# Half-range Chebyshev polynomials

In this paper, Daan Huybrechs introduced the so-called half-range Chebyshev polynomials as the semi-classical orthogonal polynomials with respect to the inner product:

$\langle f, g \rangle = \int_0^1 f(x) g(x)\frac{{\rm d} x}{\sqrt{1-x^2}}.$

By the variable transformation $y = 2x-1$, the resulting polynomials can be related to orthogonal polynomials on $(-1,1)$ with the Jacobi weight $(1-y)^{-\frac{1}{2}}$ modified by the weight $(3+y)^{-\frac{1}{2}}$.

We shall use the fact that:

$\frac{1}{\sqrt{3+y}} = \sqrt{\frac{2}{3+\sqrt{8}}}\sum_{n=0}^\infty P_n(y) \left(\frac{-1}{3+\sqrt{8}}\right)^n,$

and results from this paper to consider the half-range Chebyshev polynomials as modifications of the Jacobi polynomials $P_n^{(-\frac{1}{2},0)}(y)$.

using FastTransforms, LinearAlgebra, Plots, LaTeXStrings
const GENFIGS = joinpath(pkgdir(FastTransforms), "docs/src/generated")
!isdir(GENFIGS) && mkdir(GENFIGS)
plotlyjs()
Plots.PlotlyJSBackend()

We truncate the generating function to ensure a relative error less than eps() in the uniform norm on $(-1,1)$:

z = -1/(3+sqrt(8))
K = sqrt(-2z)
N = ceil(Int, log(abs(z), eps()/2*(1-abs(z))/K) - 1)
d = K .* z .^(0:N)
21-element Vector{Float64}:
0.585786437626905
-0.10050506338833466
0.017243942703102998
-0.0029585928302833363
0.0005076142785970194
-8.70928412987791e-5
1.4942769195655289e-5
-2.563773875152638e-6
4.3987405526054037e-7
-7.547045641060418e-8
1.2948683203084688e-8
-2.221642807903953e-9
3.8117364433902886e-10
-6.53990581302203e-11
1.122070444229295e-11
-1.925168523537399e-12
3.303066989314436e-13
-5.6671670051262317e-14
9.723321376130305e-15
-1.6682582055195078e-15
2.862278569867433e-16

Then, we convert this representation to the expansion in Jacobi polynomials $P_n^{(-\frac{1}{2}, 0)}(y)$:

u = jac2jac(d, 0.0, 0.0, -0.5, 0.0; norm1 = false, norm2 = true)
21-element Vector{Float64}:
0.9340010840223151
-0.09412895357801879
0.012332003442981326
-0.0017749924171044493
0.00026739546832682813
-4.137724994351243e-5
6.516867953544994e-6
-1.0393137552650633e-6
1.673019401749671e-7
-2.7126077156467505e-8
4.423525597533419e-9
-7.247452797834485e-10
1.1920483520702293e-10
-1.9671182646957104e-11
3.2552876283504113e-12
-5.400110170678753e-13
8.977555063800248e-14
-1.4948797757615094e-14
2.4980123321513196e-15
-4.125155260660654e-16
7.540496818753807e-17

Our working polynomial degree will be:

n = 100
100

We compute the connection coefficients between the modified orthogonal polynomials and the Jacobi polynomials:

P = plan_modifiedjac2jac(Float64, n+1, -0.5, 0.0, u)
FastTransforms Modified Jacobi--Jacobi plan for 101-element array of Float64

We store the connection to first kind Chebyshev polynomials:

P1 = plan_jac2cheb(Float64, n+1, -0.5, 0.0; normjac = true)
FastTransforms Jacobi--Chebyshev plan for 101-element array of Float64

We compute the Chebyshev series for the degree-$k\le n$ modified polynomial and its values at the Chebyshev points:

q = k -> lmul!(P1, lmul!(P, [zeros(k); 1.0; zeros(n-k)]))
qvals = k-> ichebyshevtransform(q(k))
#3 (generic function with 1 method)

With the symmetric Jacobi matrix for $P_n^{(-\frac{1}{2}, 0)}(y)$ and the modified plan, we may compute the modified Jacobi matrix and the corresponding roots (as eigenvalues):

XP = SymTridiagonal([-inv((4n-1)*(4n-5)) for n in 1:n+1], [4n*(2n-1)/(4n-1)/sqrt((4n-3)*(4n+1)) for n in 1:n])
XQ = FastTransforms.modified_jacobi_matrix(P, XP)
SymTridiagonal(XQ.dv[1:10], XQ.ev[1:9])
10×10 LinearAlgebra.SymTridiagonal{Float64, Vector{Float64}}:
0.27324    0.615517     ⋅           ⋅            ⋅            ⋅            ⋅            ⋅            ⋅             ⋅
0.615517  -0.0327708   0.509195     ⋅            ⋅            ⋅            ⋅            ⋅            ⋅             ⋅
⋅         0.509195   -0.0115134   0.50391       ⋅            ⋅            ⋅            ⋅            ⋅             ⋅
⋅          ⋅          0.50391    -0.00564494   0.502131      ⋅            ⋅            ⋅            ⋅             ⋅
⋅          ⋅           ⋅          0.502131    -0.00333235   0.501338      ⋅            ⋅            ⋅             ⋅
⋅          ⋅           ⋅           ⋅           0.501338    -0.00219677   0.500918      ⋅            ⋅             ⋅
⋅          ⋅           ⋅           ⋅            ⋅           0.500918    -0.00155663   0.500668      ⋅             ⋅
⋅          ⋅           ⋅           ⋅            ⋅            ⋅           0.500668    -0.00116057   0.500509       ⋅
⋅          ⋅           ⋅           ⋅            ⋅            ⋅            ⋅           0.500509    -0.000898551   0.5004
⋅          ⋅           ⋅           ⋅            ⋅            ⋅            ⋅            ⋅           0.5004       -0.000716244

And we plot:

x = (chebyshevpoints(Float64, n+1, Val(1)) .+ 1 ) ./ 2
p = plot(x, qvals(0); linewidth=2.0, legend = false, xlim=(0,1), xlabel=L"x",
ylabel=L"T^h_n(x)", title="Half-Range Chebyshev Polynomials and Their Roots",
extra_plot_kwargs = KW(:include_mathjax => "cdn"))
for k in 1:10
λ = (eigvals(SymTridiagonal(XQ.dv[1:k], XQ.ev[1:k-1])) .+ 1) ./ 2
plot!(x, qvals(k); linewidth=2.0, color=palette(:default)[k+1])
scatter!(λ, zero(λ); markersize=2.5, color=palette(:default)[k+1])
end
p
savefig(joinpath(GENFIGS, "halfrange.html"))
"/juliateam/.julia/packages/FastTransforms/0VwwO/docs/src/generated/halfrange.html"

By Theorem 2.20 it turns out that the derivatives of the half-range Chebyshev polynomials are a linear combination of at most two polynomials orthogonal with respect to $\sqrt{(3+y)(1-y)}(1+y)$ on $(-1,1)$. This fact enables us to compute the banded differentiation matrix:

v̂ = 3*[u; 0]+XP[1:N+2, 1:N+1]*u
v = jac2jac(v̂, -0.5, 0.0, 0.5, 1.0; norm1 = true, norm2 = true)
function threshold!(A::AbstractArray, ϵ)
for i in eachindex(A)
if abs(A[i]) < ϵ A[i] = 0 end
end
A
end
P′ = plan_modifiedjac2jac(Float64, n+1, 0.5, 1.0, v)
DP = UpperTriangular(diagm(1=>[sqrt(n*(n+1/2)) for n in 1:n])) # The classical differentiation matrix representing 𝒟 P^{(-1/2,0)}(y) = P^{(1/2,1)}(y) D_P.
DQ = UpperTriangular(threshold!(P′\(DP*(P*I)), 100eps())) # The semi-classical differentiation matrix representing 𝒟 Q(y) = Q̂(y) D_Q.
UpperTriangular(DQ[1:10,1:10])
10×10 LinearAlgebra.UpperTriangular{Float64, Matrix{Float64}}:
0.0  2.11682  0.481094  0.0       0.0      0.0       0.0       0.0       0.0       0.0
⋅   0.0      3.82901   0.789619  0.0      0.0       0.0       0.0       0.0       0.0
⋅    ⋅       0.0       5.53918   1.08781  0.0       0.0       0.0       0.0       0.0
⋅    ⋅        ⋅        0.0       7.24821  1.38334   0.0       0.0       0.0       0.0
⋅    ⋅        ⋅         ⋅        0.0      8.95658   1.67782   0.0       0.0       0.0
⋅    ⋅        ⋅         ⋅         ⋅       0.0      10.6646    1.97177   0.0       0.0
⋅    ⋅        ⋅         ⋅         ⋅        ⋅        0.0      12.3723    2.26542   0.0
⋅    ⋅        ⋅         ⋅         ⋅        ⋅         ⋅        0.0      14.0799    2.55888
⋅    ⋅        ⋅         ⋅         ⋅        ⋅         ⋅         ⋅        0.0      15.7874
⋅    ⋅        ⋅         ⋅         ⋅        ⋅         ⋅         ⋅         ⋅        0.0