Add default algorithm dispatches for AbstractSteadyStateProblem #673
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This PR adds default algorithm dispatches for AbstractSteadyStateProblem to use the same defaults as NonlinearProblem. The implementation mirrors the existing NonlinearProblem dispatches to ensure consistent behavior. 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <[email protected]>
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| @@ -37,6 +37,34 @@ function SciMLBase.__solve(prob::NonlinearProblem, ::Nothing, args...; kwargs... | |||
| ) | |||
| end | |||
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| function SciMLBase.__init(prob::SciMLBase.AbstractSteadyStateProblem, ::Nothing, args...; kwargs...) | |||
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| function SciMLBase.__init(prob::SciMLBase.AbstractSteadyStateProblem, ::Nothing, args...; kwargs...) | |
| function SciMLBase.__init( | |
| prob::SciMLBase.AbstractSteadyStateProblem, ::Nothing, args...; kwargs...) |
This commit adds comprehensive tests for the new AbstractSteadyStateProblem dispatches, verifying that: - Both in-place and out-of-place SteadyStateProblems work with default algorithm - The init and solve interfaces work correctly - StaticArrays are supported - Problem conversion from SteadyStateProblem to NonlinearProblem works - Various problem configurations are handled correctly 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <[email protected]>
| return SciMLBase.__init(nlprob, nothing, args...; kwargs...) | ||
| end | ||
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| function SciMLBase.__solve(prob::SciMLBase.AbstractSteadyStateProblem, ::Nothing, args...; kwargs...) |
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| function SciMLBase.__solve(prob::SciMLBase.AbstractSteadyStateProblem, ::Nothing, args...; kwargs...) | |
| function SciMLBase.__solve( | |
| prob::SciMLBase.AbstractSteadyStateProblem, ::Nothing, args...; kwargs...) |
| du[1] = 2 - 2u[1] | ||
| du[2] = u[1] - 4u[2] | ||
| end | ||
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| u0 = zeros(2) | ||
| prob_iip = SteadyStateProblem(f_iip, u0) | ||
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| sol = solve(prob_iip) | ||
| @test SciMLBase.successful_retcode(sol.retcode) | ||
| @test maximum(abs, sol.resid) < 1e-6 | ||
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| sol = solve(prob_iip, nothing) | ||
| @test SciMLBase.successful_retcode(sol.retcode) | ||
| @test maximum(abs, sol.resid) < 1e-6 | ||
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| @test sol_nl.u ≈ sol_converted.u atol=1e-10 | ||
| end | ||
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| f_static(u, p, t) = @SVector [2 - 2u[1], u[1] - 4u[2]] | ||
| u0_static = @SVector [0.0, 0.0] | ||
| prob_static = SteadyStateProblem(f_static, u0_static) | ||
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| @test SciMLBase.successful_retcode(sol.retcode) | ||
| @test maximum(abs, sol.resid) < 1e-6 | ||
| end | ||
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| prob1 = SteadyStateProblem(f_oop, [0.5, 0.5]) | ||
| sol1 = solve(prob1) | ||
| @test SciMLBase.successful_retcode(sol1.retcode) | ||
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| sol2 = solve(prob2) | ||
| @test SciMLBase.successful_retcode(sol2.retcode) | ||
| end | ||
| end |
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| end | |
| end |
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Summary
This PR adds
__initand__solvemethod dispatches forAbstractSteadyStateProblemto the default.jl file, allowing AbstractSteadyStateProblem to use the same default algorithms as NonlinearProblem.Changes
SciMLBase.__initmethod forAbstractSteadyStateProblemthat matches the NonlinearProblem implementationSciMLBase.__solvemethod forAbstractSteadyStateProblemthat matches the NonlinearProblem implementationMotivation
Currently,
AbstractSteadyStateProblemdoesn't have default algorithm dispatches, requiring users to explicitly specify an algorithm. This PR provides sensible defaults by reusing the same robust polyalgorithm defaults that are already battle-tested forNonlinearProblem.Test Plan
🤖 Generated with Claude Code