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Our NewtonRaphson implementation isn't robust against functions that look like this
NewtonRaphson
julia> nl(t, p) = 0.010000000000000002 + 10.000000000000002 / (1 + (0.21640425613334457 + 216.40425613334457 / (1 + (0.21640425613334457 + 216.40425613334457 / (1 + 0.0006250000000000001(t^2.0)))^2.0))^2.0) - 0.0011552453009332421t nl (generic function with 2 methods) julia> solve(NonlinearProblem(nl, 10.0), NewtonRaphson()).retcode :MAXITERS_EXCEED
The text was updated successfully, but these errors were encountered:
And note NLSolveJL does succeed here.
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I've implemented trustRegion.jl in SimpleNonlinearSolve.jl and I'll now give this a go. (I've got exams coming, but I'll try to fix it asap)
trustRegion.jl
SimpleNonlinearSolve.jl
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Our
NewtonRaphson
implementation isn't robust against functions that look like thisThe text was updated successfully, but these errors were encountered: