A Probabilistic Algorithm for Computation of Polynomial Greatest Common with Smaller Factors

Applied Mathematics and Sciences: An International Journal (MathSJ) Vol.9, No.2/3, September 2022
DOI : 10.5121/mathsj.2022.9301 1
A PROBABILISTIC ALGORITHM FOR
COMPUTATION OF POLYNOMIAL GREATEST
COMMON WITH SMALLER FACTORS
Yang Zhang1,2
, Xin Qian1, 2
, Qidi You1,2
, Xuan Zhou1,2
,
Xiyong Zhang1, 2
and Yang Wang1, 2
1
Space star technology co., LTD
2
State Key Laboratory of Space-Ground Integrated Information Technology
ABSTRACT
In the earlier work, Knuth present an algorithm to decrease the coefficient growth in the Euclidean
algorithm of polynomials called subresultant algorithm. However, the output polynomials may have a
small factor which can be removed. Then later, Brown of Bell Telephone Laboratories showed the
subresultant in another way by adding a variant called𝜏 and gave a way to compute the variant.
Nevertheless, the way failed to determine every𝜏 correctly.
In this paper, we will give a probabilistic algorithm to determine the variant 𝜏 correctly in most cases by
adding a few steps instead of computing 𝑡(𝑥) when given 𝑓(𝑥) and𝑔(𝑥) ∈ ℤ[𝑥], where 𝑡(𝑥) satisfies that
𝑠(𝑥)𝑓(𝑥) + 𝑡(𝑥)𝑔(𝑥) = 𝑟(𝑥), here 𝑡(𝑥), 𝑠(𝑥) ∈ ℤ[𝑥]
KEYWORDS
Euclidean Algorithm, Subresultant, Primitive Remainder Sequences,
1. INTRODUCTION
The Euclidean algorithm and the extended Euclidean algorithm of polynomials is an important
research object in polynomial computer algebra. Using this algorithm, one can get the g.c.d. of
two polynomials (denoted as gcd(𝑓, 𝑔) when given polynomials 𝑓(𝑥) and 𝑔(𝑥)) and decides
whether these polynomials are coprime or not. Specifically, if the degree of gcd(𝑓, 𝑔) is larger
than 0, 𝑓(𝑥) and 𝑔(𝑥) are not coprime, otherwise, 𝑓(𝑥) and 𝑔(𝑥) are coprime. Being coprime
between two polynomials means there exist common roots between these two polynomials.
To quantify the indicator whether there exists a common root between 𝑓(𝑥) and 𝑔(𝑥), Sylvester
gave a matrix in 1840 called Sylvester matrix with entries simply being the coefficients of 𝑓(𝑥)
and 𝑔(𝑥). The determinant of Sylvester matrix is called resultant. Whether the resultant of 𝑓(𝑥)
and 𝑔(𝑥) is nonzero corresponds to the case where 𝑓(𝑥) and 𝑔(𝑥) are coprime or not
respectively. Moreover, Sylvester generalized his definition and introduced the concept of
subresultant. They are nonzero if and only if the corresponding degree appears as a degree of a
remainder of the Euclidean algorithm.
However, the early Euclidean algorithm of polynomials works for polynomials in𝔽[𝑥], here 𝔽 is
a field. In 1836 Jacobi introduced pseudo-division over polynomials and extended the Euclidean

2
algorithm of polynomials in field to a domain by multiplying 𝑓(𝑥) with a certain power of the
leading coefficient of 𝑔(𝑥) before starting the division.
Using pseudo-division, there are a lot of results about polynomials even the ideal lattice used in
cryptography. From 1960, researchers built early computer algebra systems and G.C.D.
computations were an important test problem. Nevertheless, using pseudo-division in Euclidean
algorithm causes exponential coefficients growth. In 1967, Collins [1] explained that the 𝑖-th
intermediate coefficients are approximately longer by a factor of (1 + √2)
𝑖
than the input
coefficients. There are many ways to decrease the size of coefficients, most of them are quite
inefficient, however. In this paper, we mainly focus on the subresultant algorithm and its variant.
In [2], Knuth present the early subresultant algorithm and gave an elegant proof of its correctness.
In [3], Brown showed the variant of subresultant algorithm and gave a way to remove the small
factor of each remainder. However, the method he present didn't work always.
Recently, in [4], they show an algorithm to triangularize the basis of an ideal lattice which is
often used to construct ideal lattice-based cryptosystems. In their algorithm, they need to compute
all the PPRSoL (the definition given in Sec.2.4) of 𝑓(𝑥) and 𝑔(𝑥). However, to obtain the
PPRSoL, we need to compute each content of 𝑡𝑖(𝑥) satisfying 𝑠𝑖(𝑥)𝑓(𝑥) + 𝑡𝑖(𝑥)𝑔(𝑥) = 𝑟𝑖(𝑥) to
remove the extra factor in each original remaindes 𝑟𝑖(𝑥). However, in this paper, we find a new
way to obtain PPRSoL without computing 𝑡𝑖(𝑥) by applying the variant of subresultant
algorithm.
In this paper, we give some results about the extended Euclidean algorithm. Using these results,
we propose a new algorithm that outputting the PPRSoL of 𝑓(𝑥) and 𝑔(𝑥) which works for most
cases.
2. PRELIMINARIES
2.1. Notations
In this paper, a matrix is denoted as uppercase bold letter and a vector is denoted as lowercase
bold letter. For a matrix𝑨 ∈ ℝ𝑚×𝑛
, the element in the 𝑖-th row and the 𝑗-th column of 𝑨 is
expressed as 𝑎𝑖,𝑗 . For a polynomial 𝑓(𝑥) with degree 𝑛, we use 𝑙𝑐(𝑓) to present the leading
coefficient of 𝑓(𝑥) and use𝑑𝑒𝑔(𝑓) to present the degree. The degree of a constant polynomial is
defined as 0 and the degree of a zero polynomial is defined as −∞. The greatest common divisor
is abbreviated to g.c.d.. Let 𝜍[𝑥] denote the domain of polynomials in x with coefficients in 𝜍.
Unless otherwise specified, we only consider the polynomials in ℤ[𝑥].
2.2. Some Definitions
Definition 1: [Hermite Normal Form] Given a square matrix 𝑯 ∈ ℤ𝑛×𝑛
. Then 𝑯 is Hermite
Normal Form(HNF) if and only if it satisfies:
1) ℎ𝑖,𝑖 ≥ 1, for1 ≤ 𝑖 ≤ 𝑛;
2) ℎ𝑖,𝑗 = 0, for 1 ≤ 𝑗 ≤ 𝑖 ≤ 𝑛;
3) ℎ𝑗,𝑖 < ℎ𝑖,𝑖, for 1 ≤ 𝑗 < 𝑖 ≤ 𝑛.
We need to emphasize that the definition given above is only one way to define HNF. According
to row or column transformation and upper or lower triangularization, there are other different
definitions of HNF.

3
Definition 2: [Primitive Polynomial] A polynomial 𝑎(𝑥) ∈ ℤ[𝑥] is called a primitive polynomial
if for any integer |𝑘| > 1, 𝑎(𝑥)/𝑘 ∉ ℤ[𝑥].
Definition 3: [Content] For a polynomial 𝑎(𝑥) = 𝑎𝑛𝑥𝑛
+ ⋯ + 𝑎1𝑥 + 𝑎0 ∈ ℤ[𝑥], the content of
𝑎(𝑥), denoted as𝑐𝑜𝑛𝑡(𝑎(𝑥)), is the g.c.d of (𝑎𝑛, ⋯ , 𝑎1, 𝑎0).
Definition 3: [Resultant] Let 𝑓(𝑥) = 𝑓
𝑛𝑥𝑛
+ ⋯ + 𝑓1𝑥 + 𝑓0, 𝑔(𝑥) = 𝑔𝑚𝑥𝑚
+ ⋯ + 𝑔1𝑥 + 𝑔0 be
two polynomials with degree 𝑛 and 𝑚 respectively. Define the Sylvester matrix of 𝑓(𝑥) and 𝑔(𝑥)
as
𝐒𝐲𝐥𝐯(𝑓, 𝑔) =
[
𝑥𝑚−1
𝑓(𝑥)
𝑥𝑚−2
𝑓(𝑥)
⋮
𝑓(𝑥)
𝑥𝑛−1
𝑔(𝑥)
𝑥𝑛−2
𝑔(𝑥)
⋮
𝑔(𝑥) ]
=
[
𝑓
𝑛 𝑓𝑛−1 ⋯ 𝑓0
𝑓
𝑛 𝑓𝑛−1 ⋯ 𝑓0
⋱ ⋱
𝑓
𝑛 𝑓𝑛−1 ⋯ 𝑓0
𝑔𝑚 𝑔𝑚−1 ⋯ 𝑔1 𝑔0
𝑔𝑚 𝑔𝑚−1 ⋯ 𝑔1 𝑔0
⋱ ⋱
𝑔𝑚 𝑔𝑚−1 ⋯ 𝑔1 𝑔0](𝑚+𝑛)×(𝑚+𝑛)
Then the resultant of 𝑓(𝑥) and 𝑔(𝑥) , denoted as Res(𝑓(𝑥), 𝑔(𝑥)) , is the determinant of
𝐒𝐲𝐥𝐯(𝑓, 𝑔).
Definition 3: [Subresultant] Let 𝑓(𝑥) = 𝑓
𝑛𝑥𝑛
+ ⋯ + 𝑓1𝑥 + 𝑓0 , 𝑔(𝑥) = 𝑔𝑚𝑥𝑚
+ ⋯ + 𝑔1𝑥 + 𝑔0
be two polynomials with degree 𝑛 and 𝑚 respectively.For 0 ≤ 𝑘 < 𝑛, the 𝑘-th subresultant of
𝑓(𝑥) and 𝑔(𝑥) is the determinant of 𝑆𝑘(𝑓, 𝑔) defines as
𝑆𝑘(𝑓, 𝑔) =
[
𝑓
𝑛 𝑓𝑛−1 ⋯ 𝑓𝑛−𝑚+𝑘+1 ⋯ 𝑓𝑘+1 ⋯ 𝑓2𝑘−𝑚+1
𝑓
𝑛 ⋯ 𝑓𝑛−𝑚+𝑘+2 ⋯ 𝑓𝑘+2 ⋯ 𝑓2𝑘−𝑚+2
⋱ ⋮ ⋮ ⋮
𝑓
𝑛 ⋯ 𝑓
𝑚 ⋯ 𝑓𝑘
𝑔𝑚 𝑔𝑚−1 ⋯ 𝑔𝑘+1 ⋯ 𝑔𝑚−𝑛+𝑘+1 ⋯ 𝑔2𝑘−𝑛+1
𝑔𝑚 ⋮ ⋮ ⋮
⋱ ⋮ ⋮ ⋮
𝑔𝑚 ⋮ ⋮
⋱ ⋮ ⋮
𝑔𝑚 ⋯ 𝑔𝑘 ](𝑚+𝑛−2𝑘)×(𝑚+𝑛−2𝑘)

4
Remark 1. From the structure of 𝑆𝑘(𝑓, 𝑔) and 𝐒𝐲𝐥𝐯(𝑓, 𝑔), we can tell that indeed if we delete the
last 2𝑘 columns and the last 𝑘 rows of 𝑓(𝑥) and 𝑔(𝑥) respectively in 𝐒𝐲𝐥𝐯(𝑓, 𝑔), we obtain
𝑆𝑘(𝑓, 𝑔). Expecially, 𝑆0(𝑓, 𝑔) = 𝐒𝐲𝐥𝐯(𝑓, 𝑔).
Next we will give the conception of ideal lattice which takes an important role in the lattice-based
cryptography, and we mainly focus on the cases in which an ideal lattice can be derived from
𝑓(𝑥) and 𝑔(𝑥).
Ideal Lattice We define ideal lattice over a ring 𝑅 = ℤ[𝑥]/〈𝑓(𝑥)〉, where 𝑓(𝑥) ∈ ℤ[𝑥] is a monic
and irreducible polynomial of degree 𝑛 and 〈𝑓(𝑥)〉 is the ideal generated by 𝑓(𝑥) ∈ ℤ[𝑥].
Consider the coefficient embedding
𝜎：𝑅 ↦ ℤ𝑛
∑ 𝑎𝑖𝑥𝑖
𝑛−1
𝑖=0
↦ (𝑎𝑛−1, 𝑎𝑛−2, ⋯ , 𝑎0)
From [5], we know that the ideal generated by 𝑔(𝑥) forms a lattice under 𝜎 and we call it the
ideal lattice ℒ generated by 𝑔(𝑥) . Moreover, 𝑔(𝑥)mod𝑓(𝑥) , 𝑥𝑔(𝑥)mod𝑓(𝑥) , ⋯ ,
𝑥𝑛−1
𝑔(𝑥)mod𝑓(𝑥) form a basis of ℒ. As we can see, the basis is closely related to the Sylvester
matrix of 𝑓(𝑥) and 𝑔(𝑥). When 𝑓(𝑥) and 𝑔(𝑥) are coprime over ℚ[𝑥], the ideal lattice is full-
rank.
Then we present a lemma in [5] that we will use later.
Lemma 1. Let ℒ be the ideal lattice generated by 𝑔(𝑥) ∈ 𝑅 = ℤ[𝑥]/〈𝑓(𝑥)〉, where 𝑓(𝑥) is a
monic polynomial of degree 𝑛 and is relatively prime to 𝑔(𝑥). Then the Hermite Normal Form of
a basis of ℒ
𝐻 =
[
ℎ1,1 ℎ1,2 ⋯ ℎ1,𝑛
ℎ2,2 ⋯ ℎ2,𝑛
⋱ ⋮
ℎ𝑛,𝑛]
satisfies ℎ𝑖,𝑖|ℎ𝑙,𝑗, for 1 ≤ 𝑖 ≤ 𝑙 ≤ 𝑗 ≤ 𝑛.
2.3. The Classical Euclidean Algorithm of Polynomials over A Field
Given a field 𝔽. Let 𝑓(𝑥) and 𝑔(𝑥) ∈ 𝔽[𝑥] with 𝑑𝑒𝑔(𝑓) > 𝑑𝑒𝑔(𝑔). Then the division of 𝑓(𝑥)
and 𝑔(𝑥) yields a unique quotient 𝑄(𝑥) and remainder 𝑅(𝑥) such that
𝑓(𝑥) = 𝑄(𝑥)𝑔(𝑥) + 𝑅(𝑥)
here 𝑑𝑒𝑔(𝑔) > 𝑑𝑒𝑔(𝑟), 𝑑𝑒𝑔(𝑞) = 𝑑𝑒𝑔(𝑓) − 𝑑𝑒𝑔(𝑔).
If we repeat the step for each divisor polynomial and remainder, we will obtain a sequence of
remainders with decreasing degrees. Formally, a detailed procedure of the Euclidean algorithm of
polynomials over a field is present as following:

5
{
𝑓(𝑥) = 𝑄1(𝑥)𝑔(𝑥) + 𝑅1(𝑥)
𝑔(𝑥) = 𝑄2(𝑥)𝑅1(𝑥) + 𝑅2(𝑥)
⋮
𝑅𝑙−2(𝑥) = 𝑄𝑙(𝑥)𝑅𝑙−1(𝑥) + 𝑅𝑙(𝑥)
𝑅𝑙−1(𝑥) = 𝑄𝑙+1(𝑥)𝑅𝑙(𝑥)
where𝑑𝑒𝑔(𝑔) > 𝑑𝑒𝑔(𝑅1) > ⋯ > 𝑑𝑒𝑔(𝑅𝑙) and all the coefficients are in the given field. Note
that if deg(𝑅𝑙) = 0, it shows that 𝑓(𝑥) and 𝑔(𝑥) are coprime in 𝔽[𝑥], which means the resultant
of 𝑓(𝑥) and 𝑔(𝑥) is nonzero.
2.4. Polynomial Remainder Sequence
The procedure of the Euclidean algorithm of polynomials over a unique factorization domain
(UFD) is similar to the one over a field. The difference exits because the division between two
polynomials requires exact divisibility in the given domain, which is usually impossible to
realize. To solve the problem, the procedure of pseudo-division is proposed, which yields a
unique pseudo-quotient 𝑞(𝑥) and pseudo-remainder 𝑟(𝑥) such that
(lc(𝑔))
𝛿+1
𝑓(𝑥) = 𝑞(𝑥)𝑔(𝑥) + 𝑟(𝑥)
here 𝑑𝑒𝑔(𝑔) > 𝑑𝑒𝑔(𝑟), 𝛿 = 𝑑𝑒𝑔(𝑓) − 𝑑𝑒𝑔(𝑔), 𝑟(𝑥) is equivalent with prem(𝑓, 𝑔). Moreover,
the coefficients of 𝑞(𝑥) and 𝑟(𝑥) are in the given domain.
For nonzero polynomials 𝑎(𝑥), 𝑏(𝑥) ∈ 𝜍[𝑥], we say 𝑎(𝑥) is similar to 𝑏(𝑥)(𝑎(𝑥)~𝑏(𝑥)) if there
exist 𝑐1, 𝑐2 ∈ 𝜍 such that 𝑐1𝑎(𝑥) = 𝑐2𝑏(𝑥). So if we choose 𝑟′(𝑥) that is similar to 𝑟(𝑥), we can
do the same step as above for 𝑔(𝑥) and 𝑟′(𝑥). Thus, we can rewrite the procedure of pseudo-
division:
𝛼𝑓(𝑥) = 𝑞(𝑥)𝑔(𝑥) + 𝛽𝑟(𝑥).
Then the detailed procedure of pseudo-division is present as following:
{
𝛼1𝑓(𝑥) = 𝑞1(𝑥)𝑔(𝑥) + 𝛽1𝑟1(𝑥)
𝛼2𝑔(𝑥) = 𝑞2(𝑥)𝑟1(𝑥) + 𝛽2𝑟2(𝑥)
⋮
𝛼𝑙𝑟𝑙−2(𝑥) = 𝑞𝑙(𝑥)𝑟𝑙−1(𝑥) + 𝛽𝑙𝑟𝑙(𝑥)
𝛼𝑙+1𝑟𝑙−1(𝑥) = 𝑞𝑙+1(𝑥)𝑟𝑙(𝑥)
here 𝑑𝑒𝑔(𝑔) > 𝑑𝑒𝑔(𝑟1) > ⋯ > 𝑑𝑒𝑔(𝑟𝑙) and all the𝛼𝑖 and 𝛽𝑖 are in the given domain.
Generally, we denote 𝑓(𝑥) = 𝑟−1(𝑥) and 𝑔(𝑥) = 𝑟0(𝑥), then 𝛼𝑖 = (𝑙𝑐(r𝑖−1))
𝛿𝑖−2−1
, where 𝛿𝑖 =
𝑑𝑒𝑔(𝑟𝑖) − 𝑑𝑒𝑔(𝑟𝑖+1). Note that now prem(𝑟𝑖−2, 𝑟𝑖−1) = 𝛽𝑖𝑟𝑖(𝑥). Then 𝑟−1(𝑥), 𝑟0(𝑥),⋯,𝑟𝑙(𝑥)
form a sequence called polynomial remainder sequence(PRS).
From [5], if a remainder 𝑟(𝑥) = 𝑠(𝑥)𝑓(𝑥) + 𝑡(𝑥)𝑔(𝑥) can derive a basis of ideal lattice, 𝑡(𝑥)
must be primitive. In this paper, we also want to obtain such remainders and we call these
remainders as primitive PRS of lattice (PPRSoL). Next, we give a result about 𝑠𝑖(𝑥) and 𝑡𝑖(𝑥).

6
Lemma 2. Let 𝑓(𝑥), 𝑔(𝑥) ∈ ℤ[𝑥] be two polynomials with degree 𝑛 and 𝑚 respectively, where
𝑛 > 𝑚 . Let 𝑟−1(𝑥), 𝑟0(𝑥), ⋯ , 𝑟𝑙(𝑥) be the remainders in procedure of pseudo-division. If
𝑑𝑒𝑔(𝑟𝑖) = 𝑛𝑖, then 𝑟𝑖(𝑥) = 𝑠𝑖(𝑥)𝑓(𝑥) + 𝑡𝑖(𝑥)𝑔(𝑥) satisfies 𝑑𝑒𝑔(𝑠𝑖) < 𝑚, 𝑑𝑒𝑔(𝑡𝑖) < 𝑛 and:
1) 𝑠𝑖(𝑥) = (𝛼𝑖𝑠𝑖−2(𝑥) − 𝑞𝑖(𝑥)𝑠𝑖−1(𝑥))/𝛽𝑖, 𝑡𝑖(𝑥) = (𝛼𝑖𝑡𝑖−2(𝑥) − 𝑞𝑖(𝑥)𝑡𝑖−1(𝑥))/𝛽𝑖
2) 𝑑𝑒𝑔(𝑠𝑖) = 𝑚 − 𝑑𝑒𝑔(𝑟𝑖−1),𝑑𝑒𝑔(𝑡𝑖) = 𝑛 − 𝑑𝑒𝑔(𝑟𝑖−1)
If we represent 𝑟𝑖(𝑥) = 𝑠𝑖(𝑥)𝑓(𝑥) + 𝑡𝑖(𝑥)𝑔(𝑥) under the embedding σ, for 𝑖 = −1, 0, ⋯ , 𝑙, then
we can denote 𝑟𝑖(𝑥) as a sequence of vectors and we use a matrix 𝑹 to represent 𝑟𝑖(𝑥) as
following:
[
𝑓𝑛 ⋯ 𝑓𝑛−𝑚+1 𝑓𝑛−𝑚 ⋯ 𝑓0
⋱ ⋱
𝑓𝑛 𝑓𝑛−1 ⋯ 𝑓𝑚−1 𝑓𝑚−2 ⋯ 𝑓1 𝑓0
𝑟0,𝑛0
⋯ 𝑟0,0
⋱
⋮ 𝑟0,𝑛0
⋯ 𝑟0,0
𝑟1,𝑛0
⋯ 𝑟1,0
𝟎 ⋱
𝑟1,𝑛1
⋯ 𝑟1,0
⋱
⋮ 𝑟𝑙,𝑛𝑙
⋱
𝑟𝑙,𝑛𝑙](𝑚+𝑛)×(𝑚+𝑛)
Also, we use 𝑺 and 𝑻 to represent the matrice denoting 𝑠𝑖(𝑥) and 𝑡𝑖(𝑥) respectively.
𝑻 =
[
1
⋱
1
𝑡1,𝑛−𝑛0
⋯ 𝑡1,0
⋱ ⋱
𝑡1,𝑛−𝑛0
⋯ 𝑡1,0
⋱
𝑡𝑙,𝑛−𝑛𝑙−1
⋯ 𝑡𝑙,0
⋱ ⋱
𝑡𝑙,𝑛−𝑛𝑙−1
⋯ 𝑡𝑙,0 ]𝑛×𝑛

7
𝑺 =
[
0
⋱
0
𝑠1,𝑚−𝑛0
⋯ 𝑠1,0
⋱ ⋱
𝑠1,𝑚−𝑛0
⋯ 𝑠1,0
⋱
𝑠𝑙,𝑚−𝑛𝑙−1
⋯ 𝑠𝑙,0
⋱ ⋱
𝑠𝑙,𝑚−𝑛𝑙−1
⋯ 𝑠𝑙,0 ]𝑛×𝑚
So if we give a matrix named 𝑺𝑻, then procedure of pseudo-division can be represent as a matrix
multiplication.
𝑺𝑻 = [
1
⋱ 𝟎
1
𝑺 𝑻
]
(𝑚+𝑛)×(𝑚+𝑛)
𝑺𝑻 ∙ 𝐒𝐲𝐥𝐯(𝑓, 𝑔) = 𝑹
Here we need to show that by elementary row transformation, 𝑺𝑻 can be transformed into
𝑺𝑻 = [
1
⋱ 𝟎
1
𝟎 𝑻
]
(𝑚+𝑛)×(𝑚+𝑛)
which means that the determinant of 𝑺𝑻 equals to the determinant of 𝑻. Also according to lemma
2, it turns out that after appropriate row switching, 𝑻 is actually an upper triangular matrix, thus
the determinant of 𝑻 is |∏ lc(𝑡𝑖)𝑛𝑖−1−𝑛𝑖
𝑙
𝑖=0 |.
In the following part, we introduce some typical PRSs which differs from each other by choosing
different 𝛽𝑖.
2.4.1. Euclidean Polynomial Remainder Sequences
When choosing 𝛽𝑖 = 1 for all 𝑖 in PRS, we obtain Euclidean PRS. This is a generalization of the
Euclidean algorithm over integers. However, the algorithm is quite inefficient because with the
proceeding of the sequence, the coefficients of the remainders grow exponentially. To be
specific, we need to calculate each 𝑡𝑖(𝑥) and cont(𝑡𝑖(𝑥)) to get a eligible PPRSoL, which costs
too much. So we need to determine certain 𝛽𝑖 to ensure the efficiency.

8
2.4.2. Primitive Polynomial Remainder Sequences
When choosing 𝛽𝑖 = cont(prem(𝑟𝑖−2, 𝑟𝑖−1)) for all 𝑖 in PRS, we obtain primitive PRS. Although
the algorithm stops the coefficients growing exponentially in every step of the pseudo-division,
however, when proceeding primitive PRS, the coefficients of 𝑠𝑖(𝑥) and 𝑡𝑖(𝑥) may be not in the
given domain, which means that the PRS we obtain is not PPRSoL. So primitive PRS doesn't
satisfy our requirement.
2.4.3. Subresultant Polynomial Remainder Sequences
When 𝛽𝑖 is related to the subresultant, we obtain subresultant PRS. The equation set as following
depicts the procedure of the subresultant PRS algorithm in [2].
{
𝛼′1𝑓(𝑥) = 𝑞′1(𝑥)𝑔(𝑥) + 𝛽′1𝑟′1(𝑥)
𝛼′2𝑔(𝑥) = 𝑞′2(𝑥)𝑟1(𝑥) + 𝛽′2𝑟′2(𝑥)
⋮
𝛼′𝑙𝑟′𝑙−2(𝑥) = 𝑞′𝑙(𝑥)𝑟𝑙−1(𝑥) + 𝛽′𝑙𝑟′𝑙(𝑥)
where 𝑟−1(𝑥) = 𝑓(𝑥), 𝑟0(𝑥) = 𝑔(𝑥), 𝑛𝑖 = 𝑑𝑒𝑔(𝑟′𝑖), 𝛿𝑖 = 𝑛𝑖 − 𝑛𝑖+1 , 𝛼′𝑖 = (lc(𝑟′𝑖−1))
𝛿𝑖−2+1
,
𝛽′𝑖 = lc(𝑟′𝑖−2)ℎ𝑖
𝛿𝑖−2
, ℎ1 = 1, ℎ𝑖 = (𝑙𝑐(𝑟′𝑖−2))𝛿𝑖−3ℎ𝑖−1
1−𝛿𝑖−3
, for 2 ≤ 𝑖 ≤ 𝑙 + 1.
Intuitively, the intact subresultant algorithm can be present in Algorithm 1. We point out that
because we want to get PPRSoL, the input of every PRS algorithm in the paper contains a monic
and irreducible polynomial.
Algorithm 1 Subresultant PRS Algorithm
Input: two polynomials 𝑓(𝑥), 𝑔(𝑥) ∈ ℤ[𝑥] with degree 𝑛 and 𝑚 respectively and 𝑓(𝑥) is monic and
irreducible
Output: Subresultant PRS, 𝑟′0(𝑥), 𝑟′1(𝑥), ⋯
1.[Initialize] 𝑙 ← ℎ ← 1, 𝑟′0(𝑥) = 𝑔(𝑥),𝑖 ← 1
2.[Pseudo-division]
2.1 Set δ = 𝑑𝑒𝑔(𝑓) − 𝑑𝑒𝑔(𝑔)
2.2 Calculate 𝑟(𝑥) such that 𝑟(𝑥) = 𝑠(𝑥)𝑓(𝑥) + 𝑡(𝑥)𝑔(𝑥)
3.[Adjust remainder]
3.1 𝑢(𝑥) ← 𝑔(𝑥), 𝑟′𝑖(𝑥) ← 𝑔(𝑥) ← 𝑟(𝑥)/𝑙ℎ𝛿
3.2 𝑙 ← 𝑙𝑐(𝑓),ℎ ← ℎ1−𝛿
𝑙𝛿
3.3 If 𝑑𝑒𝑔(𝑟) = 0, go to Step 4
3.4 𝑖 ← 𝑖 + 1, go to Step 2
4.[Return] 𝑟′0(𝑥), 𝑟′1(𝑥), ⋯
Notice that for 𝑟′𝑖(𝑥) = 𝑠′𝑖(𝑥)𝑓(𝑥) + 𝑡′𝑖(𝑥)𝑔(𝑥) , 𝑡′𝑖(𝑥) maybe not primitive in the given
domain, which means that we can still decrease the coefficients of 𝑟′𝑖(𝑥) by removing a factor.
In [6], the author shows that the ℎ𝑖 is indeed the 𝑛𝑖−1-th subresultant of 𝑓(𝑥) and 𝑔(𝑥), that is
ℎ𝑖 = 𝑆𝑛𝑖−1
(𝑓, 𝑔). Also, in [3], the author shows that every ℎ𝑖 is an integer and 𝑟′𝑖(𝑥) ∈ ℤ[𝑥] and
gives an elegant proof.

9
2.4.4. Improvements of Subresultant Polynomial Remainder Sequences
This is another expression of subresultant PRS. As stated above, for the output of Algorithm 1,
𝑟′𝑖(𝑥) = 𝑠′𝑖(𝑥)𝑓(𝑥) + 𝑡′𝑖(𝑥)𝑔(𝑥), 𝑡′𝑖(𝑥) maybe not primitive and there might exist a divisor 𝜏𝑖
such that 𝑡𝑖(𝑥) = 𝑡′𝑖(𝑥)/𝜏𝑖 is primitive. So in the improvement version, the author transforms the
procedure of the subresultant PRS algorithm as following,
{
𝛼1𝑓(𝑥) = 𝑞1(𝑥)𝑔(𝑥) + 𝛽1𝑟1(𝑥)
𝛼2𝑔(𝑥) = 𝑞2(𝑥)𝑟1(𝑥) + 𝛽2𝑟2(𝑥)
⋮
𝛼𝑙𝑟𝑙−2(𝑥) = 𝑞𝑙(𝑥)𝑟𝑙−1(𝑥) + 𝛽𝑙𝑟𝑙(𝑥)
where 𝑟−1(𝑥) = 𝑓(𝑥) , 𝑟0(𝑥) = 𝑔(𝑥) , ℎ1 = 1 , 𝑛𝑖 = 𝑑𝑒𝑔(𝑟𝑖) , 𝛿𝑖 = 𝑛𝑖 − 𝑛𝑖+1 , 𝛼𝑖 =
(𝑙𝑐(𝑟𝑖−1))
𝛿𝑖−2+1
, 𝛽𝑖 = 𝑙𝑐(𝑟𝑖−2)ℎ𝑖
𝛿𝑖−2
𝜏𝑖−1
−𝛿𝑖−2−1
𝜏𝑖 , ℎ𝑖 = (𝜏𝑖−2𝑙𝑐(𝑟𝑖−2))𝛿𝑖−3ℎ𝑖−1
1−𝛿𝑖−3
, for 2 ≤
𝑖 ≤ 𝑙 + 1. 𝜏𝑖 is an integer such that 𝑡′𝑖(𝑥)/𝜏𝑖is a primitive polynomial. Clearly, 𝜏0 = 1. In [3], the
author chose 𝜏𝑖 = lc(𝑟𝑖−1) if lc(𝑟𝑖−1)|𝑟′𝑖(𝑥), otherwise 𝜏𝑖 = 1. However, the method to choose 𝜏𝑖
doesn't work for every 𝜏𝑖.
Comparing the two subresultant algorithms, we need to emphasis that all the ℎ𝑖s are equal in the
two algorithms.
3. SOME PROPERTIES OF THE SUB RESULTANT POLYNOMIAL REMAINDER
SEQUENCE
Before presenting our algorithm, we give some results about the subresultant PRS.
Proposition 1. Given two polynomials 𝑎(𝑥) = 𝑎𝑛𝑥𝑛
+ ⋯ + 𝑎1𝑥 + 𝑎0 and 𝑏(𝑥) = 𝑏𝑚𝑥𝑚
+ ⋯ +
𝑏1𝑥 + 𝑏0 ∈ ℤ[𝑥], where 𝑛 > 𝑚. Write 𝑏𝑚
𝑛−𝑚+1
𝑎(𝑥) = 𝑞(𝑥)𝑏(𝑥) + 𝑟(𝑥). Define the matrix
If the determinant of the matrix 𝑴𝑖 is denoted as ∆𝑖 , where 𝑴𝑖 is the 𝑖 × 𝑖 submatrix of 𝑴
obtained by deleting the last (𝑛 − 𝑚 + 2 − 𝑖) rows and the last (𝑛 + 1 − 𝑖) columns from 𝑴, 𝑖 =
0, … , 𝑛 − 𝑚 + 1 . Then 𝑞(𝑥) = ∑ ∆𝑛−𝑚+1−𝑖𝑏𝑚
𝑖
𝑥𝑖
𝑛−𝑚
𝑖=0 . Moreover, we have
(𝑐𝑜𝑛𝑡(𝑎(𝑥))𝑐𝑜𝑛𝑡(𝑏(𝑥))
𝑛−𝑚
)|𝑞(𝑥).
Proof. We first give the detail of the pseudo-division procedure,
{
𝑏𝑚𝑎(𝑥) = 𝑎𝑛𝑥𝑛−𝑚
𝑏(𝑥) + 𝑅1(𝑥)
𝑏𝑚𝑅1(𝑥) = 𝑙𝑐(𝑅1)𝑥𝑛−𝑚−1
𝑏(𝑥) + 𝑅2(𝑥)
⋮
𝑏𝑚𝑅𝑛−𝑚−1(𝑥) = 𝑙𝑐(𝑅𝑛−𝑚−1)𝑥𝑏(𝑥) + 𝑅𝑛−𝑚(𝑥)
𝑏𝑚𝑅𝑛−𝑚(𝑥) = 𝑙𝑐(𝑅𝑛−𝑚)𝑥𝑏(𝑥) + 𝑟(𝑥)

10
We denote 𝑅0(𝑥) = 𝑎(𝑥) and 𝑅𝑛−𝑚+1(𝑥) = 𝑟(𝑥), then we claim that 𝑅𝑖(𝑥) = ∑ ∆
̅𝑖,𝑗𝑥𝑗
𝑛−𝑖
𝑗=0 ,
where ∆
̅𝑖,𝑗 is the determinant of the (𝑖 + 1) × (𝑖 + 1) matrix 𝑀𝑖,𝑗 obtained by deleting the last
(𝑛 − 𝑚 + 1 − 𝑖) rows and the last (𝑛 + 1 − 𝑖) columns except column (𝑛 + 1 − 𝑗) from 𝑀, 𝑖 =
0, … , 𝑛 − 𝑚 + 1, 𝑗 = 0, … , 𝑛 − 𝑖. Clearly, ∆𝑖+1= ∆
̅𝑖,𝑛−𝑖.
Then we explain the claim by induction on 𝑖, 𝑖 = 0, … , 𝑛 − 𝑚 + 1.
For 𝑖 = 0, we have 𝑅0(𝑥) = 𝑎(𝑥) and it's obvious that 𝑎𝑗 = ∆
̅0,𝑗 for 𝑗 = 0, … , 𝑛.
Next we assume that the claim holds for 𝑖 = 𝑘 − 1. Then we denote 𝑏𝑚𝑅𝑘−1(𝑥) and 𝑏(𝑥) as
following,
[
𝑏𝑚∆
̅𝑘−1,𝑛+1−𝑘 𝑏𝑚∆
̅𝑘−1,𝑛−𝑘 ⋯ 𝑏𝑚∆
̅𝑘−1,𝑛−𝑚+1−𝑘 ⋯ ⋯ 𝑏𝑚∆
̅𝑘−1,1 𝑏𝑚∆
̅𝑘−1,0
𝑏𝑚 𝑏𝑚−1 ⋯ 𝑏0 0 ⋯ 0 0
]
Then the coefficient of 𝑥𝑛−𝑘+1−𝑖
in 𝑅𝑘(𝑥) is 𝑏𝑚∆
̅𝑘−1,𝑛+1−𝑘−𝑖 − 𝑏𝑚−𝑖∆
̅𝑘−1,𝑛+1−𝑘 if 1 ≤ 𝑖 ≤ 𝑚
and 𝑏𝑚∆
̅𝑘−1,𝑛+1−𝑘−𝑖 otherwise. According to the structure of 𝑀 we know that the coefficient of
𝑥𝑛−𝑘+1−𝑖
is exactly ∆
̅𝑘,𝑛−𝑘+1−𝑖. So the claim holds.
From the claim we have 𝑙𝑐(𝑅𝑖) = ∆
̅𝑖,𝑛−𝑖= ∆𝑖+1 , so 𝑞(𝑥) = ∑ 𝑙𝑐(𝑅𝑛−𝑚−𝑖)𝑏𝑚
𝑖
𝑥𝑖
𝑛−𝑚
𝑖=0 =
∑ ∆𝑛−𝑚+1−𝑖𝑏𝑚
𝑖
𝑥𝑖
𝑛−𝑚
𝑖=0 .
Then from the structure of 𝑀𝑖 , we know (𝑐𝑜𝑛𝑡(𝑎(𝑥))𝑐𝑜𝑛𝑡(𝑏(𝑥))
𝑛−𝑚−𝑖
)|∆𝑛−𝑚+1−𝑖 . So
(𝑐𝑜𝑛𝑡(𝑎(𝑥))𝑐𝑜𝑛𝑡(𝑏(𝑥))
𝑛−𝑚
)|∆𝑛−𝑚+1−𝑖𝑏𝑚
𝑖
, which means (𝑐𝑜𝑛𝑡(𝑎(𝑥))𝑐𝑜𝑛𝑡(𝑏(𝑥))
𝑛−𝑚
)|𝑞(𝑥).
Proposition 2. Let 𝑟1(𝑥), ⋯ , 𝑟𝑙(𝑥) be the remainders obtained in improved subresultant algorithm.
Present 𝑟𝑖(𝑥) = 𝑠𝑖(𝑥)𝑓(𝑥) + 𝑡𝑖(𝑥)𝑔(𝑥), for 𝑖 = 1, ⋯, 𝑙. Then we have 𝑙𝑐(𝑡𝑖) =
ℎ𝑖+1
𝜏𝑖
.
Proof. According to Lemma 2, 𝑡𝑖(𝑥) =
1
𝛽𝑖
(𝛼𝑖𝑡𝑖−2(𝑥) − 𝑞𝑖(𝑥)𝑡𝑖−1(𝑥)) and 𝑑𝑒𝑔(𝑡𝑖) = 𝑛 − 𝑛𝑖−1.
Also 𝑑𝑒𝑔(𝑞𝑖) = 𝛿𝑖−2 , so 𝑑𝑒𝑔(𝑡𝑖) = 𝑛 − 𝑛𝑖−3 < 𝑑𝑒𝑔(𝑞𝑖𝑡𝑖−1) = 𝑛 − 𝑛𝑖−1 . Then 𝑙𝑐(𝑡𝑖) =
1
𝛽𝑖
𝑙𝑐(𝑞𝑖) 𝑙𝑐(𝑡𝑖−1), so 𝑙𝑐(𝑞𝑖) =
𝑙𝑐(𝑟𝑖−2)
𝛼𝑖. Then 𝑙𝑐(𝑡𝑖) =
𝑙𝑐(𝑟𝑖−2)𝛼𝑖
𝑙𝑐(𝑟𝑖−1)𝛽𝑖
𝑙𝑐(𝑡𝑖−1) =
𝛼1…𝛼𝑖
𝛽1…𝛽𝑖𝑙𝑐(𝑟𝑖−1)
.
Because 𝛼𝑖 = (𝑙𝑐(𝑟𝑖−1))
𝛿𝑖−2+1
, 𝛽𝑖 = 𝑙𝑐(𝑟𝑖−1)ℎ𝑖
𝛿𝑖−2
𝜏𝑖−1
−𝛿𝑖−2−1
𝜏𝑖, then we have
𝑙𝑐(𝑡𝑖) =
1
(𝜏𝑖−1 𝑙𝑐(𝑟𝑖−1))𝛿𝑖−2+1
𝑙𝑐(𝑟𝑖−2) ℎ𝑖
𝛿𝑖−2
𝜏𝑖
(𝜏𝑖−2 𝑙𝑐(𝑟𝑖−2))𝛿𝑖−3+1
𝑙𝑐(𝑟𝑖−3) ℎ𝑖−1
𝛿𝑖−3
𝜏𝑖−1
…
(𝜏0 𝑙𝑐(𝑟0))𝛿−1+1
𝑙𝑐(𝑟−1) ℎ1
𝛿−1
𝜏1
=
1
𝜏𝑖
(𝜏𝑖−1 𝑙𝑐(𝑟𝑖−1))𝛿𝑖−2
ℎ𝑖
𝛿𝑖−2
ℎ𝑖−1
𝛿𝑖−3
…
(𝜏0 𝑙𝑐(𝑟0))𝛿−1
ℎ1
𝛿−1
=
1
𝜏𝑖
ℎ𝑖
𝛿𝑖−2
ℎ𝑖−1
𝛿𝑖−3
…
(𝜏0 𝑙𝑐(𝑟1))𝛿0
ℎ1
𝛿−1
ℎ2
= ⋯ =
ℎ𝑖+1
𝜏𝑖
Remark 2. If we do similar steps for 𝑟′0(𝑥), 𝑟′1(𝑥), ⋯ , 𝑟′𝑙(𝑥) in Algorithm 1 and present each
𝑟′𝑖(𝑥) = 𝑠′𝑖(𝑥)𝑓(𝑥) + 𝑡′𝑖(𝑥)𝑔(𝑥), then we obtain 𝑙𝑐(𝑡′𝑖) = ℎ𝑖+1.
Before giving next lemmas, we first present a useful algorithm from [5]. We use the same
symbols in [5], {𝑛 − 𝑛𝑖−1 + 1, ⋯ , 𝑛 − 𝑛𝑖} = 𝐼𝑖, then {1,2, ⋯, 𝑛} = ⋃ 𝐼𝑖
𝑙
𝑖+1 .

11
Algorithm 2 A Useful Algorithm
Input:𝑟0(𝑥), 𝑟1(𝑥), ⋯ , 𝑟𝑙(𝑥)from improved subresultant algorithm
Output: 𝑟̅0(𝑥), 𝑟̅1(𝑥), ⋯ , 𝑟̅𝑙(𝑥)
1.When𝑘 ∈ 𝐼𝑙, 𝑟′𝑘(𝑥) = 𝑟𝑙(𝑥)𝑥𝑛−𝑘
,𝑖 ← 𝑙 − 1
2.When 𝑘 ∈ 𝐼𝑖
2.1 Set Compute 𝜙 and 𝜓, such that 𝜓𝑙𝑐(𝑟𝑖) + 𝜙𝑙𝑐(𝑟̅𝑖+1) = gcd(𝑙𝑐(𝑟𝑖), 𝑙𝑐(𝑟̅𝑖+1))
2.2 Set 𝑟̅𝑖(𝑥) = 𝜓𝑟𝑖(𝑥) + 𝜙𝑟̅𝑖+1(𝑥)𝑥𝛿𝑖
2.3 If 𝑙𝑐(𝑟̅𝑛−𝑛𝑖
) = 1, set 𝑟̅𝑗(𝑥) = 𝑟̅𝑛−𝑛𝑖
(𝑥)𝑥𝑛−𝑛𝑖−𝑗
, 𝑗 = 1, ⋯ , 𝑛 − 𝑛𝑖, go to Step 3; otherwise
𝑟̅𝑘(𝑥) = 𝑟̅𝑛−𝑛𝑖
(𝑥)𝑥𝑛−𝑛𝑖−𝑘
, 𝑖 ← 𝑙 − 1
2.4If 𝑖 > 0, go to Step 2, otherwise go to Step 3
4.Return𝑟̅0(𝑥), 𝑟̅1(𝑥), ⋯ , 𝑟̅𝑙(𝑥)
We need to explain that Algorithm 2 is equivalent to the corresponding algorithm in [4] and we
just use polynomials to express the output instead of a matrix in [4].
Then we will present some results of 𝑐𝑜𝑛𝑡(𝑟𝑖(𝑥))and 𝑙𝑐(𝑟̅𝑖).
Lemma 3. Let 𝑟1(𝑥), ⋯, 𝑟𝑙(𝑥) be the polynomial remainder sequence obtained in improved
subresultant algorithm. Then 𝑐𝑜𝑛𝑡(𝑟𝑖(𝑥))|𝑐𝑜𝑛𝑡(𝑟𝑖−1(𝑥)) for 0 ≤ 𝑖 ≤ 𝑙 − 1.
Proof. We prove this lemma by induction on 𝑖, 𝑖 = 0, … , 𝑙 − 1.
Suppose that 𝐻 is the Hermite Normal Form over the ideal lattice ℒ generated by 𝑔(𝑥) ∈
ℤ[𝑥]/〈𝑓(𝑥)〉, and 𝑟𝑖(𝑥) belongs to ℒ. When 𝑖 = 0, because 𝑟0(𝑥) generates the ideal lattice ℒ ,
then all the vectors in ℒ can be divided exactly by 𝑐𝑜𝑛𝑡(𝑟0(𝑥)).
Next we suppose that when 𝑖 ≤ 𝑘 − 1, 𝑐𝑜𝑛𝑡(𝑟𝑖−1(𝑥))|𝑐𝑜𝑛𝑡(𝑟𝑖(𝑥)), then we need to show that
𝑐𝑜𝑛𝑡(𝑟𝑘(𝑥))|𝑐𝑜𝑛𝑡(𝑟𝑘+1(𝑥)).
Consider the (𝑘 + 1)-th equation in improved subresultant algorithm,
𝛼𝑘+1𝑟𝑘−1(𝑥) = 𝑞𝑘+1(𝑥)𝑟𝑘(𝑥) + 𝛽𝑘+1(𝑥)𝑟𝑘+1(𝑥),
then we know 𝑐𝑜𝑛𝑡(𝑟𝑘−1(𝑥))𝑐𝑜𝑛𝑡(𝑟𝑘(𝑥))
𝛿𝑘−1+1
|𝛽𝑘+1𝑟𝑘+1(𝑥). Because
𝑡𝑘+1(𝑥) = (𝛼𝑘+1𝑡𝑘−1(𝑥) − 𝑞𝑘+1(𝑥)𝑡𝑘(𝑥)) 𝛽𝑘+1
⁄ ,
𝛽𝑘+1 must contain a factor as the content of 𝛼𝑘+1𝑡𝑘−1(𝑥) − 𝑞𝑘+1(𝑥)𝑡𝑘(𝑥) . Also 𝛼𝑘+1 =
𝑙𝑐(𝑟𝑘)𝛿𝑘−1+1
, (𝑐𝑜𝑛𝑡(𝑟𝑘−1(𝑥))𝑐𝑜𝑛𝑡(𝑟𝑘(𝑥))
𝛿𝑘−1
)|𝑞𝑘+1(𝑥) due to Proposition 1. Based on the
assumption(𝑐𝑜𝑛𝑡(𝑟𝑘−1(𝑥))|𝑐𝑜𝑛𝑡(𝑟𝑘(𝑥)), so (𝑐𝑜𝑛𝑡(𝑟𝑘−1(𝑥))𝑐𝑜𝑛𝑡(𝑟𝑘(𝑥))
𝛿𝑘−1
) |𝛽𝑘+1.
If (𝑐𝑜𝑛𝑡(𝑟𝑘(𝑥)) ∤ 𝑐𝑜𝑛𝑡(𝑟𝑘+1(𝑥)) , then there exists a prime 𝑎 such that 𝑎|𝑐𝑜𝑛𝑡(𝑟𝑘(𝑥)) and
(𝑎 ∙ 𝑐𝑜𝑛𝑡(𝑟𝑘−1(𝑥))𝑐𝑜𝑛𝑡(𝑟𝑘(𝑥))
𝛿𝑘−1
) |𝛽𝑘+1. We give 2 cases as following:
1) 𝑎 ∙ 𝑐𝑜𝑛𝑡(𝑟𝑘−1(𝑥)) ∤ 𝑐𝑜𝑛𝑡(𝑟𝑘(𝑥)) , which means that 𝑎 ∤ 𝑐𝑜𝑛𝑡(𝑟𝑘−1(𝑥)) and 𝑎 ∤
𝑐𝑜𝑛𝑡(𝑟𝑘(𝑥))
𝑐𝑜𝑛𝑡(𝑟𝑘−1(𝑥))
. According to Proposition 1, we know 𝑟𝑘+1(𝑥) = ∑ ∆
̅𝑛𝑘−1,𝑗 𝑥𝑗
𝛽𝑘+1
⁄
𝑛𝑘−1
𝑗=0 , here

12
∆
̅𝑛𝑘−1,𝑗 is the determinant of the (𝛿𝑘−1 + 2) × (𝛿𝑘−1 + 2) matrix obtained by deleting the
last 𝑛𝑘 columns except column 𝑛𝑘−1 + 1 − 𝑗 from 𝑀 , 𝑗 = 0, … , 𝑛𝑘 − 1 . Because 𝑎 ∤
𝑟𝑘−1(𝑥)
𝑐𝑜𝑛𝑡(𝑟𝑘−1(𝑥))
, there exits a 𝑗 > 1 such that 𝑎|𝑀𝑗(𝑥), which means 𝑎|
𝑙𝑐(𝑟𝑘)
𝑐𝑜𝑛𝑡(𝑟𝑘(𝑥))
. Thus we
obtan (𝑎 ∙ 𝑐𝑜𝑛𝑡(𝑟𝑘−1(𝑥))𝑐𝑜𝑛𝑡(𝑟𝑘(𝑥))
𝛿𝑘−1
) |𝛼𝑘+1 . According to equation 𝑡𝑘+1(𝑥) =
(𝛼𝑘+1𝑡𝑘−1(𝑥) − 𝑞𝑘+1(𝑥)𝑡𝑘(𝑥)) 𝛽𝑘+1
⁄ ,we have that
𝛿𝑘−1
) |𝑞𝑘+1(𝑥).
𝛿𝑘−1
) |𝛽𝑘+1𝑟𝑘+1(𝑥), which means we have get
𝑐𝑜𝑛𝑡(𝑟𝑘(𝑥))|𝑐𝑜𝑛𝑡(𝑟𝑘+1(𝑥)).
2) 𝑎 ∙ 𝑐𝑜𝑛𝑡(𝑟𝑘−1(𝑥))|𝑐𝑜𝑛𝑡(𝑟𝑘(𝑥)). Because 𝛼𝑘+1 = 𝑙𝑐(𝑟𝑘)𝛿𝑘−1+1
, then we have result that
𝑐𝑜𝑛𝑡(𝑟𝑘(𝑥))𝛿𝑘−1+1
|𝛼𝑘+1 , thus (𝑎 ∙ 𝑐𝑜𝑛𝑡(𝑟𝑘−1(𝑥))𝑐𝑜𝑛𝑡(𝑟𝑘(𝑥))
𝛿𝑘−1
) |𝛼𝑘+1 . As the same
step in case 1, we still get 𝑐𝑜𝑛𝑡(𝑟𝑘(𝑥))|𝑐𝑜𝑛𝑡(𝑟𝑘+1(𝑥)).
So in conclusion we obtain 𝑐𝑜𝑛𝑡(𝑟𝑘(𝑥))|𝑐𝑜𝑛𝑡(𝑟𝑘+1(𝑥)). The proof is completed.
resultant algorithm and 𝑟1
̅(𝑥), ⋯ , 𝑟𝑙
̅(𝑥) be the output of Algorithm 2. If
gcd(𝑙𝑐(𝑟𝑖),𝑐𝑜𝑛𝑡(𝑟𝑖+1(𝑥))) = 𝑐𝑜𝑛𝑡(𝑟𝑖(𝑥)) for 𝑖 ≤ 𝑙 − 1 , then 𝑙𝑐(𝑟̅𝑖) = 𝑐𝑜𝑛𝑡(𝑟𝑖(𝑥)) .
Moreover, 𝑙𝑐(𝑟̅𝑖)|𝑟𝑖
̅(𝑥).
Proof. We notice that from Algorithm 2, 𝑟̅𝑖(𝑥) = 𝜓𝑟𝑖(𝑥) + 𝜙𝑟̅𝑖+1(𝑥)𝑥𝛿𝑖 , where 𝜓 and 𝜙
satisfy 𝜓𝑙𝑐(𝑟𝑖) + 𝜙𝑙𝑐(𝑟̅𝑖+1) = gcd(𝑙𝑐(𝑟𝑖),𝑙𝑐(𝑟̅𝑖+1)) = 𝑙𝑐(𝑟̅𝑖) . If we already have
gcd(𝑙𝑐(𝑟𝑖),𝑐𝑜𝑛𝑡(𝑟𝑖+1(𝑥))) = 𝑐𝑜𝑛𝑡(𝑟𝑖(𝑥)) and 𝑐𝑜𝑛𝑡(𝑟𝑖+1(𝑥)) = 𝑙𝑐(𝑟̅𝑖+1), then we have 𝑙𝑐(𝑟̅𝑖) =
𝑐𝑜𝑛𝑡(𝑟𝑖(𝑥)).
When 𝑖 = 𝑙, this is a trivial result because 𝑙𝑐(𝑟̅𝑙) = 𝑟̅𝑙(𝑥) = 𝑐𝑜𝑛𝑡(𝑟𝑙(𝑥)). So we know 𝑙𝑐(𝑟̅𝑙−1) =
𝑐𝑜𝑛𝑡(𝑟𝑙−1(𝑥)),𝑙𝑐(𝑟̅𝑙−2) = 𝑐𝑜𝑛𝑡(𝑟𝑙−2(𝑥)), …,, and so on. Thus, if gcd(𝑙𝑐(𝑟𝑖), 𝑐𝑜𝑛𝑡(𝑟𝑖+1(𝑥))) =
𝑐𝑜𝑛𝑡(𝑟𝑖(𝑥)) for 𝑖 ≤ 𝑙 − 1, then 𝑙𝑐(𝑟̅𝑖) = 𝑐𝑜𝑛𝑡(𝑟𝑖(𝑥)).
For the second part, according to the assumption, gcd(𝑙𝑐(𝑟𝑖),𝑐𝑜𝑛𝑡(𝑟𝑖+1(𝑥))) = 𝑐𝑜𝑛𝑡(𝑟𝑖(𝑥)),
then 𝑐𝑜𝑛𝑡(𝑟𝑖(𝑥))|𝑐𝑜𝑛𝑡(𝑟𝑖+1(𝑥)) . Also 𝑟̅𝑖(𝑥) = 𝜓𝑟𝑖(𝑥) + 𝜙𝑟̅𝑖+1(𝑥)𝑥𝛿𝑖 , so 𝑐𝑜𝑛𝑡(𝑟𝑖(𝑥))|𝑟̅𝑖(𝑥) .
Due to 𝑙𝑐(𝑟̅𝑖) = 𝑐𝑜𝑛𝑡(𝑟𝑖(𝑥)), we know that 𝑙𝑐(𝑟̅𝑖) = 𝑐𝑜𝑛𝑡(𝑟𝑖(𝑥))|𝑟̅𝑖(𝑥), for 0 ≤ 𝑖 ≤ 𝑙.
resultant algorithm. Then gcd(𝑙𝑐(𝑟𝑖),𝑐𝑜𝑛𝑡(𝑟𝑖+1(𝑥))) = 𝑐𝑜𝑛𝑡(𝑟𝑖(𝑥)).
Proof. We prove this lemma by induction on 𝑖, 𝑖 = 1, … , 𝑙 − 1.
First, suppose that 𝑯 is the Hermite Normal Form of the ideal lattice ℒ generated by 𝑔(𝑥) ∈
ℤ[𝑥]/〈𝑓(𝑥)〉, and 𝑟𝑖(𝑥) belongs to ℒ. Denote
𝑙𝑐(𝑟𝑖)
𝑙𝑐(𝐻𝑛−𝑛𝑖
)
as 𝛾𝑖 and 𝑑𝑖 = 𝑛 − 𝑛𝑖, here 𝑯𝑖(𝑥) is the
corresponding polynomial of the 𝑖 -th row, then 𝑟𝑖(𝑥) = 𝛾𝑖𝑯𝑑𝑖
(𝑥) + ∑ 𝐴𝑖,𝑗(𝑥)
𝑙
𝑗=𝑖+1 𝑯𝑑𝑗
(𝑥) ,
where deg(𝐴𝑖,𝑗) < 𝑛𝑖 − 𝑛𝑗.

13
From Lemma 4, 𝑙𝑐 (𝑯𝑑𝑗
) |𝑯𝑑𝑗
(𝑥), for 𝑖 ≤ 𝑗 ≤ 𝑙 . So 𝑙𝑐(𝑯𝑑𝑖
)|𝑐𝑜𝑛𝑡(𝑟𝑖(𝑥)). Because 𝑟𝑖(𝑥) =
𝑡𝑖(𝑥)𝑔(𝑥)𝑚𝑜𝑑𝑓(𝑥) belongs to ℒ and 𝑡𝑖(𝑥) is primitive, then gcd(𝛾𝑖, 𝐴𝑖,𝑖+1(𝑥), … , 𝐴𝑖,𝑙(𝑥)) = 1,
thus there exists some 𝑖 < 𝑘 ≤ 𝑙 , 𝑐𝑜𝑛𝑡 (
𝑟𝑖(𝑥)
𝑙𝑐(𝐻𝑑𝑖
)
) = gcd(𝛾𝑖,
𝑙𝑐(𝐻𝑑𝑘
)
𝑙𝑐(𝐻𝑑𝑖
)
) , which means that
𝑐𝑜𝑛𝑡(𝑟𝑖(𝑥)) = gcd(𝑙𝑐(𝑟𝑖),𝑙𝑐(𝐻𝑑𝑘
)). So every content of 𝑟𝑖(𝑥) must be a factor of 𝑯𝑛. Specially,
we have 𝑐𝑜𝑛𝑡(𝑟𝑙−1(𝑥)) = 𝑙𝑐(𝑟̅𝑙−1), which shows that the result holds for 𝑖 = 𝑙 − 1.
Now assume that for 𝑖 ≥ 𝑘 , we have gcd(𝑙𝑐(𝑟𝑖),𝑐𝑜𝑛𝑡(𝑟𝑖+1(𝑥)) = 𝑐𝑜𝑛𝑡(𝑟𝑖(𝑥)) . Then from
Lemma 3, we have 𝑙𝑐(𝑟̅𝑖) = 𝑐𝑜𝑛𝑡(𝑟𝑖(𝑥)).
Next we consider 𝑘 − 1, from the Algorithm 2, gcd(𝑙𝑐(𝑟𝑘−1), 𝑙𝑐(𝑟̅𝑘)) = 𝑙𝑐(𝑟̅𝑘−1). Then because
𝑙𝑐(𝑟̅𝑖) = 𝑐𝑜𝑛𝑡(𝑟𝑖(𝑥)) for 𝑖 ≥ 𝑘, gcd(𝑙𝑐(𝑟𝑘−1), 𝑐𝑜𝑛𝑡(𝑟𝑘(𝑥))) = 𝑙𝑐(𝑟̅𝑘−1). So we need to show
𝑙𝑐(𝑟̅𝑘−1) = 𝑐𝑜𝑛𝑡(𝑟𝑘−1(𝑥)).
First, 𝑐𝑜𝑛𝑡(𝑟𝑘−1(𝑥))|𝑙𝑐(𝑟𝑘−1) and according to the Lemma 3, 𝑐𝑜𝑛𝑡(𝑟𝑘−1(𝑥))|𝑐𝑜𝑛𝑡(𝑟̅𝑘(𝑥)), so
𝑐𝑜𝑛𝑡(𝑟𝑘−1(𝑥))|gcd(𝑙𝑐(𝑟𝑘−1), 𝑐𝑜𝑛𝑡(𝑟𝑘(𝑥))) = 𝑙𝑐(𝑟̅𝑘−1) . We suppose 𝑙𝑐(𝑟̅𝑘−1) = 𝑎 ∙
𝑐𝑜𝑛𝑡(𝑟𝑘−1(𝑥)) for a prime 𝑎. According to Lemma 4, 𝑙𝑐(𝑟̅𝑖) = 𝑐𝑜𝑛𝑡(𝑟𝑖(𝑥)) = 𝑐𝑜𝑛𝑡(𝑟̅𝑖(𝑥)) for
𝑖 ≥ 𝑘 , so we have 𝑐𝑜𝑛𝑡(𝑟𝑘−1(𝑥))|𝑐𝑜𝑛𝑡(𝑟̅𝑘−1(𝑥)) . Also the step diminishes the leading
coefficient and 𝑙𝑐(𝑟̅𝑘−1)|𝑙𝑐(𝑟𝑘−1), then 𝑐𝑜𝑛𝑡(𝑟̅𝑘−1(𝑥)) ≤ 𝑐𝑜𝑛𝑡(𝑟𝑘−1(𝑥)). So 𝑐𝑜𝑛𝑡(𝑟𝑘−1(𝑥)) =
𝑐𝑜𝑛𝑡(𝑟̅𝑘−1(𝑥)).
Consider the𝑘-th equation in improved subresultant algorithm,
𝛼𝑘𝑟𝑘−2(𝑥) = 𝑞𝑘(𝑥)𝑟𝑘−1(𝑥) + 𝛽𝑘𝑟𝑘(𝑥)
here 𝛼𝑘 = 𝑙𝑐(𝑟𝑘−1)𝛿𝑘−2+1
. Because 𝑎 ∙ 𝑐𝑜𝑛𝑡(𝑟𝑘−1(𝑥))|𝑙𝑐(𝑟𝑘−1) and 𝑎 ∙
𝑐𝑜𝑛𝑡(𝑟𝑘−1(𝑥))|𝑐𝑜𝑛𝑡(𝑟𝑘(𝑥)) , we know that 𝑎 ∙ (𝑐𝑜𝑛𝑡(𝑟𝑘−1(𝑥)))
𝛿𝑘−2+1
|𝛼𝑘 and 𝑎 ∙
𝑐𝑜𝑛𝑡(𝑟𝑘−1(𝑥))|𝑟𝑘(𝑥) , which means, if we divide the equation above by 𝜇 = 𝑎 ∙
(𝑐𝑜𝑛𝑡(𝑟𝑘−1(𝑥)))
𝛿𝑘−2+1
𝑐𝑜𝑛𝑡(𝑟𝑘−2(𝑥)), then
𝛼𝑘𝑟𝑘−2(𝑥)
𝜇
and
𝛽𝑘𝑟𝑘(𝑥)
𝜇
both belong to ℤ[𝑥], while
𝑞𝑘(𝑥)𝑟𝑘−1(𝑥)
𝜇
doesn't. This is a contradiction. So the proof is completed.
Using the results above, we realize that 𝑙𝑐(𝑡𝑖) is related to 𝜏𝑖 which is the unknown. We tried
some equations and found the following equation,
gcd(𝑙𝑐(𝑡𝑖), 𝑙𝑐(𝑟̅𝑖−1)) = 𝑔𝑐𝑑 (
𝑙𝑐(𝑟̅𝑖−1)
, 𝑙𝑐(𝑟̅𝑖−1))
for 𝑖 = 0,1,2, ⋯, 𝑙.Also in our experiments, the conjecture hold with extremely high probability.
4. A NEW ALGORITHM FOR COMPUTATION OF POLYNOMIAL GREATEST
COMMON
In this section, we give a probabilistic subresultant algorithm by applying the results in the last
section. We need to emphasis that the algorithm is not deterministic yet. The detail of the
algorithm is present as following.

14
Algorithm 3 Probabilistic Subresultant Algorithm
Input: two polynomials 𝑓(𝑥), 𝑔(𝑥) ∈ ℤ[𝑥] with degree 𝑛 and 𝑚 respectively and 𝑓(𝑥) is monic and
irreducible
Output: Probabilistic subresultant PRS, 𝑟0(𝑥), 𝑟1(𝑥), ⋯
1.[Initialize] 𝑙 ← ℎ ← 1, 𝑢1(𝑥) ← 𝑓(𝑥),𝑢2(𝑥) ← 𝑔(𝑥),𝑖 ← 1
2.Compute 𝑙𝑐(𝑢2)𝛿+1
𝑢1(𝑥) − 𝑞(𝑥)𝑢2(𝑥) = 𝑟(𝑥), here 𝑑𝑒𝑔(𝑟) < 𝑑𝑒𝑔(𝑢2), 𝛿 = 𝑑𝑒𝑔(𝑢1) − 𝑑𝑒𝑔(𝑢2)
3.𝑢(𝑥) ← 𝑢1(𝑥), 𝑢1(𝑥) ← 𝑢2(𝑥), 𝑢2(𝑥) ← 𝑟(𝑥)
4.When 𝑑𝑒𝑔(𝑢2) ≠ 0,
4.1 𝑙 ← 𝑙𝑐(𝑢2), ℎ ← 𝑙𝛿
ℎ1−𝛿
4.2 𝜏 ← gcd(ℎ, 𝑐𝑜𝑛𝑡(𝑢2(𝑥))), 𝜏′ ← gcd(𝑙𝑐(𝑢)/𝑐𝑜𝑛𝑡(𝑢(𝑥)), 𝑐𝑜𝑛𝑡(𝑢(𝑥)))
4.3 𝜏 ←/𝜏′, 𝑟𝑖(𝑥) ← 𝑟(𝑥)/(𝑙ℎ𝛿
𝜏)
4.4 𝛿 = 𝑑𝑒𝑔(𝑢1) − 𝑑𝑒𝑔(𝑢2)
4.5 Compute 𝑙𝑐(𝑢2)𝛿+1
𝑢1(𝑥) − 𝑞(𝑥)𝑢2(𝑥) = 𝑟(𝑥), 𝑑𝑒𝑔(𝑟) < 𝑑𝑒𝑔(𝑢2), 𝛿 = 𝑑𝑒𝑔(𝑢1) − 𝑑𝑒𝑔(𝑢2)
4.6 𝑢(𝑥) ← 𝑢1(𝑥), 𝑢1(𝑥) ← 𝑢2(𝑥), 𝑢2(𝑥) ← 𝑟(𝑥)/(𝑙ℎ𝛿
)
4.7 𝑖 ← 𝑖 + 1
5.[Return] 𝑟0(𝑥), 𝑟1(𝑥), ⋯
For the often-used polynomials in ideal lattice-based cryptography 𝑥𝑛
+ 1 and 𝑥𝑛
− 𝑥 − 1, here
𝑛 is a power of 2, we give the experiment results. For each polynomial, we sample 10000
examples randomly with coefficients in the range [-20,20] and the correctness is present below.
Polynomial 𝑥𝑛
+ 1 𝑥𝑛
− 𝑥 − 1
Correctness 97.88% 99.73%
5. CONCLUSIONS
In this paper, we give some results about the contents and small factors of remainders during
Euclidean algorithm of polynomials. By applying these results, we proposed a probabilistic
subresultant which can output correct remainders with high probability.
Due to the case of failure, the next research will be focus on the exact expression of each 𝜏𝑖 and
relation between 𝑙𝑐(𝑡𝑖) and cont(𝑟𝑗(𝑥)) to obtain a determinisitic improved subresultant
algorithm.
REFERENCES
[1] G.E. Collins. Subresultants and reduced polynomial remainder sequences. J. ACM, 14(1): 128--142
(1967)
[2] D.E. Knuth. The art of computer programming. Seminumerical Algorithms. vol. 2, 3rd Edition, 1998
(1st Edition, 1969)
[3] W.S. Brown. The subresultant PRS algorithm. ACM Trans. Math. Software, 4(3):237--249 (1978)
[4] Y. Zhang, R.Z. Liu, D.D. Lin. Fast Triangularization of Ideal Latttice Basis. Journal of Electronics
and Information Technology, 42(1): 98-104 (2020)
[5] Y. Zhang, R.Z. Liu, D.D. Lin. Improved Key Generation Algorithm for Gentry's Fully Homomorphic
Encryption Scheme. ICISC: 97-111 (2018)
[6] J Gathen, T Lücking. Subresultants Revisited. Latin American Symposium on Theoretical
Informatics, LNCS 1776: 318–342

A Probabilistic Algorithm for Computation of Polynomial Greatest Common with Smaller Factors

More Related Content

Similar to A Probabilistic Algorithm for Computation of Polynomial Greatest Common with Smaller Factors (20)

More from mathsjournal (20)

Recently uploaded (20)

A Probabilistic Algorithm for Computation of Polynomial Greatest Common with Smaller Factors