GitHub

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.

source
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.

source
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.

source
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.

source
isadaptive(i::DEIntegrator)

Checks if the integrator is adaptive

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

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

Defaults to false.

source
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.

source
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

source
SciMLBase.forwarddiff_chunksize — Function
forwarddiff_chunksize(alg::AbstractSciMLAlgorithm)

Trait declaration for the ForwardDiff chunk size used by the algorithm when calling the model function with ForwardDiff.Dual numbers.

Returns a Val{N}(): Val(0) means unspecified (the framework will choose a default, typically 1 for FunctionWrapper compatibility). Val(N) for any positive integer N means the algorithm will use chunk size N.

This is used by DiffEqBase to compile FunctionWrapper variants with matching Dual number chunk sizes, avoiding NoFunctionWrapperFoundError.

Defaults to Val(0) (unspecified).

source
SciMLBase.has_lazy_interpolation — Function
has_lazy_interpolation(alg::AbstractDEAlgorithm)

Trait declaration for whether an algorithm computes the solution interpolation lazily.

Defaults to false.

source
SciMLBase.allows_late_binding_tstops — Function
allows_late_binding_tstops(
    alg::SciMLBase.AbstractODEAlgorithm
) -> Bool

Trait declaration for whether an algorithm supports specifying tstops as a function tstops(p, tspan) to be called after initialization.

Defaults to false.

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

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

source
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.

source

Abstract SciML Algorithms