Operations on polynomials and series
In this article we will cover common operations that you will probably have to do if you deal with polynomials.
Basic Notion and Facts
Let \(A(x) = a_0 + a_1 x + \dots + a_n x^n\) be a polynomial over some field \(\mathbb F\). It For simplicity we will write \(A\) instead of \(A(x)\) wherever possible, which will be understandable from the context. It is assumed that either \(a_n \neq 0\) or \(A(x)=0\).
The degree of polynomial \(A\) with \(a_n \neq 0\) is defined as \(\deg A = n\). For consistency, degree of \(A(x) = 0\) is defined as \(\deg A = \infty\). In this notion, \(\deg AB = \deg A + \deg B\) for arbitrary polynomials \(A\) and \(B\).
Polynomials form an Euclidean ring which means that for any polynomials \(A\) and \(B \neq 0\) we can uniquely represent \(A\) as
Here \(R\) is the remainder of \(A\) modulo \(B\) and \(D\) is called the quotient. If \(A\) and \(B\) have the same remainder modulo \(C\), they're said to be equivalent modulo \(C\), which is denoted as \(A \equiv B \pmod{C}\). Several important properties of polynomial Euclidean division:

\(A\) is a multiple of \(B\) if and only if \(A \equiv 0 \pmod B\).

It implies that \(A \equiv B \pmod C\) if and only if \(AB\) is a multiple of \(C\).

In particular, \(A \equiv B \pmod{C \cdot D}\) implies \(A \equiv B \pmod{C}\).

For any linear polynomial \(xr\) it holds that \(A(x) \equiv A(r) \pmod{xr}\).

It implies that \(A\) is a multiple of \(xr\) if and only if \(A(r)=0\).

For modulo being \(x^k\), it holds that \(A \equiv a_0 + a_1 x + \dots + a_{k1} x^{k1} \pmod{x^k}\).
Basic implementation
Here you can find the basic implementation of polynomial algebra.
It supports all trivial operations and some other useful methods. The main class is poly<T>
for polynomials with coefficients of type T
.
All arithmetic operation +
, 
, *
, %
and /
are supported, %
and /
standing for remainder and quotient in Euclidean division.
There is also the class modular<m>
for performing arithmetic operations on remainders modulo a prime number m
.
Other useful functions:
deriv()
: computes the derivative \(P'(x)\) of \(P(x)\).integr()
: computes the indefinite integral \(Q(x) = \int P(x)\) of \(P(x)\) such that \(Q(0)=0\).inv(size_t n)
: calculate the first \(n\) coefficients of \(P^{1}(x)\) in \(O(n \log n)\).log(size_t n)
: calculate the first \(n\) coefficients of \(\ln P(x)\) in \(O(n \log n)\).exp(size_t n)
: calculate the first \(n\) coefficients of \(\exp P(x)\) in \(O(n \log n)\).pow(size_t k, size_t n)
: calculate the first \(n\) coefficients for \(P^{k}(x)\) in \(O(n \log nk)\).deg()
: returns the degree of \(P(x)\).lead()
: returns the coefficient of \(x^{\deg P(x)}\).resultant(poly<T> a, poly<T> b)
: computes the resultant of \(a\) and \(b\) in \(O(a \cdot b)\).bpow(T x, size_t n)
: computes \(x^n\).bpow(T x, size_t n, T m)
: computes \(x^n \pmod{m}\).chirpz(T z, size_t n)
: computes \(P(1), P(z), P(z^2), \dots, P(z^{n1})\) in \(O(n \log n)\).vector<T> eval(vector<T> x)
: evaluates \(P(x_1), \dots, P(x_n)\) in \(O(n \log^2 n)\).poly<T> inter(vector<T> x, vector<T> y)
: interpolates a polynomial by a set of pairs \(P(x_i) = y_i\) in \(O(n \log^2 n)\). And some more, feel free to explore the code!
Arithmetic
Multiplication
The very core operation is the multiplication of two polynomials. That is, given the polynomials \(A\) and \(B\):
You have to compute polynomial \(C = A \cdot B\), which is defined as
It can be computed in \(O(n \log n)\) via the Fast Fourier transform and almost all methods here will use it as subroutine.
Inverse series
If \(A(0) \neq 0\) there always exists an infinite formal power series \(A^{1}(x) = q_0+q_1 x + q_2 x^2 + \dots\) such that \(A^{1} A = 1\). It often proves useful to compute first \(k\) coefficients of \(A^{1}\) (that is, to compute it modulo \(x^k\)). There are two major ways to calculate it.
Divide and conquer
This algorithm was mentioned in Schönhage's article and is inspired by Graeffe's method. It is known that for \(B(x)=A(x)A(x)\) it holds that \(B(x)=B(x)\), that is, \(B(x)\) is an even polynomial. It means that it only has nonzero coefficients with even numbers and can be represented as \(B(x)=T(x^2)\). Thus, we can do the following transition:
Note that \(T(x)\) can be computed with a single multiplication, after which we're only interested in the first half of coefficients of its inverse series. This effectively reduces the initial problem of computing \(A^{1} \pmod{x^k}\) to computing \(T^{1} \pmod{x^{\lfloor k / 2 \rfloor}}\).
The complexity of this method can be estimated as
Sieveking–Kung algorithm
The generic process described here is known as Hensel lifting, as it follows from Hensel's lemma. We'll cover it in more detail further below, but for now let's focus on ad hoc solution. "Lifting" part here means that we start with the approximation \(B_0=q_0=a_0^{1}\), which is \(A^{1} \pmod x\) and then iteratively lift from \(\bmod x^a\) to \(\bmod x^{2a}\).
Let \(B_k \equiv A^{1} \pmod{x^a}\). The next approximation needs to follow the equation \(A B_{k+1} \equiv 1 \pmod{x^{2a}}\) and may be represented as \(B_{k+1} = B_k + x^a C\). From this follows the equation
Let \(A B_k \equiv 1 + x^a D \pmod{x^{2a}}\), then the equation above implies
From this, one can obtain the final formula, which is
Thus starting with \(B_0 \equiv a_0^{1} \pmod x\) we will compute the sequence \(B_k\) such that \(AB_k \equiv 1 \pmod{x^{2^k}}\) with the complexity
The algorithm here might seem a bit more complicated than the first one, but it has a very solid and practical reasoning behind it, as well as a great generalization potential if looked from a different perspective, which would be explained further below.
Euclidean division
Consider two polynomials \(A(x)\) and \(B(x)\) of degrees \(n\) and \(m\). As it was said earlier you can rewrite \(A(x)\) as
Let \(n \geq m\), it would imply that \(\deg D = n  m\) and the leading \(nm+1\) coefficients of \(A\) don't influence \(R\). It means that you can recover \(D(x)\) from the largest \(nm+1\) coefficients of \(A(x)\) and \(B(x)\) if you consider it as a system of equations.
The system of linear equations we're talking about can be written in the following form:
From the looks of it, we can conclude that with the introduction of reversed polynomials
the system may be rewritten as
From this you can unambiguously recover all coefficients of \(D(x)\):
And from this, in turn, you can recover \(R(x)\) as \(R(x) = A(x)  B(x)D(x)\).
Note that the matrix above is a socalled triangular Toeplitz matrix and, as we see here, solving system of linear equations with arbitrary Toeplitz matrix is, in fact, equivalent to polynomial inversion. Moreover, inverse matrix of it would also be triangular Toeplitz matrix and its entries, in terms used above, are the coefficients of \((B^R(x))^{1} \pmod{x^{nm+1}}\).
Calculating functions of polynomial
Newton's method
Let's generalize the Sieveking–Kung algorithm. Consider equation \(F(P) = 0\) where \(P(x)\) should be a polynomial and \(F(x)\) is some polynomialvalued function defined as
where \(\beta\) is some constant. It can be proven that if we introduce a new formal variable \(y\), we can express \(F(x)\) as
where \(F'(x)\) is the derivative formal power series defined as
and \(G(x, y)\) is some formal power series of \(x\) and \(y\). With this result we can find the solution iteratively.
Let \(F(Q_k) \equiv 0 \pmod{x^{a}}\). We need to find \(Q_{k+1} \equiv Q_k + x^a C \pmod{x^{2a}}\) such that \(F(Q_{k+1}) \equiv 0 \pmod{x^{2a}}\).
Substituting \(x = Q_{k+1}\) and \(y=Q_k\) in the formula above, we get
Since \(Q_{k+1}  Q_k \equiv 0 \pmod{x^a}\), it also holds that \((Q_{k+1}  Q_k)^2 \equiv 0 \pmod{x^{2a}}\), thus
The last formula gives us the value of \(Q_{k+1}\):
Thus, knowing how to invert polynomials and how to compute \(F(Q_k)\), we can find \(n\) coefficients of \(P\) with the complexity
where \(f(n)\) is the time needed to compute \(F(Q_k)\) and \(F'(Q_k)^{1}\) which is usually \(O(n \log n)\).
The iterative rule above is known in numerical analysis as Newton's method.
Hensel's lemma
As was mentioned earlier, formally and generically this result is known as Hensel's lemma and it may in fact used in even broader sense when we work with a series of nested rings. In this particular case we worked with a sequence of polynomial remainders modulo \(x\), \(x^2\), \(x^3\) and so on.
Another example where Hensel's lifting might be helpful are socalled padic numbers where we, in fact, work with the sequence of integer remainders modulo \(p\), \(p^2\), \(p^3\) and so on. For example, Newton's method can be used to find all possible automorphic numbers (numbers that end on itself when squared) with a given number base. The problem is left as an exercise to the reader. You might consider this problem to check if your solution works for \(10\)based numbers.
Logarithm
For the function \(\ln P(x)\) it's known that:
Thus we can calculate \(n\) coefficients of \(\ln P(x)\) in \(O(n \log n)\).
Inverse series
Turns out, we can get the formula for \(A^{1}\) using Newton's method. For this we take the equation \(A=Q^{1}\), thus:
Exponent
Let's learn to calculate \(e^{P(x)}=Q(x)\). It should hold that \(\ln Q = P\), thus:
\(k\)th power
Now we need to calculate \(P^k(x)=Q\). This may be done via the following formula:
Note though, that you can calculate the logarithms and the exponents correctly only if you can find some initial \(Q_0\).
To find it, you should calculate the logarithm or the exponent of the constant coefficient of the polynomial.
But the only reasonable way to do it is if \(P(0)=1\) for \(Q = \ln P\) so \(Q(0)=0\) and if \(P(0)=0\) for \(Q = e^P\) so \(Q(0)=1\).
Thus you can use formula above only if \(P(0) = 1\). Otherwise if \(P(x) = \alpha x^t T(x)\) where \(T(0)=1\) you can write that:
Note that you also can calculate some \(k\)th root of a polynomial if you can calculate \(\sqrt[k]{\alpha}\), for example for \(\alpha=1\).
Evaluation and Interpolation
Chirpz Transform
For the particular case when you need to evaluate a polynomial in the points \(x_r = z^{2r}\) you can do the following:
Let's substitute \(2kr = r^2+k^2(rk)^2\). Then this sum rewrites as:
Which is up to the factor \(z^{r^2}\) equal to the convolution of the sequences \(u_k = a_k z^{k^2}\) and \(v_k = z^{k^2}\).
Note that \(u_k\) has indexes from \(0\) to \(n\) here and \(v_k\) has indexes from \(n\) to \(m\) where \(m\) is the maximum power of \(z\) which you need.
Now if you need to evaluate a polynomial in the points \(x_r = z^{2r+1}\) you can reduce it to the previous task by the transformation \(a_k \to a_k z^k\).
It gives us an \(O(n \log n)\) algorithm when you need to compute values in powers of \(z\), thus you may compute the DFT for nonpowers of two.
Another observation is that \(kr = \binom{k+r}{2}  \binom{k}{2}  \binom{r}{2}\). Then we have
The coefficient of \(x^{n+r}\) of the product of the polynomials \(A_0(x) = \sum\limits_{k=0}^n a_{nk}z^{\binom{nk}{2}}x^k\) and \(A_1(x) = \sum\limits_{k\geq 0}z^{\binom{k}{2}}x^k\) equals \(z^{\binom{r}{2}}A(z^r)\). You can use the formula \(z^\binom{k+1}{2}=z^{\binom{k}{2}+k}\) to calculate the coefficients of \(A_0(x)\) and \(A_1(x)\).
Multipoint Evaluation
Assume you need to calculate \(A(x_1), \dots, A(x_n)\). As mentioned earlier, \(A(x) \equiv A(x_i) \pmod{xx_i}\). Thus you may do the following:
 Compute a segment tree such that in the segment \([l,r)\) stands the product \(P_{l, r}(x) = (xx_l)(xx_{l+1})\dots(xx_{r1})\).
 Starting with \(l=1\) and \(r=n\) at the root node. Let \(m=\lfloor(l+r)/2\rfloor\). Let's move down to \([l,m)\) with the polynomial \(A(x) \pmod{P_{l,m}(x)}\).
 This will recursively compute \(A(x_l), \dots, A(x_{m1})\), now do the same for \([m,r)\) with \(A(x) \pmod{P_{m,r}(x)}\).
 Concatenate the results from the first and second recursive call and return them.
The whole procedure will run in \(O(n \log^2 n)\).
Interpolation
There's a direct formula by Lagrange to interpolate a polynomial, given set of pairs \((x_i, y_i)\):
Computing it directly is a hard thing but turns out, we may compute it in \(O(n \log^2 n)\) with a divide and conquer approach:
Consider \(P(x) = (xx_1)\dots(xx_n)\). To know the coefficients of the denominators in \(A(x)\) we should compute products like:
But if you consider the derivative \(P'(x)\) you'll find out that \(P'(x_i) = P_i\). Thus you can compute \(P_i\)'s via evaluation in \(O(n \log^2 n)\).
Now consider the recursive algorithm done on same segment tree as in the multipoint evaluation. It starts in the leaves with the value \(\dfrac{y_i}{P_i}\) in each leaf.
When we return from the recursion we should merge the results from the left and the right vertices as \(A_{l,r} = A_{l,m}P_{m,r} + P_{l,m} A_{m,r}\).
In this way when you return back to the root you'll have exactly \(A(x)\) in it. The total procedure also works in \(O(n \log^2 n)\).
GCD and Resultants
Assume you're given polynomials \(A(x) = a_0 + a_1 x + \dots + a_n x^n\) and \(B(x) = b_0 + b_1 x + \dots + b_m x^m\).
Let \(\lambda_0, \dots, \lambda_n\) be the roots of \(A(x)\) and let \(\mu_0, \dots, \mu_m\) be the roots of \(B(x)\) counted with their multiplicities.
You want to know if \(A(x)\) and \(B(x)\) have any roots in common. There are two interconnected ways to do that.
Euclidean algorithm
Well, we already have an article about it. For an arbitrary domain you can write the Euclidean algorithm as easy as:
template<typename T>
T gcd(const T &a, const T &b) {
return b == T(0) ? a : gcd(b, a % b);
}
It can be proven that for polynomials \(A(x)\) and \(B(x)\) it will work in \(O(nm)\).
Resultant
Let's calculate the product \(A(\mu_0)\cdots A(\mu_m)\). It will be equal to zero if and only if some \(\mu_i\) is the root of \(A(x)\).
For symmetry we can also multiply it with \(b_m^n\) and rewrite the whole product in the following form:
The value defined above is called the resultant of the polynomials \(A(x)\) and \(B(x)\). From the definition you may find the following properties:
 \(\mathcal R(A, B) = (1)^{nm} \mathcal R(B, A)\).
 \(\mathcal R(A, B)= a_n^m b_m^n\) when \(n=0\) or \(m=0\).
 If \(b_m=1\) then \(\mathcal R(A  CB, B) = \mathcal R(A, B)\) for an arbitrary polynomial \(C(x)\) and \(n,m \geq 1\).
 From this follows \(\mathcal R(A, B) = b_m^{\deg(A)  \deg(ACB)}\mathcal R(A  CB, B)\) for arbitrary \(A(x)\), \(B(x)\), \(C(x)\).
Miraculously it means that resultant of two polynomials is actually always from the same ring as their coefficients!
Also these properties allow us to calculate the resultant alongside the Euclidean algorithm, which works in \(O(nm)\).
template<typename T>
T resultant(poly<T> a, poly<T> b) {
if(b.is_zero()) {
return 0;
} else if(b.deg() == 0) {
return bpow(b.lead(), a.deg());
} else {
int pw = a.deg();
a %= b;
pw = a.deg();
base mul = bpow(b.lead(), pw) * base((b.deg() & a.deg() & 1) ? 1 : 1);
base ans = resultant(b, a);
return ans * mul;
}
}
HalfGCD algorithm
There is a way to calculate the GCD and resultants in \(O(n \log^2 n)\).
The procedure to do so implements a \(2 \times 2\) linear transform which maps a pair of polynomials \(a(x)\), \(b(x)\) into another pair \(c(x), d(x)\) such that \(\deg d(x) \leq \frac{\deg a(x)}{2}\). If you're careful enough, you can compute the halfGCD of any pair of polynomials with at most \(2\) recursive calls to the polynomials which are at least \(2\) times smaller.
The specific details of the algorithm are somewhat tedious to explain, however you can find its implementation in the library, as half_gcd
function.
After halfGCD is implemented, you can repeatedly apply it to polynomials until you're reduced to the pair of \(\gcd(a, b)\) and \(0\).