ApproxFun+IntervalArithmetic: make ODE solving really rigorous

[dlfivefifty]

The following code returns successfully for solving Airy's equation (the extra definitions are work arounds for bugs):

julia> Base.broadcast(::typeof(*), a::IntervalArithmetic.Interval, b::AbstractVector) = 
           map(x -> a*x, b)

julia> Base.Broadcast.broadcasted(::typeof(*), a::IntervalArithmetic.Interval, b::AbstractVector) = 
           map(x -> a*x, b)

julia> Base.length(d::UnitRange{<:IntervalArithmetic.Interval}) = Integer(maximum(d.stop.hi)-minimum(d.start.lo)+1)

julia> u = [Dirichlet(); D^2 - x] \ [[1, 0], 0]
Fun(Chebyshev([-1, -1]..[1, 1]),IntervalArithmetic.Interval{Float64}[[0.528647, 0.528648], [-0.522247, -0.522246], [-0.023162, -0.0231619], [0.0223854, 0.0223855], [-0.00557882, -0.00557881], [-0.000122104, -0.000122103], [9.37066e-05, 9.37067e-05], [-1.68122e-05, -1.68121e-05], [-2.4373e-07, -2.43729e-07], [1.63064e-07, 1.63065e-07]  …  [1.54639e-10, 1.5464e-10], [-1.89644e-11, -1.89643e-11], [-1.63813e-13, -1.63812e-13], [9.21385e-14, 9.21386e-14], [-9.91957e-15, -9.91956e-15], [-7.12466e-17, -7.12465e-17], [3.76658e-17, 3.76659e-17], [-3.63796e-18, -3.63795e-18], [-2.23433e-20, -2.23432e-20], [-1.12162e-20, -1.12161e-20]])

It happens to have worked:

julia> parse(BigFloat,"0.546136459064141770613483785351029091974067802242010192661484687977676021081218637004123196030567166414008915304513669838826570884623515686") in u(0) # true value from mathematica
true

However, this is mostly luck: the convergence criteria doesn't actually control the decay in the tail. To do this properly (and thereby have apriori guaranteed success) we would need to modify the convergence check at

ApproxFunBase.jl/src/Caching/matrix.jl

Line 122 in 7dd9ef0

while (k ≤ min(m+M,A_dim) || BLAS.nrm2(M,yp,1) > tolerance )

to properly bound the tail.