#
LaurentPolynomials — Module.
This package implements univariate Laurent polynomials, and univariate rational fractions. The coefficients can be in any ring (possibly even non-commutative, like `Matrix{Int}).
The initial motivation in 2018 was to have an easy way to port GAP polynomials to Julia. The reasons for still having my own package are multiple:
- I need my polynomials to behave well when coefficients are in a ring, in which case I use pseudo-division and subresultant gcd.
- I need my polynomials to work as well as possible with coefficients of type
Twhere the elements have azeromethod butTitself does not have one, becauseTdoes not contain the necessary information. An example is modular arithmetic with aBigIntmodulus which cannot be part of the type. For this reason thezeropolynomial does not have an empty list of coefficients, but a list containing one element equal to zero, so it is always possible to get a zero of type T from the zero polynomial. LaurentPolynomialsis designed to be used byPuiseuxPolynomials.- In many cases, my polynomials are several times faster than those in the package
Polynomials. Also the interface is simple and flexible.
The only package on which this package depends is LinearAlgebra, through the use of the function exactdiv.
Laurent polynomials have the parametric type Pol{T}, where Tis the type of the coefficients. They are constructed by giving a vector of coefficients of type T, and a valuation (an Int). We call true polynomials those whose valuation is ≥0.
There is a current variable name (a Symbol) which is used to print polynomials nicely at the repl or in IJulia or Pluto. This name can be changed globally, or just for printing a specific polynomial. But polynomials do not record individually which symbol they should be printed with.
Examples
julia> Pol(:q) # define symbol used for printing and return Pol([1],1)
Pol{Int64}: q
julia> @Pol q # same as q=Pol(:q) useful to start session with polynomials
Pol{Int64}: q
julia> Pol([1,2]) # valuation is taken to be 0 if omitted
Pol{Int64}: 2q+1
julia> 2q+1 # same polynomial
Pol{Int64}: 2q+1
julia> Pol() # omitting all arguments gives Pol([1],1)
Pol{Int64}: q
julia> p=Pol([1,2,1],-1) # here the valuation is specified to be -1
Pol{Int64}: q+2+q⁻¹
julia> q+2+q^-1 # same polynomial
Pol{Int64}: q+2+q⁻¹julia> print(p) # if not nice printing give an output which can be read back
Pol([1, 2, 1],-1)
# change the variable for printing just this time
julia> print(IOContext(stdout,:limit=>true,:varname=>"x"),p)
x+2+x⁻¹
julia> print(IOContext(stdout,:TeX=>true),p) # TeXable output (used in Pluto, IJulia)
q+2+q^{-1}
A polynomial can be taken apart with the functions valuation, degree and getindex. An index p[i] gives the coefficient of degree i of p.
julia> valuation(p),degree(p)
(-1, 1)
julia> p[0], p[1], p[-1], p[10]
(2, 1, 1, 0)
julia> p[valuation(p):degree(p)]
3-element Vector{Int64}:
1
2
1
julia> p[begin:end] # the same as the above line
3-element Vector{Int64}:
1
2
1
julia> coefficients(p) # the same again
3-element Vector{Int64}:
1
2
1A polynomial is a scalar if the valuation and degree are 0. The function scalar returns the constant coefficient if the polynomial is a scalar, and nothing otherwise.
julia> Pol(1)
Pol{Int64}: 1
julia> convert(Pol{Int},1) # the same thing
Pol{Int64}: 1
julia> scalar(Pol(1))
1
julia> convert(Int,Pol(1)) # the same thing
1
julia> Int(Pol(1)) # the same thing
1
julia> scalar(q+1) # nothing; convert would give an errorIn arrays Pol{T} of different types T are promoted to the same type T (when the T involved have a promotion) and a number is promoted to a polynomial.
Usual arithmetic (+, -, *, ^, /, //, one, isone, zero, iszero, ==) works. Elements of type <:Number or of type T for a Pol{T} are considered as scalars for scalar operations on the coefficients.
julia> derivative(p)
Pol{Int64}: 1-q⁻²
julia> p=(q+1)^2
Pol{Int64}: q²+2q+1
julia> p/2
Pol{Float64}: 0.5q²+1.0q+0.5
julia> p//2
Pol{Rational{Int64}}: (1//2)q²+q+1//2
julia> p(1//2) # value of p at 1//2
9//4
julia> p(0.5)
2.25
julia> Pol([1,2,3],[2.0,1.0,3.0]) # find p taking values [2.0,1.0,3.0] at [1,2,3]
Pol{Float64}: 1.5q²-5.5q+6.0Polynomials are scalars for broadcasting. They can be sorted (they have cmp and isless functions which compare the valuation and the coefficients), they can be keys in a Dict (they have a hash function).
The functions divrem, div, %, gcd, gcdx, lcm, powermod operate between true polynomials over a field, using the polynomial division. Over a ring it is better to use pseudodiv and srgcd instead of divrem and gcd (by default gcd between integer polynomials delegates to srgcd).
LinearAlgebra.exactdiv does division (over a field or a ring) when it is exact, otherwise gives an error.
julia> divrem(q^3+1,2q+1) # changes coefficients to field elements
(0.5q²-0.25q+0.125, 0.875)
julia> divrem(q^3+1,2q+1//1) # case of coefficients already field elements
((1//2)q²+(-1//4)q+1//8, 7//8)
julia> pseudodiv(q^3+1,2q+1) # pseudo-division keeps the ring
(4q²-2q+1, 7)
julia> (4q^2-2q+1)*(2q+1)+7 # but multiplying back gives a multiple of the polynomial
Pol{Int64}: 8q³+8
julia> LinearAlgebra.exactdiv(q+1,2.0) # LinearAlgebra.exactdiv(q+1,2) would give an error
Pol{Float64}: 0.5q+0.5Finally, Pols have methods conj, adjoint which operate on coefficients, methods positive_part, negative_part and bar (useful for Kazhdan-Lusztig theory) and a method randpol to produce random polynomials.
Inverting polynomials is a way to get a rational fraction Frac{Pol{T}}, where Frac is a general type for fractions. Rational fractions are normalized so that the numerator and denominator are true polynomials prime to each other. They have the arithmetic operations +, - , *, /, //, ^, inv, one, isone, zero, iszero (which can operate between a Pol or a Number and a Frac{Pol{T}}).
julia> a=1/(q+1)
Frac{Pol{Int64}}: 1/(q+1)
julia> Pol(2/a) # convert back to `Pol`
Pol{Int64}: 2q+2
julia> numerator(a)
Pol{Int64}: 1
julia> denominator(a)
Pol{Int64}: q+1
julia> m=[q+1 q+2;q-2 q-3]
2×2 Matrix{Pol{Int64}}:
q+1 q+2
q-2 q-3
julia> n=inv(Frac.(m)) # convert to rational fractions to invert the matrix
2×2 Matrix{Frac{Pol{Int64}}}:
(-q+3)/(2q-1) (-q-2)/(-2q+1)
(q-2)/(2q-1) (q+1)/(-2q+1)
julia> map(x->x(1),n) # evaluate at 1 the inverse matrix
2×2 Matrix{Float64}:
2.0 3.0
-1.0 -2.0
julia> map(x->x(1;Rational=true),n) # evaluate at 1 using //
2×2 Matrix{Rational{Int64}}:
2 3
-1 -2Rational fractions are also scalars for broadcasting and can be sorted (have cmp and isless methods).
#
LaurentPolynomials.Pol — Type.
Pol(c::AbstractVector,v::Integer=0;check=true,copy=true)
Make a polynomial of valuation v with coefficients c.
Unless check is false normalize the result by making sure that c has no leading or trailing zeroes (do not set check=false unless you are sure this is already the case).
Unless copy=false the contents of c are copied (you can gain one allocation by setting copy=false if you know the contents can be shared)
Pol(t::Symbol)
Sets the name of the variable for printing Pols to t, and returns the polynomial of degree 1 equal to that variable.
Pol(x::AbstractVector,y::AbstractVector)
Interpolation: find a Pol (of nonnegative valuation) of smallest degree taking values y at points x. The values y should be in a field for the function to be type stable.
julia> p=Pol([1,1,1])
Pol{Int64}: q²+q+1
julia> vals=p.(1:5)
5-element Vector{Int64}:
3
7
13
21
31
julia> Pol(1:5,vals*1//1)
Pol{Rational{Int64}}: q²+q+1
julia> Pol(1:5,vals*1.0)
Pol{Float64}: 1.0q²+1.0q+1.0#
LaurentPolynomials.@Pol — Macro.
@Pol q
is equivalent to q=Pol(:q) excepted it creates q in the global scope of the current module, since it uses eval.
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Base.divrem — Function.
divrem(a::Pol, b::Pol)
a and b should be true polynomials (nonnegative valuation). Computes (q,r) such that a=q*b+r and degree(r)<degree(b). Type stable if the coefficients of b are in a field.
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Base.gcd — Method.
gcd(p::Pol, q::Pol) computes the gcd of the polynomials. It uses the subresultant algorithms for the gcd of integer polynomials.
julia> gcd(2q+2,2q^2-2)
Pol{Int64}: 2q+2
julia> gcd((2q+2)//1,(2q^2-2)//1)
Pol{Rational{Int64}}: q+1#
Base.gcdx — Method.
gcdx(a::Pol,b::Pol)
for polynomials over a field returns d,u,v such that d=ua+vb and d=gcd(a,b).
julia> gcdx(q^3-1//1,q^2-1//1)
(q-1, 1, -q)#
LaurentPolynomials.pseudodiv — Function.
pseudodiv(a::Pol, b::Pol)
pseudo-division of a by b. If d is the leading coefficient of b, computes (q,r) such that d^(degree(a)+1-degree(b))a=q*b+r and degree(r)<degree(b). Does not do division so works over any ring. For true polynomials (errors if the valuation of a or of b is negative).
julia> pseudodiv(q^2+1,2q+1)
(2q-1, 5)
julia> (2q+1)*(2q-1)+5
Pol{Int64}: 4q²+4See Knuth AOCP2 4.6.1 Algorithm R
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LaurentPolynomials.srgcd — Function.
srgcd(a::Pol,b::Pol)
sub-resultant gcd: gcd of polynomials over a unique factorization domain
julia> srgcd(4q+4,6q^2-6)
Pol{Int64}: 2q+2See Knuth AOCP2 4.6.1 Algorithm C
#
Base.powermod — Function.
powermod(p::Pol, x::Integer, q::Pol) computes
julia> powermod(q-1//1,3,q^2+q+1)
Pol{Rational{Int64}}: 6q+3#
LaurentPolynomials.randpol — Function.
randpol(T,d)
random polynomial of degree d with coefficients from T
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LaurentPolynomials.Frac — Type.
`Frac(a::Pol,b::Pol;prime=false)
Polynomials a and b are promoted to same coefficient type, and checked for being true polynomials (otherwise they are both multiplied by the same power of the variable so they become true polynomials), and unless prime=true they are checked for having a non-trivial gcd.
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LaurentPolynomials.negative_part — Function.
negative_part(p::Pol) keep the terms of degree≤0
#
LaurentPolynomials.positive_part — Function.
positive_part(p::Pol) keep the terms of degree≥0
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LaurentPolynomials.bar — Function.
bar(p::Pol) transform p(q) into p(q⁻¹)
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LaurentPolynomials.shift — Function.
shift(p::Pol,s) efficient way to multiply a polynomial by Pol()^s.
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LaurentPolynomials.resultant — Function.
resultant(p::Pol,q::Pol)
The function computes the resultant of the two polynomials, as the determinant of the Sylvester matrix. ```
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LaurentPolynomials.discriminant — Function.
discriminant(p::Pol) the resultant of the polynomial with its derivative. This detects multiple zeroes.