Update ExaModels and MadNLP#31
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## main #31 +/- ##
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- Coverage 85.09% 49.92% -35.17%
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Files 6 5 -1
Lines 711 701 -10
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- Hits 605 350 -255
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This appears to make very little difference for performance and allocations/memory. Using the quadrotor example from our benchmarks with 1000 time points, I get the following with the current release: using NLPModelsIpopt, InfiniteOpt, InfiniteExaModels
m = quad(num_supports = 1000, backend = ExaTranscriptionBackend(IpoptSolver))
set_silent(m)
@time build_transcription_backend!(m) # builds the ExaModel
@time optimize!(m)0.168895 seconds (450.06 k allocations: 24.030 MiB)
0.865922 seconds (310 allocations: 2.626 MiB)With this PR, I get: 0.174419 seconds (449.24 k allocations: 24.468 MiB)
0.856932 seconds (310 allocations: 2.629 MiB) |
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This is broken because of exanauts/ExaModels.jl#269. Previously, we could add constraints without a generator by directly giving an ExaModels expression and an iterator, but that method was removed. |
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Requires exanauts/ExaModels.jl#286. |
This tests out the ExaModels changes proposed in exanauts/ExaModels.jl#256.
This now updates the versioning of ExaModels and MadNLP with the new versions.