JuliaSmoothOptimizers
How to handle NaNs?
Example:
julia> model = CUTEstModel("BENNETT5LS")
Minimization problem BENNETT5LS
nvar = 3, ncon = 0 (0 linear)
julia> x = model.meta.x0
3-element Array{Float64,1}:
-2000.0
50.0
0.8
julia> g = grad(model, x)
3-element Array{Float64,1}:
37.195399822788076
1518.022256935859
-478331.78119128384
julia> x1 = x - g
3-element Array{Float64,1}:
-2037.195399822788
-1468.022256935859
478332.5811912838
julia> obj(model, x1)
NaN
Solvers should be able to trap NaNs and return an appropriate status.
Do you mean at the individual solver level or bmark level? Is this to test that a solver doesn't return :first_order when it's actually returning a NaN value?
:exception sounds like a good value for the situation.
At the solver level, there should be a mechanism to trap NaNs. Benchmarks should recognize that a solver generated a NaN, and there should be a good status for it. I think there could be a specific status for NaN, or we could use :exception and have a specific solver status in addition.
Le jeudi 12 décembre 2019 à 19:12 -0800, Dominique a écrit :
At the solver level, there should be a mechanism to trap NaNs. Benchmarks should recognize that a solver generated a NaN, and there should be a good status for it. I think there could be a specific status for NaN, or we could use :exception and have a specific solver status in addition. In our Stopping framework in development, I believe it is Stopping that should take care of such issues. The solver should not address NaNs and other Inf exceptions, Stopping should uniformly address any such circumstance.
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