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fix typos
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docs/src/index.md

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@@ -841,7 +841,7 @@ savefig("rational_function.svg"); nothing # hide
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* [MultiPoly.jl](https://github.com/daviddelaat/MultiPoly.jl) for sparse multivariate polynomials
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* [DynamicPolynomals.jl](https://github.com/JuliaAlgebra/DynamicPolynomials.jl) Multivariate polynomials implementation of commutative and non-commutative variables
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* [DynamicPolynomials.jl](https://github.com/JuliaAlgebra/DynamicPolynomials.jl) Multivariate polynomials implementation of commutative and non-commutative variables
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* [MultivariatePolynomials.jl](https://github.com/JuliaAlgebra/MultivariatePolynomials.jl) for multivariate polynomials and moments of commutative or non-commutative variables
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src/abstract-polynomial.jl

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@@ -57,7 +57,7 @@ basis_symbol(::Type{AbstractUnivariatePolynomial{B,T,X}}) where {B,T,X} = "Χ($(
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## idea is vector space stuff (scalar_add, scalar_mult, vector +/-, ^) goes here
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## connection (convert, transform) is specific to a basis (storage)
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## ⊗(p::P{T,X}, q::P{S,Y}) is specic to basis/storage
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## ⊗(p::P{T,X}, q::P{S,Y}) is specific to basis/storage
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# type of basis
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basistype(p::AbstractUnivariatePolynomial{B,T,X}) where {B,T,X} = B

src/common.jl

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@@ -1005,7 +1005,7 @@ basis(p::P, k::Int, _var=indeterminate(p); var=_var) where {P<:AbstractPolynomia
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#=
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composition
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cf. https://github.com/JuliaMath/Polynomials.jl/issues/511 for a paper with implentations
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cf. https://github.com/JuliaMath/Polynomials.jl/issues/511 for a paper with implementations
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=#
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"""
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Base.:+(p::P, c::S) where {T,X, P<:AbstractPolynomial{T,X}, S} = scalar_add(c,p)
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## polynomial + polynomial when different types
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## - each polynomial container type implents PB{B,T,X} + PB{B,S,X}
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## - each polynomial container type implements PB{B,T,X} + PB{B,S,X}
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## - this handles case X ≠ Y unless constant
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## - when PB₁ ≠ PB₂ we promote both polynomials
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function Base.:+(p::P, q::Q) where {T,X,P <: AbstractPolynomial{T,X}, S,Y,Q <: AbstractPolynomial{S,Y}}
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# Th ⊕ methd below is used in Special Polynomials, but not here, as it was removed for
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# Th ⊕ method below is used in Special Polynomials, but not here, as it was removed for
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# similar methods in the polynomial-basetypes
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# addition of polynomials is just vector space addition, so can be done regardless
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# of basis, as long as the same. These ⊕ methods try to find a performant means to add

src/polynomial-container-types/immutable-dense-polynomial.jl

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@@ -23,7 +23,7 @@ ImmutableDensePolynomial{B,T,X,N}(check::Type{Val{false}}, cs::NTuple{N,T}) wher
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ImmutableDensePolynomial{B,T,X,N}(check::Type{Val{true}}, cs::NTuple{N,T}) where {B,N, T,X} =
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ImmutableDensePolynomial{B,T,X,N}(cs)
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# tuple with mis-matched size
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# tuple with mismatched size
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function ImmutableDensePolynomial{B,T,X,N}(xs::NTuple{M,S}) where {B,T,S,X,N,M}
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p = ImmutableDensePolynomial{B,S,X,M}(xs)
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convert(ImmutableDensePolynomial{B,T,X,N}, ImmutableDensePolynomial{B,T,X,M}(xs))

src/polynomial-container-types/mutable-dense-laurent-polynomial.jl

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This polynomial type essentially uses an offset vector (`Vector{T}`,`order`) to store the coefficients of a polynomial relative to the basis `B` with indeterminate `X`.
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The typical offset is to have `0` as the order, but, say, to accomodate Laurent polynomials, or more efficient storage of basis elements any order may be specified.
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The typical offset is to have `0` as the order, but, say, to accommodate Laurent polynomials, or more efficient storage of basis elements any order may be specified.
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This type trims trailing zeros and the leading zeros on construction.
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src/polynomials/standard-basis/immutable-polynomial.jl

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# julia> @time Base.power_by_squaring(p,15);
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# 0.000145 seconds (7 allocations: 6.547 KiB)
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# This is not inferrable, as `n` is not a compile time constant
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# This is not inferable, as `n` is not a compile time constant
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Base.:^(p::ImmutablePolynomial, n::Integer) = immutable_power(p, n)
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function immutable_power(p::ImmutablePolynomial{T,X,N}, n::Integer) where {T,X,N}
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iszero(p) && return p

src/polynomials/standard-basis/standard-basis.jl

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@@ -312,13 +312,13 @@ function _gcd_noda_sasaki(a::Vector{T}, b::Vector{S};
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tol = atol + rtol
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# determine the degree of GCD as the nullity of the Sylvester matrix
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# this computation can be replaced by simply setting nd = 1, in which case the Sylvester matrix is not formed
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# this computation can be replaced by simply setting nᵣ = 1, in which case the Sylvester matrix is not formed
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nd = na1 + nb1 - 2 - rank([NGCD.convmtx(a1,nb1-1) NGCD.convmtx(b1,na1-1)], atol = tol) # julia 1.1
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nd == 0 && (return [one(R)])
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nᵣ = na1 + nb1 - 2 - rank([NGCD.convmtx(a1,nb1-1) NGCD.convmtx(b1,na1-1)], atol = tol) # julia 1.1
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nᵣ == 0 && (return [one(R)])
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sc = one(R)
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while na1 > nd
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while na1 > nᵣ
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na1 < nb1 && ((a1, b1, na1, nb1) = (b1, a1, nb1, na1))
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@inbounds for i in na1:-1:nb1
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s = -a1[i] / b1[nb1]

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