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SciMLAlgorithms

Definition of the AbstractSciMLAlgorithm Interface

SciMLAlgorithms are defined as types which have dispatches to the function signature:

CommonSolve.solve(prob::AbstractSciMLProblem, alg::AbstractSciMLAlgorithm; kwargs...)

Algorithm-Specific Arguments

Note that because the keyword arguments of solve are designed to be common across the whole problem type, algorithms should have the algorithm-specific keyword arguments defined as part of the algorithm constructor. For example, Rodas5 has a choice of autodiff::Bool which is not common across all ODE solvers, and thus autodiff is a algorithm-specific keyword argument handled via Rodas5(autodiff=true).

Remake

Note that remake is applicable to AbstractSciMLAlgorithm types, but this is not used in the public API. It's used for solvers to swap out components like ForwardDiff chunk sizes.

Common Algorithm Keyword Arguments

Commonly used algorithm keyword arguments are:

Traits

SciMLBase.isautodifferentiable — Function
isautodifferentiable(alg::AbstractDEAlgorithm)

Trait declaration for whether an algorithm is compatible with direct automatic differentiation, i.e. can have algorithms like ForwardDiff or ReverseDiff attempt to differentiate directly through the solver.

Defaults to false as only pure-Julia algorithms can have this be true.

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SciMLBase.allows_arbitrary_number_types — Function
allows_arbitrary_number_types(alg::AbstractDEAlgorithm)

Trait declaration for whether an algorithm is compatible with direct automatic differentiation, i.e. can have algorithms like ForwardDiff or ReverseDiff attempt to differentiate directly through the solver.

Defaults to false as only pure-Julia algorithms can have this be true.

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SciMLBase.allowscomplex — Function
allowscomplex(alg::AbstractDEAlgorithm)

Trait declaration for whether an algorithm is compatible with having complex numbers as the state variables.

Defaults to false.

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SciMLBase.isadaptive — Function
isadaptive(alg::AbstractDEAlgorithm)

Trait declaration for whether an algorithm uses adaptivity, i.e. has a non-quasi-static compute graph.

Defaults to true.

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isadaptive(i::DEIntegrator)

Checks if the integrator is adaptive

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SciMLBase.isdiscrete — Function
isdiscrete(alg::AbstractDEAlgorithm)

Trait declaration for whether an algorithm allows for discrete state values, such as integers.

Defaults to false.

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SciMLBase.forwarddiffs_model — Function
forwarddiffs_model(alg::AbstractDEAlgorithm)

Trait declaration for whether an algorithm uses ForwardDiff.jl on the model function is called with ForwardDiff.jl

Defaults to false as only pure-Julia algorithms can have this be true.

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SciMLBase.forwarddiffs_model_time — Function
forwarddiffs_model_time(alg::AbstractDEAlgorithm)

Trait declaration for whether an algorithm uses ForwardDiff.jl on the model f(u,p,t) function is called with ForwardDiff.jl on the t argument.

Defaults to false as only a few pure-Julia algorithms (Rosenbrock methods) have this as true

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SciMLBase.has_init — Function
has_init(a) -> Bool

Trait for specifying whether the passed algorithm supports init. Any inited object can solve!.

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SciMLBase.has_step — Function
has_step(a) -> Bool

Trait for specifying whether the passed algorithm supports step!, specifying a more direct control over the internal solver process. See https://docs.sciml.ai/SciMLBase/stable/interfaces/Init_Solve/#init-and-the-Iterator-Interface for more details.

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Abstract SciML Algorithms