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Remove OptimizationBase.jl pieces
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‎src/solve.jl‎

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@@ -31,223 +31,6 @@ function Base.showerror(io::IO, e::NonConcreteEltypeError)
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print(io, e.eltype)
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end
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# Skip the DiffEqBase handling
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struct IncompatibleOptimizerError <: Exception
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err::String
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end
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function Base.showerror(io::IO, e::IncompatibleOptimizerError)
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print(io, e.err)
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end
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"""
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```julia
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solve(prob::OptimizationProblem, alg::AbstractOptimizationAlgorithm,
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args...; kwargs...)::OptimizationSolution
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```
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For information about the returned solution object, refer to the documentation for [`OptimizationSolution`](@ref)
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## Keyword Arguments
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The arguments to `solve` are common across all of the optimizers.
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These common arguments are:
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- `maxiters`: the maximum number of iterations
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- `maxtime`: the maximum amount of time (typically in seconds) the optimization runs for
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- `abstol`: absolute tolerance in changes of the objective value
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- `reltol`: relative tolerance in changes of the objective value
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- `callback`: a callback function
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Some optimizer algorithms have special keyword arguments documented in the
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solver portion of the documentation and their respective documentation.
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These arguments can be passed as `kwargs...` to `solve`. Similarly, the special
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keyword arguments for the `local_method` of a global optimizer are passed as a
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`NamedTuple` to `local_options`.
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Over time, we hope to cover more of these keyword arguments under the common interface.
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A warning will be shown if a common argument is not implemented for an optimizer.
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## Callback Functions
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The callback function `callback` is a function that is called after every optimizer
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step. Its signature is:
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```julia
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callback = (state, loss_val) -> false
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```
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where `state` is an `OptimizationState` and stores information for the current
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iteration of the solver and `loss_val` is loss/objective value. For more
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information about the fields of the `state` look at the `OptimizationState`
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documentation. The callback should return a Boolean value, and the default
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should be `false`, so the optimization stops if it returns `true`.
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### Callback Example
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Here we show an example of a callback function that plots the prediction at the current value of the optimization variables.
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For a visualization callback, we would need the prediction at the current parameters i.e. the solution of the `ODEProblem` `prob`.
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So we call the `predict` function within the callback again.
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```julia
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function predict(u)
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Array(solve(prob, Tsit5(), p = u))
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end
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function loss(u, p)
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pred = predict(u)
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sum(abs2, batch .- pred)
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end
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callback = function (state, l; doplot = false) #callback function to observe training
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display(l)
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# plot current prediction against data
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if doplot
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pred = predict(state.u)
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pl = scatter(t, ode_data[1, :], label = "data")
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scatter!(pl, t, pred[1, :], label = "prediction")
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display(plot(pl))
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end
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return false
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end
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```
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If the chosen method is a global optimizer that employs a local optimization
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method, a similar set of common local optimizer arguments exists. Look at `MLSL` or `AUGLAG`
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from NLopt for an example. The common local optimizer arguments are:
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- `local_method`: optimizer used for local optimization in global method
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- `local_maxiters`: the maximum number of iterations
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- `local_maxtime`: the maximum amount of time (in seconds) the optimization runs for
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- `local_abstol`: absolute tolerance in changes of the objective value
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- `local_reltol`: relative tolerance in changes of the objective value
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- `local_options`: `NamedTuple` of keyword arguments for local optimizer
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"""
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function solve(prob::OptimizationProblem, alg, args...;
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kwargs...)::AbstractOptimizationSolution
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if supports_opt_cache_interface(alg)
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solve!(init(prob, alg, args...; kwargs...))
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else
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if prob.u0 !== nothing && !isconcretetype(eltype(prob.u0))
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throw(NonConcreteEltypeError(eltype(prob.u0)))
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end
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_check_opt_alg(prob, alg; kwargs...)
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__solve(prob, alg, args...; kwargs...)
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end
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end
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function SciMLBase.solve(
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prob::EnsembleProblem{T}, args...; kwargs...) where {T <: OptimizationProblem}
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return SciMLBase.__solve(prob, args...; kwargs...)
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end
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function _check_opt_alg(prob::OptimizationProblem, alg; kwargs...)
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!allowsbounds(alg) && (!isnothing(prob.lb) || !isnothing(prob.ub)) &&
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throw(IncompatibleOptimizerError("The algorithm $(typeof(alg)) does not support box constraints. Either remove the `lb` or `ub` bounds passed to `OptimizationProblem` or use a different algorithm."))
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requiresbounds(alg) && isnothing(prob.lb) &&
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throw(IncompatibleOptimizerError("The algorithm $(typeof(alg)) requires box constraints. Either pass `lb` and `ub` bounds to `OptimizationProblem` or use a different algorithm."))
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!allowsconstraints(alg) && !isnothing(prob.f.cons) &&
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throw(IncompatibleOptimizerError("The algorithm $(typeof(alg)) does not support constraints. Either remove the `cons` function passed to `OptimizationFunction` or use a different algorithm."))
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requiresconstraints(alg) && isnothing(prob.f.cons) &&
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throw(IncompatibleOptimizerError("The algorithm $(typeof(alg)) requires constraints, pass them with the `cons` kwarg in `OptimizationFunction`."))
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# Check that if constraints are present and the algorithm supports constraints, both lcons and ucons are provided
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allowsconstraints(alg) && !isnothing(prob.f.cons) &&
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(isnothing(prob.lcons) || isnothing(prob.ucons)) &&
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throw(ArgumentError("Constrained optimization problem requires both `lcons` and `ucons` to be provided to OptimizationProblem. " *
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"Example: OptimizationProblem(optf, u0, p; lcons=[-Inf], ucons=[0.0])"))
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!allowscallback(alg) && haskey(kwargs, :callback) &&
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throw(IncompatibleOptimizerError("The algorithm $(typeof(alg)) does not support callbacks, remove the `callback` keyword argument from the `solve` call."))
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requiresgradient(alg) && !(prob.f isa AbstractOptimizationFunction) &&
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throw(IncompatibleOptimizerError("The algorithm $(typeof(alg)) requires gradients, hence use `OptimizationFunction` to generate them with an automatic differentiation backend e.g. `OptimizationFunction(f, AutoForwardDiff())` or pass it in with `grad` kwarg."))
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requireshessian(alg) && !(prob.f isa AbstractOptimizationFunction) &&
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throw(IncompatibleOptimizerError("The algorithm $(typeof(alg)) requires hessians, hence use `OptimizationFunction` to generate them with an automatic differentiation backend e.g. `OptimizationFunction(f, AutoFiniteDiff(); kwargs...)` or pass them in with `hess` kwarg."))
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requiresconsjac(alg) && !(prob.f isa AbstractOptimizationFunction) &&
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throw(IncompatibleOptimizerError("The algorithm $(typeof(alg)) requires constraint jacobians, hence use `OptimizationFunction` to generate them with an automatic differentiation backend e.g. `OptimizationFunction(f, AutoFiniteDiff(); kwargs...)` or pass them in with `cons` kwarg."))
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requiresconshess(alg) && !(prob.f isa AbstractOptimizationFunction) &&
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throw(IncompatibleOptimizerError("The algorithm $(typeof(alg)) requires constraint hessians, hence use `OptimizationFunction` to generate them with an automatic differentiation backend e.g. `OptimizationFunction(f, AutoFiniteDiff(), AutoFiniteDiff(hess=true); kwargs...)` or pass them in with `cons` kwarg."))
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return
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end
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const OPTIMIZER_MISSING_ERROR_MESSAGE = """
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Optimization algorithm not found. Either the chosen algorithm is not a valid solver
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choice for the `OptimizationProblem`, or the Optimization solver library is not loaded.
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Make sure that you have loaded an appropriate Optimization.jl solver library, for example,
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`solve(prob,Optim.BFGS())` requires `using OptimizationOptimJL` and
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`solve(prob,Adam())` requires `using OptimizationOptimisers`.
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For more information, see the Optimization.jl documentation: <https://docs.sciml.ai/Optimization/stable/>.
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"""
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struct OptimizerMissingError <: Exception
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alg::Any
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end
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function Base.showerror(io::IO, e::OptimizerMissingError)
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println(io, OPTIMIZER_MISSING_ERROR_MESSAGE)
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print(io, "Chosen Optimizer: ")
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print(e.alg)
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end
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"""
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```julia
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init(prob::OptimizationProblem, alg::AbstractOptimizationAlgorithm, args...; kwargs...)
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```
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## Keyword Arguments
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The arguments to `init` are the same as to `solve` and common across all of the optimizers.
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These common arguments are:
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- `maxiters` (the maximum number of iterations)
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- `maxtime` (the maximum of time the optimization runs for)
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- `abstol` (absolute tolerance in changes of the objective value)
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- `reltol` (relative tolerance in changes of the objective value)
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- `callback` (a callback function)
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Some optimizer algorithms have special keyword arguments documented in the
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solver portion of the documentation and their respective documentation.
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These arguments can be passed as `kwargs...` to `init`.
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See also [`solve(prob::OptimizationProblem, alg, args...; kwargs...)`](@ref)
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"""
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function init(prob::OptimizationProblem, alg, args...; kwargs...)::AbstractOptimizationCache
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if prob.u0 !== nothing && !isconcretetype(eltype(prob.u0))
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throw(NonConcreteEltypeError(eltype(prob.u0)))
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end
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_check_opt_alg(prob::OptimizationProblem, alg; kwargs...)
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cache = __init(prob, alg, args...; prob.kwargs..., kwargs...)
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return cache
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end
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"""
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```julia
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solve!(cache::AbstractOptimizationCache)
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```
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Solves the given optimization cache.
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See also [`init(prob::OptimizationProblem, alg, args...; kwargs...)`](@ref)
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"""
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function solve!(cache::AbstractOptimizationCache)::AbstractOptimizationSolution
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__solve(cache)
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end
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# needs to be defined for each cache
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supports_opt_cache_interface(alg) = false
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function __solve(cache::AbstractOptimizationCache)::AbstractOptimizationSolution end
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function __init(prob::OptimizationProblem, alg, args...;
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kwargs...)::AbstractOptimizationCache
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throw(OptimizerMissingError(alg))
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end
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# if no cache interface is supported at least the following method has to be defined
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function __solve(prob::OptimizationProblem, alg, args...; kwargs...)
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throw(OptimizerMissingError(alg))
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end
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# Functions used in solve dispatches
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eltypedual(x) = false

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