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Standardizing NonlinearSolve Return Codes #587

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218 changes: 109 additions & 109 deletions Project.toml
Original file line number Diff line number Diff line change
@@ -1,109 +1,109 @@
name = "SciMLBase"
uuid = "0bca4576-84f4-4d90-8ffe-ffa030f20462"
authors = ["Chris Rackauckas <[email protected]> and contributors"]
version = "2.18.0"
[deps]
ADTypes = "47edcb42-4c32-4615-8424-f2b9edc5f35b"
ArrayInterface = "4fba245c-0d91-5ea0-9b3e-6abc04ee57a9"
CommonSolve = "38540f10-b2f7-11e9-35d8-d573e4eb0ff2"
ConstructionBase = "187b0558-2788-49d3-abe0-74a17ed4e7c9"
Distributed = "8ba89e20-285c-5b6f-9357-94700520ee1b"
DocStringExtensions = "ffbed154-4ef7-542d-bbb7-c09d3a79fcae"
EnumX = "4e289a0a-7415-4d19-859d-a7e5c4648b56"
FillArrays = "1a297f60-69ca-5386-bcde-b61e274b549b"
FunctionWrappersWrappers = "77dc65aa-8811-40c2-897b-53d922fa7daf"
IteratorInterfaceExtensions = "82899510-4779-5014-852e-03e436cf321d"
LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
Logging = "56ddb016-857b-54e1-b83d-db4d58db5568"
Markdown = "d6f4376e-aef5-505a-96c1-9c027394607a"
PrecompileTools = "aea7be01-6a6a-4083-8856-8a6e6704d82a"
Preferences = "21216c6a-2e73-6563-6e65-726566657250"
Printf = "de0858da-6303-5e67-8744-51eddeeeb8d7"
RecipesBase = "3cdcf5f2-1ef4-517c-9805-6587b60abb01"
RecursiveArrayTools = "731186ca-8d62-57ce-b412-fbd966d074cd"
Reexport = "189a3867-3050-52da-a836-e630ba90ab69"
RuntimeGeneratedFunctions = "7e49a35a-f44a-4d26-94aa-eba1b4ca6b47"
SciMLOperators = "c0aeaf25-5076-4817-a8d5-81caf7dfa961"
StaticArraysCore = "1e83bf80-4336-4d27-bf5d-d5a4f845583c"
Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
SymbolicIndexingInterface = "2efcf032-c050-4f8e-a9bb-153293bab1f5"
Tables = "bd369af6-aec1-5ad0-b16a-f7cc5008161c"
TruncatedStacktraces = "781d530d-4396-4725-bb49-402e4bee1e77"
[weakdeps]
ChainRules = "082447d4-558c-5d27-93f4-14fc19e9eca2"
ChainRulesCore = "d360d2e6-b24c-11e9-a2a3-2a2ae2dbcce4"
PartialFunctions = "570af359-4316-4cb7-8c74-252c00c2016b"
PyCall = "438e738f-606a-5dbb-bf0a-cddfbfd45ab0"
PythonCall = "6099a3de-0909-46bc-b1f4-468b9a2dfc0d"
RCall = "6f49c342-dc21-5d91-9882-a32aef131414"
Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f"
[extensions]
SciMLBaseChainRulesCoreExt = "ChainRulesCore"
SciMLBasePartialFunctionsExt = "PartialFunctions"
SciMLBasePyCallExt = "PyCall"
SciMLBasePythonCallExt = "PythonCall"
SciMLBaseRCallExt = "RCall"
SciMLBaseZygoteExt = "Zygote"
[compat]
ADTypes = "0.2.5"
ArrayInterface = "7.6"
ChainRules = "1.58.0"
ChainRulesCore = "1.18"
CommonSolve = "0.2.4"
ConstructionBase = "1.5"
DataFrames = "1.6"
Distributed = "1.9"
DocStringExtensions = "0.9"
EnumX = "1"
FillArrays = "1.9"
FunctionWrappersWrappers = "0.1.3"
IteratorInterfaceExtensions = "^1"
LinearAlgebra = "1.9"
Logging = "1.9"
Markdown = "1.9"
ModelingToolkit = "8.74"
PartialFunctions = "1.1"
PrecompileTools = "1.2"
Preferences = "1.3"
Printf = "1.9"
PyCall = "1.96"
PythonCall = "0.9.15"
RCall = "0.14.0"
RecipesBase = "1.0"
RecursiveArrayTools = "3.3.4"
Reexport = "1"
RuntimeGeneratedFunctions = "0.5"
SciMLOperators = "0.3.7"
StaticArrays = "1.7"
StaticArraysCore = "1.4"
Statistics = "1.9"
SymbolicIndexingInterface = "0.3.2"
Tables = "1.11"
TruncatedStacktraces = "1.4"
Zygote = "0.6.67"
julia = "1.9"
[extras]
Aqua = "4c88cf16-eb10-579e-8560-4a9242c79595"
ChainRulesCore = "d360d2e6-b24c-11e9-a2a3-2a2ae2dbcce4"
DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
DelayDiffEq = "bcd4f6db-9728-5f36-b5f7-82caef46ccdb"
ModelingToolkit = "961ee093-0014-501f-94e3-6117800e7a78"
OrdinaryDiffEq = "1dea7af3-3e70-54e6-95c3-0bf5283fa5ed"
PartialFunctions = "570af359-4316-4cb7-8c74-252c00c2016b"
Pkg = "44cfe95a-1eb2-52ea-b672-e2afdf69b78f"
PyCall = "438e738f-606a-5dbb-bf0a-cddfbfd45ab0"
PythonCall = "6099a3de-0909-46bc-b1f4-468b9a2dfc0d"
RCall = "6f49c342-dc21-5d91-9882-a32aef131414"
SafeTestsets = "1bc83da4-3b8d-516f-aca4-4fe02f6d838f"
StaticArrays = "90137ffa-7385-5640-81b9-e52037218182"
StochasticDiffEq = "789caeaf-c7a9-5a7d-9973-96adeb23e2a0"
Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f"
[targets]
test = ["Pkg", "PyCall", "PythonCall", "SafeTestsets", "Test", "StaticArrays", "StochasticDiffEq", "Aqua", "Zygote", "PartialFunctions", "DataFrames", "ModelingToolkit", "OrdinaryDiffEq"]
name = "SciMLBase"
uuid = "0bca4576-84f4-4d90-8ffe-ffa030f20462"
authors = ["Chris Rackauckas <[email protected]> and contributors"]
version = "2.19.0"

[deps]
ADTypes = "47edcb42-4c32-4615-8424-f2b9edc5f35b"
ArrayInterface = "4fba245c-0d91-5ea0-9b3e-6abc04ee57a9"
CommonSolve = "38540f10-b2f7-11e9-35d8-d573e4eb0ff2"
ConstructionBase = "187b0558-2788-49d3-abe0-74a17ed4e7c9"
Distributed = "8ba89e20-285c-5b6f-9357-94700520ee1b"
DocStringExtensions = "ffbed154-4ef7-542d-bbb7-c09d3a79fcae"
EnumX = "4e289a0a-7415-4d19-859d-a7e5c4648b56"
FillArrays = "1a297f60-69ca-5386-bcde-b61e274b549b"
FunctionWrappersWrappers = "77dc65aa-8811-40c2-897b-53d922fa7daf"
IteratorInterfaceExtensions = "82899510-4779-5014-852e-03e436cf321d"
LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
Logging = "56ddb016-857b-54e1-b83d-db4d58db5568"
Markdown = "d6f4376e-aef5-505a-96c1-9c027394607a"
PrecompileTools = "aea7be01-6a6a-4083-8856-8a6e6704d82a"
Preferences = "21216c6a-2e73-6563-6e65-726566657250"
Printf = "de0858da-6303-5e67-8744-51eddeeeb8d7"
RecipesBase = "3cdcf5f2-1ef4-517c-9805-6587b60abb01"
RecursiveArrayTools = "731186ca-8d62-57ce-b412-fbd966d074cd"
Reexport = "189a3867-3050-52da-a836-e630ba90ab69"
RuntimeGeneratedFunctions = "7e49a35a-f44a-4d26-94aa-eba1b4ca6b47"
SciMLOperators = "c0aeaf25-5076-4817-a8d5-81caf7dfa961"
StaticArraysCore = "1e83bf80-4336-4d27-bf5d-d5a4f845583c"
Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
SymbolicIndexingInterface = "2efcf032-c050-4f8e-a9bb-153293bab1f5"
Tables = "bd369af6-aec1-5ad0-b16a-f7cc5008161c"
TruncatedStacktraces = "781d530d-4396-4725-bb49-402e4bee1e77"

[weakdeps]
ChainRules = "082447d4-558c-5d27-93f4-14fc19e9eca2"
ChainRulesCore = "d360d2e6-b24c-11e9-a2a3-2a2ae2dbcce4"
PartialFunctions = "570af359-4316-4cb7-8c74-252c00c2016b"
PyCall = "438e738f-606a-5dbb-bf0a-cddfbfd45ab0"
PythonCall = "6099a3de-0909-46bc-b1f4-468b9a2dfc0d"
RCall = "6f49c342-dc21-5d91-9882-a32aef131414"
Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f"

[extensions]
SciMLBaseChainRulesCoreExt = "ChainRulesCore"
SciMLBasePartialFunctionsExt = "PartialFunctions"
SciMLBasePyCallExt = "PyCall"
SciMLBasePythonCallExt = "PythonCall"
SciMLBaseRCallExt = "RCall"
SciMLBaseZygoteExt = "Zygote"

[compat]
ADTypes = "0.2.5"
ArrayInterface = "7.6"
ChainRules = "1.58.0"
ChainRulesCore = "1.18"
CommonSolve = "0.2.4"
ConstructionBase = "1.5"
DataFrames = "1.6"
Distributed = "1.9"
DocStringExtensions = "0.9"
EnumX = "1"
FillArrays = "1.9"
FunctionWrappersWrappers = "0.1.3"
IteratorInterfaceExtensions = "^1"
LinearAlgebra = "1.9"
Logging = "1.9"
Markdown = "1.9"
ModelingToolkit = "8.74"
PartialFunctions = "1.1"
PrecompileTools = "1.2"
Preferences = "1.3"
Printf = "1.9"
PyCall = "1.96"
PythonCall = "0.9.15"
RCall = "0.14.0"
RecipesBase = "1.0"
RecursiveArrayTools = "3.3.4"
Reexport = "1"
RuntimeGeneratedFunctions = "0.5"
SciMLOperators = "0.3.7"
StaticArrays = "1.7"
StaticArraysCore = "1.4"
Statistics = "1.9"
SymbolicIndexingInterface = "0.3.2"
Tables = "1.11"
TruncatedStacktraces = "1.4"
Zygote = "0.6.67"
julia = "1.9"

[extras]
Aqua = "4c88cf16-eb10-579e-8560-4a9242c79595"
ChainRulesCore = "d360d2e6-b24c-11e9-a2a3-2a2ae2dbcce4"
DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
DelayDiffEq = "bcd4f6db-9728-5f36-b5f7-82caef46ccdb"
ModelingToolkit = "961ee093-0014-501f-94e3-6117800e7a78"
OrdinaryDiffEq = "1dea7af3-3e70-54e6-95c3-0bf5283fa5ed"
PartialFunctions = "570af359-4316-4cb7-8c74-252c00c2016b"
Pkg = "44cfe95a-1eb2-52ea-b672-e2afdf69b78f"
PyCall = "438e738f-606a-5dbb-bf0a-cddfbfd45ab0"
PythonCall = "6099a3de-0909-46bc-b1f4-468b9a2dfc0d"
RCall = "6f49c342-dc21-5d91-9882-a32aef131414"
SafeTestsets = "1bc83da4-3b8d-516f-aca4-4fe02f6d838f"
StaticArrays = "90137ffa-7385-5640-81b9-e52037218182"
StochasticDiffEq = "789caeaf-c7a9-5a7d-9973-96adeb23e2a0"
Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f"

[targets]
test = ["Pkg", "PyCall", "PythonCall", "SafeTestsets", "Test", "StaticArrays", "StochasticDiffEq", "Aqua", "Zygote", "PartialFunctions", "DataFrames", "ModelingToolkit", "OrdinaryDiffEq"]
38 changes: 38 additions & 0 deletions src/retcodes.jl
Original file line number Diff line number Diff line change
Expand Up @@ -237,6 +237,9 @@ EnumX.@enumx ReturnCode begin
nonlinear solver was chosen. If fixed point iteration is used on a stiff problem,
it will be faster by avoiding the Jacobian but it will make a stiff ODE solver not
stable for stiff problems!
* For nonlinear solvers, this can occur if certain threshold was exceeded. For example,
in approximate jacobian solvers like Broyden, Klement, etc. if the number of jacobian
resets exceeds the threshold, then this return code is given.

## Properties

Expand Down Expand Up @@ -343,6 +346,41 @@ EnumX.@enumx ReturnCode begin
* successful_retcode = false
"""
MaxTime

"""
`ReturnCode.InternalLineSearchFailed`

Internal Line Search used by the algorithm has failed.

## Properties

* successful_retcode = false
"""
InternalLineSearchFailed

"""
`ReturnCode.ShrinkThresholdExceeded`

The trust region radius was shrunk more times than the provided threshold.

## Properties

* successful_retcode = false
"""
ShrinkThresholdExceeded

"""
`ReturnCode.Stalled`

The solution has stalled. This is only returned by algorithms for which stalling is a
failure mode. Certain solvers like Nonlinear Least Squares solvers are considered
successful if the solution has stalled, in those cases `ReturnCode.Success` is returned.

## Properties

* successful_retcode = false
"""
Stalled
end

Base.:(!=)(retcode::ReturnCode.T, s::Symbol) = Symbol(retcode) != s
Expand Down
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