https://github.com/juliaapproximation/orthogonalpolynomialsquasi.jl
A package for representing orthogonal polynomials as quasi arrays
https://github.com/juliaapproximation/orthogonalpolynomialsquasi.jl
Last synced: 10 months ago
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A package for representing orthogonal polynomials as quasi arrays
- Host: GitHub
- URL: https://github.com/juliaapproximation/orthogonalpolynomialsquasi.jl
- Owner: JuliaApproximation
- License: mit
- Created: 2019-09-17T06:27:13.000Z (almost 7 years ago)
- Default Branch: master
- Last Pushed: 2021-03-24T16:17:41.000Z (over 5 years ago)
- Last Synced: 2025-09-12T21:52:02.442Z (10 months ago)
- Language: Julia
- Size: 181 KB
- Stars: 7
- Watchers: 7
- Forks: 2
- Open Issues: 19
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# OrthogonalPolynomialsQuasi.jl
A package for representing orthogonal polynomials as quasi arrays
[](https://travis-ci.org/JuliaApproximation/OrthogonalPolynomialsQuasi.jl)
[](https://codecov.io/gh/JuliaApproximation/OrthogonalPolynomialsQuasi.jl)
**This package has been superseded by [ClassicalOrthogonalPolynomials.jl](https://github.com/JuliaApproximation/ClassicalOrthogonalPolynomials.jl)**
This package implements classical orthogonal polynomials as quasi-arrays where one one axes is continuous and the other axis is discrete (countably infinite), as implemented in [QuasiArrays.jl](https://github.com/JuliaApproximation/QuasiArrays.jl) and [ContinuumArrays.jl](https://github.com/JuliaApproximation/ContinuumArrays.jl).
```julia
julia> using OrthogonalPolynomialsQuasi, ContinuumArrays
julia> P = Legendre(); # Legendre polynomials
julia> size(P) # uncountable ∞ x countable ∞
(ℵ₁, ∞)
julia> axes(P) # essentially (-1..1, 1:∞), Inclusion plays the same role as Slice
(Inclusion(-1.0..1.0 (Chebyshev)), OneToInf())
julia> P[0.1,1:10] # [P_0(0.1), …, P_9(0.1)]
10-element Array{Float64,1}:
1.0
0.1
-0.485
-0.14750000000000002
0.3379375
0.17882875
-0.2488293125
-0.19949294375000004
0.180320721484375
0.21138764183593753
julia> @time P[range(-1,1; length=10_000), 1:10_000]; # construct 10_000^2 Vandermonde matrix
1.624796 seconds (10.02 k allocations: 1.491 GiB, 6.81% gc time)
```
This also works for associated Legendre polynomials as weighted Ultraspherical polynomials:
```julia
julia> associatedlegendre(m) = ((-1)^m*prod(1:2:(2m-1)))*(UltrasphericalWeight((m+1)/2).*Ultraspherical(m+1/2))
associatedlegendre (generic function with 1 method)
julia> associatedlegendre(2)[0.1,1:10]
10-element Array{Float64,1}:
2.9699999999999998
1.4849999999999999
-6.9052500000000006
-5.041575
10.697754375
10.8479361375
-13.334647528125
-18.735466024687497
13.885467170308594
28.220563705988674
```
## p-Finite Element Method
The language of quasi-arrays gives a natural framework for constructing p-finite element methods. The convention
is that adjoint-products are understood as inner products over the axes with uniform weight. Thus to solve Poisson's equation
using its weak formulation with Dirichlet conditions we can expand in a weighted Jacobi basis:
```julia
julia> P¹¹ = Jacobi(1.0,1.0); # Quasi-matrix of Jacobi polynomials
julia> w = JacobiWeight(1.0,1.0); # quasi-vector correspoinding to (1-x^2)
julia> w[0.1] ≈ (1-0.1^2)
true
julia> S = w .* P¹¹; # Quasi-matrix of weighted Jacobi polynomials
julia> D = Derivative(axes(S,1)); # quasi-matrix corresponding to derivative
julia> Δ = (D*S)'*(D*S) # weak laplacian corresponding to inner products of weighted Jacobi polynomials
∞×∞ LazyArrays.ApplyArray{Float64,2,typeof(*),Tuple{Adjoint{Int64,BandedMatrices.BandedMatrix{Int64,Adjoint{Int64,InfiniteArrays.InfStepRange{Int64,Int64}},InfiniteArrays.OneToInf{Int64}}},LazyArrays.BroadcastArray{Float64,2,typeof(*),Tuple{LazyArrays.BroadcastArray{Float64,1,typeof(/),Tuple{Int64,InfiniteArrays.InfStepRange{Int64,Int64}}},BandedMatrices.BandedMatrix{Int64,Adjoint{Int64,InfiniteArrays.InfStepRange{Int64,Int64}},InfiniteArrays.OneToInf{Int64}}}}}} with indices OneToInf()×OneToInf():
2.66667 ⋅ ⋅ ⋅ ⋅ ⋅ ⋅ ⋅ …
⋅ 6.4 ⋅ ⋅ ⋅ ⋅ ⋅ ⋅
⋅ ⋅ 10.2857 ⋅ ⋅ ⋅ ⋅ ⋅
⋅ ⋅ ⋅ 14.2222 ⋅ ⋅ ⋅ ⋅
⋅ ⋅ ⋅ ⋅ 18.1818 ⋅ ⋅ ⋅
⋅ ⋅ ⋅ ⋅ ⋅ 22.1538 ⋅ ⋅ …
⋅ ⋅ ⋅ ⋅ ⋅ ⋅ 26.1333 ⋅
⋅ ⋅ ⋅ ⋅ ⋅ ⋅ ⋅ 30.1176
⋅ ⋅ ⋅ ⋅ ⋅ ⋅ ⋅ ⋅
⋅ ⋅ ⋅ ⋅ ⋅ ⋅ ⋅ ⋅
⋅ ⋅ ⋅ ⋅ ⋅ ⋅ ⋅ ⋅ …
⋅ ⋅ ⋅ ⋅ ⋅ ⋅ ⋅ ⋅
⋮ ⋮ ⋱
```