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ChrisRackauckasChrisRackauckas
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Refactor basic problems into sets of problems
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‎src/SciMLBase.jl‎

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@@ -700,7 +700,9 @@ include("problems/discrete_problems.jl")
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include("problems/implicit_discrete_problems.jl")
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include("problems/steady_state_problems.jl")
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include("problems/analytical_problems.jl")
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include("problems/basic_problems.jl")
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include("problems/linear_problems.jl")
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include("problems/nonlinear_problems.jl")
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include("problems/integral_problems.jl")
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include("problems/ode_problems.jl")
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include("problems/rode_problems.jl")
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include("problems/sde_problems.jl")

‎src/problems/integral_problems.jl‎

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@doc doc"""
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Defines an integral problem.
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Documentation Page: https://docs.sciml.ai/Integrals/stable/
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## Mathematical Specification of an Integral Problem
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Integral problems are multi-dimensional integrals defined as:
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```math
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\int_{lb}^{ub} f(u,p) du
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```
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where `p` are parameters. `u` is a `Number` or `AbstractVector`
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whose geometry matches the space being integrated.
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This space is bounded by the lowerbound `lb` and upperbound `ub`,
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which are `Number`s or `AbstractVector`s with the same geometry as `u`.
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## Problem Type
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### Constructors
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```
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IntegralProblem(f::AbstractIntegralFunction,domain,p=NullParameters(); kwargs...)
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IntegralProblem(f::AbstractIntegralFunction,lb,ub,p=NullParameters(); kwargs...)
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IntegralProblem(f,domain,p=NullParameters(); nout=nothing, batch=nothing, kwargs...)
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IntegralProblem(f,lb,ub,p=NullParameters(); nout=nothing, batch=nothing, kwargs...)
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```
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- f: the integrand, callable function `y = f(u,p)` for out-of-place (default) or an
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`IntegralFunction` or `BatchIntegralFunction` for inplace and batching optimizations.
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- domain: an object representing an integration domain, i.e. the tuple `(lb, ub)`.
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- lb: DEPRECATED: Either a number or vector of lower bounds.
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- ub: DEPRECATED: Either a number or vector of upper bounds.
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- p: The parameters associated with the problem.
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- nout: DEPRECATED (see `IntegralFunction`): length of the vector output of the integrand
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(by default the integrand is assumed to be scalar)
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- batch: DEPRECATED (see `BatchIntegralFunction`): number of points the integrand can
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evaluate simultaneously (by default there is no batching)
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- kwargs: Keyword arguments copied to the solvers.
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Additionally, we can supply iip like IntegralProblem{iip}(...) as true or false to declare at
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compile time whether the integrator function is in-place.
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### Fields
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The fields match the names of the constructor arguments.
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"""
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struct IntegralProblem{isinplace, P, F, T, K} <: AbstractIntegralProblem{isinplace}
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f::F
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domain::T
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p::P
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kwargs::K
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@add_kwonly function IntegralProblem{iip}(f::AbstractIntegralFunction{iip}, domain,
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p = NullParameters(); nout = nothing, batch = nothing,
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kwargs...) where {iip}
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warn_paramtype(p)
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new{iip, typeof(p), typeof(f), typeof(domain), typeof(kwargs)}(f,
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domain, p, kwargs)
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end
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end
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function IntegralProblem(f::AbstractIntegralFunction,
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domain,
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p = NullParameters();
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kwargs...)
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IntegralProblem{isinplace(f)}(f, domain, p; kwargs...)
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end
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@deprecate IntegralProblem{iip}(f::AbstractIntegralFunction,
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lb::Union{Number, AbstractVector{<:Number}},
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ub::Union{Number, AbstractVector{<:Number}},
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p = NullParameters(); kwargs...) where {iip} IntegralProblem{iip}(
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f, (lb, ub), p; kwargs...)
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function IntegralProblem(f, args...; kwargs...)
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IntegralProblem{isinplace(f, 3)}(f, args...; kwargs...)
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end
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function IntegralProblem{iip}(
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f, args...; nout = nothing, batch = nothing, kwargs...) where {iip}
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if nout !== nothing || batch !== nothing
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@warn "`nout` and `batch` keywords are deprecated in favor of inplace `IntegralFunction`s or `BatchIntegralFunction`s. See the updated Integrals.jl documentation for details."
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end
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g = if iip
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if batch === nothing
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output_prototype = nout === nothing ? Array{Float64, 0}(undef) :
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Vector{Float64}(undef, nout)
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IntegralFunction(f, output_prototype)
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else
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output_prototype = nout === nothing ? Float64[] :
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Matrix{Float64}(undef, nout, 0)
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BatchIntegralFunction(f, output_prototype, max_batch = batch)
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end
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else
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if batch === nothing
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IntegralFunction(f)
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else
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BatchIntegralFunction(f, max_batch = batch)
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end
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end
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IntegralProblem(g, args...; kwargs...)
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end
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function Base.getproperty(prob::IntegralProblem, name::Symbol)
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if name === :lb
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domain = getfield(prob, :domain)
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lb, ub = domain
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return lb
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elseif name === :ub
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domain = getfield(prob, :domain)
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lb, ub = domain
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return ub
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elseif name === :ps
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return ParameterIndexingProxy(prob)
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end
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return Base.getfield(prob, name)
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end
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struct QuadratureProblem end
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@deprecate QuadratureProblem(args...; kwargs...) IntegralProblem(args...; kwargs...)
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@doc doc"""
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Defines a integral problem over pre-sampled data.
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Documentation Page: https://docs.sciml.ai/Integrals/stable/
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## Mathematical Specification of a data Integral Problem
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Sampled integral problems are defined as:
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```math
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\sum_i w_i y_i
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```
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where `y_i` are sampled values of the integrand, and `w_i` are weights
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assigned by a quadrature rule, which depend on sampling points `x`.
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## Problem Type
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### Constructors
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```
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SampledIntegralProblem(y::AbstractArray, x::AbstractVector; dim=ndims(y), kwargs...)
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```
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- y: The sampled integrand, must be a subtype of `AbstractArray`.
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It is assumed that the values of `y` along dimension `dim`
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correspond to the integrand evaluated at sampling points `x`
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- x: Sampling points, must be a subtype of `AbstractVector`.
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- dim: Dimension along which to integrate. Defaults to the last dimension of `y`.
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- kwargs: Keyword arguments copied to the solvers.
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### Fields
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The fields match the names of the constructor arguments.
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"""
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struct SampledIntegralProblem{Y, X, K} <: AbstractIntegralProblem{false}
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y::Y
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x::X
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dim::Int
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kwargs::K
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@add_kwonly function SampledIntegralProblem(y::AbstractArray, x::AbstractVector;
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dim = ndims(y),
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kwargs...)
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@assert dim<=ndims(y) "The integration dimension `dim` is larger than the number of dimensions of the integrand `y`"
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@assert length(x)==size(y, dim) "The integrand `y` must have the same length as the sampling points `x` along the integrated dimension."
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@assert axes(x, 1)==axes(y, dim) "The integrand `y` must obey the same indexing as the sampling points `x` along the integrated dimension."
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new{typeof(y), typeof(x), typeof(kwargs)}(y, x, dim, kwargs)
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end
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end

‎src/problems/linear_problems.jl‎

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@doc doc"""
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Defines a linear system problem.
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Documentation Page: https://docs.sciml.ai/LinearSolve/stable/basics/LinearProblem/
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## Mathematical Specification of a Linear Problem
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### Concrete LinearProblem
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To define a `LinearProblem`, you simply need to give the `AbstractMatrix` ``A``
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and an `AbstractVector` ``b`` which defines the linear system:
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```math
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Au = b
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```
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### Matrix-Free LinearProblem
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For matrix-free versions, the specification of the problem is given by an
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operator `A(u,p,t)` which computes `A*u`, or in-place as `A(du,u,p,t)`. These
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are specified via the `AbstractSciMLOperator` interface. For more details, see
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the [SciMLBase Documentation](https://docs.sciml.ai/SciMLBase/stable/).
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Note that matrix-free versions of LinearProblem definitions are not compatible
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with all solvers. To check a solver for compatibility, use the function xxxxx.
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## Problem Type
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### Constructors
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Optionally, an initial guess ``u₀`` can be supplied which is used for iterative
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methods.
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```julia
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LinearProblem{isinplace}(A,x,p=NullParameters();u0=nothing,kwargs...)
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LinearProblem(f::AbstractSciMLOperator,u0,p=NullParameters();u0=nothing,kwargs...)
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```
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`isinplace` optionally sets whether the function is in-place or not, i.e. whether
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the solvers are allowed to mutate. By default this is true for `AbstractMatrix`,
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and for `AbstractSciMLOperator`s it matches the choice of the operator definition.
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Parameters are optional, and if not given, then a `NullParameters()` singleton
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will be used, which will throw nice errors if you try to index non-existent
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parameters. Any extra keyword arguments are passed on to the solvers.
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### Fields
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* `A`: The representation of the linear operator.
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* `b`: The right-hand side of the linear system.
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* `p`: The parameters for the problem. Defaults to `NullParameters`. Currently unused.
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* `u0`: The initial condition used by iterative solvers.
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* `kwargs`: The keyword arguments passed on to the solvers.
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"""
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struct LinearProblem{uType, isinplace, F, bType, P, K} <:
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AbstractLinearProblem{bType, isinplace}
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A::F
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b::bType
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u0::uType
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p::P
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kwargs::K
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@add_kwonly function LinearProblem{iip}(A, b, p = NullParameters(); u0 = nothing,
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kwargs...) where {iip}
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warn_paramtype(p)
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new{typeof(u0), iip, typeof(A), typeof(b), typeof(p), typeof(kwargs)}(A, b, u0, p,
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kwargs)
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end
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end
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function LinearProblem(A, b, args...; kwargs...)
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if A isa AbstractArray
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LinearProblem{true}(A, b, args...; kwargs...)
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elseif A isa Number
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LinearProblem{false}(A, b, args...; kwargs...)
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else
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LinearProblem{isinplace(A, 4)}(A, b, args...; kwargs...)
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end
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end

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