefore a polynomial of the form $a_0 + a_1X + \cdots a_{n-1}X^{n-1}$ can be thought of as the vector
$\inparen{a_0,a_1,\cdots, a_{n-1}}.$ Thus, the ring $R$ is a vector
space of dimension $n$ over $\F_p.$\\

Now, notice that the map $T$ described above is $\F_p$-linear. By
this, we mean that for all $\alpha,\beta \in \F_p$ and $u,v\in R$, we
have $T(\alpha u + \beta v) = \alpha T(u) + \beta T(v).$ If we think
of these elements of $\F_p$ as scalars, they can be 'pulled out of'
$T.$

Therefore, it's enough to know the image of each $X^i$ by the
map.
\bea{
p(X) & = & a_0 + a_1X + \cdots + a_{n-1}X^{n-1}\\
T(p(X)) & = & a_0 + a_1T(X) + \cdots + a_{n-1}T(X^{n-1})
}

\subsection{The Berlekamp Sub-algebra}

Now let $B$ be the map $T -I$ where $I$ is the identity map (maps
everything to itself). Then $B$ sends any element $a\in R$ to $a^p - a.$ Now define $\mathcal{B} = \ker(B) = \ker(T - I).$ It is easy to
check that the kernel of any linear map from one vector space into
another (in this case $R$ to $R$) forms a subspace of the vector
space. Hence $\mathcal{B}$ is a subspace of the vector space $R.$

This space $\mathcal{B}$ is called the Berlekamp sub-algebra. \\

What does this space look like? Let $a$ be any element in
$\mathcal{B}$ and therefore is an element of $R.$ Let the chinese
remainder theorem map this to the tuple $(a_1,a_2, \cdots, a_m).$ And
therefore $a^p - a = (a_1^p - a_1, \cdots, a_m^p - a_m).$ And since
$a\in \mathcal{B}$, each of the $a_i^p - a_i$ must be $0.$ Now, $a_i^p - a_i$ is an element of $\F_p[X]/(g_i) \iso \F_{p^{d_i}}$ and
therefore $a_i^p - a_i = 0$ can happen only if
$a_i \in \F_p.$\footnote{the elements of $\F_{p^d}$ that satisfy $X^p - X = 0$ are precisely those elements of $\F_p$}

And therefore, each element of the tuple will infact be an element of
$\F_p$ and therefore
$$\mathcal{B} \iso \F_p \times \cdots \times\F_p$$

And since $\mathcal{B}$ is a product of $m$ copies of $\F_p$,
$\mathcal{B}$ is an $m$ dimensional subspace of $R$ over $\F_p.$

\subsection{Finding a Basis}

A basis for $R$ is obvious, $\inbrace{1,X,X^2,\cdots, X^{n-1}}$ but
how do we find a basis for $\mathcal{B}?$ Let us say $T$ acts on $R$
as
$$T(X^i) = \sum_{j=0}^{n-1} \alpha_{ji}X^j$$
then we can think of $T$ as a the matrix
$\inparen{\alpha_{ji}}_{i,j}.$ Thus, thinking of polynomials in $R$ as
a tuple of coefficients described above, then the action of $T$ is
just left multiplication by this matrix.

Thus, the matrix for $B$ would be $\hat{B} = (\alpha_{ji})_{i,j} - I.$ Hence the
kernel of this map is just asking for all vectors $v$ such that
$\hat{B}v = 0.$ And therefore, a basis for $\mathcal{B}$ can be
obtained by gaussian elimination of $\hat{B}.$

Once we have a basis $\inbrace{b_1, b_2,\cdots, b_m}$, we can pick a
random element of $\mathcal{B}$ by just picking $m$ random elements
$a_m$ from $\F_p$ and $\sum a_ib_i$ would be our random element from
$\mathcal{B}.$ \\

Any element $a$ in $\mathcal{B}$ gets mapped to
$\F_p\times \cdots \times \F_p$ by the Chinese remainder theorem. And
therefore, we can use the Cantor-Zassenhaus idea there:
$a^{\frac{p-1}{2}}$ corresponds to a vector of just $1$s and $-1$s.

So here is the final algorithm.

\begin{algorithm}
\caption{{\sc Berlekamp Factorization}}
\begin{algorithmic}[1]
\REQUIRE A polynomial $f\in \F_p[X]$ of degree $n$
\STATE Make $f$ square-free.
\STATE Let $R$ be the ring $\F_p[X]/(f),$ considered as a $n$
dimensional vector space over $\F_p.$
\STATE Construct the matrix of transformation $\hat{B}$ corresponding
to the map $a\mapsto a^p - a.$
\STATE Use gaussian elimination and find a basis $\inbrace{b_1, b_2, \cdots, b_m}$ for the berlekamp subalgebra $\mathcal{B}.$
\STATE Pick $\inbrace{a_1, \cdots, a_{m-1}} \in_R \F_p$ and let $b = \sum a_ib_i.$
\IF{$gcd(b^{\frac{p-1}{2}}+1,f)$ is non-trivial}
\STATE {\bf return} $gcd(b^{\frac{p-1}{2}}+1,f)$
\COMMENT{Happens with probability atleast $1 - 2^{m-1}$}
\ENDIF
\STATE Repeat from step $5.$
\end{algorithmic}
\end{algorithm}

\end{document}