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test/dummy_solver.jl
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| function DummySolver(nvar :: Integer, ncon :: Integer = 0) |
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Maybe this should take an NLPModelMeta as input?!
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That would be too specific. This is the simplest one to allow creating a reusable solver with various problems of the same size, for instance. If you already have the meta, you probably have the nlp.
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I know but what I’m saying is that all the information in the meta may be necessary to construct a solver, e.g., for constrained problems, taking into account the number of equality/inequality constraints, types of bounds, etc.
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I've changed now to use meta. Now both
DummySolver(nlp.meta) and DummySolver(Val(:nosolve), nlp) create the solver without solving.
test/dummy_solver.jl
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| parameters(::DummySolver) = NamedTuple(α = 1e-2, δ = 1e-8) |
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Ideally, the parameters structure also encapsulates the domain and type of each parameter.
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I was thinking about that. The issue that remains is the interaction between parameters. Like 0 <= eta_1 <= eta_2 <= 1. So I was thinking of returning the problem, but that constricts the non-scalar parameters.
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I've updated with a lot more information on the parameters. It could possibly be made clearer with structs but I haven't found a need to change yet. The interaction issue above was not solved. I also implemented a grid search.
test/dummy_solver.jl
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| ) No newline at end of file |
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This function should return a real number, right?
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Yes, it's unfinished because I don't know what to do yet.
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Codecov Report
@@ Coverage Diff @@
## master #6 +/- ##
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+ Coverage 91.20% 94.44% +3.23%
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Files 3 6 +3
Lines 91 72 -19
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- Hits 83 68 -15
+ Misses 8 4 -4
Continue to review full report at Codecov.
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