Closed MarcoFasondini closed 3 years ago
Although this can be done with ClassicalOrthogonalPolynomials.jl, I tried
p1 = SemiclassicalJacobi(0, 1, 0, 0) p3 = SemiclassicalJacobi(0, 3, 0, 0) p3\p1 ((ℵ₀×ℵ₀ Diagonal{Float64, Fill{Float64, 1, Tuple{InfiniteArrays.OneToInf{Int64}}}} with indices OneToInf()×OneToInf()) * ((ℵ₀×ℵ₀ LazyBandedMatrices.Bidiagonal{Float64, LazyArrays.BroadcastVector{Float64, typeof(*), Tuple{Float64, LazyArrays.Accumulate{Float64, 1, typeof(*), Vector{Float64}, AbstractVector{Float64}}}}, LazyArrays.BroadcastVector{Float64, typeof(-), Tuple{LazyArrays.BroadcastVector{Float64, typeof(*), Tuple{LazyArrays.BroadcastVector{Float64, typeof(*), Tuple{Float64, LazyArrays.Accumulate{Float64, 1, typeof(*), Vector{Float64}, AbstractVector{Float64}}}}, OrthogonalPolynomialRatio{Float64, SemiclassicalJacobi{Float64}}}}}}} with indices OneToInf()×OneToInf()) * (ℵ₀×ℵ₀ LazyBandedMatrices.Bidiagonal{Float64, LazyArrays.BroadcastVector{Float64, typeof(*), Tuple{Float64, LazyArrays.Accumulate{Float64, 1, typeof(*), Vector{Float64}, AbstractVector{Float64}}}}, LazyArrays.BroadcastVector{Float64, typeof(-), Tuple{LazyArrays.BroadcastVector{Float64, typeof(*), Tuple{LazyArrays.BroadcastVector{Float64, typeof(*), Tuple{Float64, LazyArrays.Accumulate{Float64, 1, typeof(*), Vector{Float64}, AbstractVector{Float64}}}}, OrthogonalPolynomialRatio{Float64, SemiclassicalJacobi{Float64}}}}}}} with indices OneToInf()×OneToInf()) with indices OneToInf()×OneToInf()) with indices OneToInf()×OneToInf()) * (ℵ₀×ℵ₀ Diagonal{Float64, Fill{Float64, 1, Tuple{InfiniteArrays.OneToInf{Int64}}}} with indices OneToInf()×OneToInf()) with indices OneToInf()×OneToInf(): NaN NaN NaN ⋅ ⋅ … ⋅ NaN NaN NaN ⋅ ⋅ ⋅ NaN NaN NaN ⋅ ⋅ ⋅ NaN NaN ⋅ ⋅ ⋅ ⋅ NaN ⋅ ⋅ ⋅ ⋅ ⋅ … ⋅ ⋅ ⋅ ⋅ ⋅ ⋅ ⋅ ⋅ ⋅ ⋅ ⋅ ⋅ ⋅ ⋅ ⋅ ⋅ ⋅ ⋅ ⋅ ⋅ ⋅ ⋅ ⋅ ⋅ ⋅ … ⋅ ⋅ ⋅ ⋅ ⋅ ⋅ ⋅ ⋅ ⋅ ⋅ ⋮ ⋱
I think it requires t > 1
t > 1
Yes, this requires t > 1 though I think it should probably tell the user this instead of returning NaN operators. Should be a simple check somewhere, I'll have a look.
Although this can be done with ClassicalOrthogonalPolynomials.jl, I tried