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JuliaSmoothOptimizers / NLSProblems.jl

Nonlinear Least Squares problems
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Use @expression in NLS models #75

Closed amontoison closed 2 years ago

codecov[bot] commented 2 years ago

Codecov Report

Base: 94.66% // Head: 94.66% // No change to project coverage :thumbsup:

Coverage data is based on head (c15d320) compared to base (90bad4d). Patch coverage: 100.00% of modified lines in pull request are covered.

Additional details and impacted files ```diff @@ Coverage Diff @@ ## main #75 +/- ## ======================================= Coverage 94.66% 94.66% ======================================= Files 151 151 Lines 1481 1481 ======================================= Hits 1402 1402 Misses 79 79 ``` | [Impacted Files](https://codecov.io/gh/JuliaSmoothOptimizers/NLSProblems.jl/pull/75?src=pr&el=tree&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=JuliaSmoothOptimizers) | Coverage Δ | | |---|---|---| | [src/BNST2.jl](https://codecov.io/gh/JuliaSmoothOptimizers/NLSProblems.jl/pull/75/diff?src=pr&el=tree&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=JuliaSmoothOptimizers#diff-c3JjL0JOU1QyLmps) | `77.77% <100.00%> (ø)` | | | [src/BNST3.jl](https://codecov.io/gh/JuliaSmoothOptimizers/NLSProblems.jl/pull/75/diff?src=pr&el=tree&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=JuliaSmoothOptimizers#diff-c3JjL0JOU1QzLmps) | `69.23% <100.00%> (ø)` | | | [src/LVcon501.jl](https://codecov.io/gh/JuliaSmoothOptimizers/NLSProblems.jl/pull/75/diff?src=pr&el=tree&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=JuliaSmoothOptimizers#diff-c3JjL0xWY29uNTAxLmps) | `80.00% <100.00%> (ø)` | | | [src/LVcon502.jl](https://codecov.io/gh/JuliaSmoothOptimizers/NLSProblems.jl/pull/75/diff?src=pr&el=tree&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=JuliaSmoothOptimizers#diff-c3JjL0xWY29uNTAyLmps) | `78.94% <100.00%> (ø)` | | | [src/LVcon503.jl](https://codecov.io/gh/JuliaSmoothOptimizers/NLSProblems.jl/pull/75/diff?src=pr&el=tree&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=JuliaSmoothOptimizers#diff-c3JjL0xWY29uNTAzLmps) | `77.77% <100.00%> (ø)` | | | [src/LVcon504.jl](https://codecov.io/gh/JuliaSmoothOptimizers/NLSProblems.jl/pull/75/diff?src=pr&el=tree&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=JuliaSmoothOptimizers#diff-c3JjL0xWY29uNTA0Lmps) | `76.47% <100.00%> (ø)` | | | [src/LVcon511.jl](https://codecov.io/gh/JuliaSmoothOptimizers/NLSProblems.jl/pull/75/diff?src=pr&el=tree&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=JuliaSmoothOptimizers#diff-c3JjL0xWY29uNTExLmps) | `81.81% <100.00%> (ø)` | | | [src/LVcon512.jl](https://codecov.io/gh/JuliaSmoothOptimizers/NLSProblems.jl/pull/75/diff?src=pr&el=tree&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=JuliaSmoothOptimizers#diff-c3JjL0xWY29uNTEyLmps) | `83.33% <100.00%> (ø)` | | | [src/LVcon513.jl](https://codecov.io/gh/JuliaSmoothOptimizers/NLSProblems.jl/pull/75/diff?src=pr&el=tree&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=JuliaSmoothOptimizers#diff-c3JjL0xWY29uNTEzLmps) | `80.95% <100.00%> (ø)` | | | [src/LVcon514.jl](https://codecov.io/gh/JuliaSmoothOptimizers/NLSProblems.jl/pull/75/diff?src=pr&el=tree&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=JuliaSmoothOptimizers#diff-c3JjL0xWY29uNTE0Lmps) | `81.81% <100.00%> (ø)` | | | ... and [93 more](https://codecov.io/gh/JuliaSmoothOptimizers/NLSProblems.jl/pull/75/diff?src=pr&el=tree-more&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=JuliaSmoothOptimizers) | | Help us with your feedback. Take ten seconds to tell us [how you rate us](https://about.codecov.io/nps?utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=JuliaSmoothOptimizers). Have a feature suggestion? [Share it here.](https://app.codecov.io/gh/feedback/?utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=JuliaSmoothOptimizers)

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amontoison commented 2 years ago

I checked the NLS models with https://github.com/JuliaSmoothOptimizers/NLPModelsJuMP.jl/pull/139 for information.