Merge pull request #1050 from SciML/revert-1049-solve_dispatches

Revert "Add solve, solve!, init"

Author: ChrisRackauckas

lib/OptimizationBase/src/solve.jl

@@ -29,147 +29,6 @@ function Base.showerror(io::IO, e::OptimizerMissingError)
     print(e.alg)
 end
 
-"""
-```julia
-solve(prob::OptimizationProblem, alg::AbstractOptimizationAlgorithm,
-    args...; kwargs...)::OptimizationSolution
-```
-
-For information about the returned solution object, refer to the documentation for [`OptimizationSolution`](@ref)
-
-## Keyword Arguments
-
-The arguments to `solve` are common across all of the optimizers.
-These common arguments are:
-
-  - `maxiters`: the maximum number of iterations
-  - `maxtime`: the maximum amount of time (typically in seconds) the optimization runs for
-  - `abstol`: absolute tolerance in changes of the objective value
-  - `reltol`: relative tolerance  in changes of the objective value
-  - `callback`: a callback function
-
-Some optimizer algorithms have special keyword arguments documented in the
-solver portion of the documentation and their respective documentation.
-These arguments can be passed as `kwargs...` to `solve`. Similarly, the special
-keyword arguments for the `local_method` of a global optimizer are passed as a
-`NamedTuple` to `local_options`.
-
-Over time, we hope to cover more of these keyword arguments under the common interface.
-
-A warning will be shown if a common argument is not implemented for an optimizer.
-
-## Callback Functions
-
-The callback function `callback` is a function that is called after every optimizer
-step. Its signature is:
-
-```julia
-callback = (state, loss_val) -> false
-```
-
-where `state` is an `OptimizationState` and stores information for the current
-iteration of the solver and `loss_val` is loss/objective value. For more
-information about the fields of the `state` look at the `OptimizationState`
-documentation. The callback should return a Boolean value, and the default
-should be `false`, so the optimization stops if it returns `true`.
-
-### Callback Example
-
-Here we show an example of a callback function that plots the prediction at the current value of the optimization variables.
-For a visualization callback, we would need the prediction at the current parameters i.e. the solution of the `ODEProblem` `prob`.
-So we call the `predict` function within the callback again.
-
-```julia
-function predict(u)
-    Array(solve(prob, Tsit5(), p = u))
-end
-
-function loss(u, p)
-    pred = predict(u)
-    sum(abs2, batch .- pred)
-end
-
-callback = function (state, l; doplot = false) #callback function to observe training
-    display(l)
-    # plot current prediction against data
-    if doplot
-        pred = predict(state.u)
-        pl = scatter(t, ode_data[1, :], label = "data")
-        scatter!(pl, t, pred[1, :], label = "prediction")
-        display(plot(pl))
-    end
-    return false
-end
-```
-
-If the chosen method is a global optimizer that employs a local optimization
-method, a similar set of common local optimizer arguments exists. Look at `MLSL` or `AUGLAG`
-from NLopt for an example. The common local optimizer arguments are:
-
-  - `local_method`: optimizer used for local optimization in global method
-  - `local_maxiters`: the maximum number of iterations
-  - `local_maxtime`: the maximum amount of time (in seconds) the optimization runs for
-  - `local_abstol`: absolute tolerance in changes of the objective value
-  - `local_reltol`: relative tolerance  in changes of the objective value
-  - `local_options`: `NamedTuple` of keyword arguments for local optimizer
-"""
-function solve(prob::OptimizationProblem, alg, args...;
-        kwargs...)::AbstractOptimizationSolution
-    if supports_opt_cache_interface(alg)
-        solve!(init(prob, alg, args...; kwargs...))
-    else
-        if prob.u0 !== nothing && !isconcretetype(eltype(prob.u0))
-            throw(NonConcreteEltypeError(eltype(prob.u0)))
-        end
-        _check_opt_alg(prob, alg; kwargs...)
-        __solve(prob, alg, args...; kwargs...)
-    end
-end
-
-"""
-```julia
-init(prob::OptimizationProblem, alg::AbstractOptimizationAlgorithm, args...; kwargs...)
-```
-
-## Keyword Arguments
-
-The arguments to `init` are the same as to `solve` and common across all of the optimizers.
-These common arguments are:
-
-  - `maxiters` (the maximum number of iterations)
-  - `maxtime` (the maximum of time the optimization runs for)
-  - `abstol` (absolute tolerance in changes of the objective value)
-  - `reltol` (relative tolerance  in changes of the objective value)
-  - `callback` (a callback function)
-
-Some optimizer algorithms have special keyword arguments documented in the
-solver portion of the documentation and their respective documentation.
-These arguments can be passed as `kwargs...` to `init`.
-
-See also [`solve(prob::OptimizationProblem, alg, args...; kwargs...)`](@ref)
-"""
-function init(prob::OptimizationProblem, alg, args...; kwargs...)::AbstractOptimizationCache
-    if prob.u0 !== nothing && !isconcretetype(eltype(prob.u0))
-        throw(NonConcreteEltypeError(eltype(prob.u0)))
-    end
-    _check_opt_alg(prob::OptimizationProblem, alg; kwargs...)
-    cache = __init(prob, alg, args...; prob.kwargs..., kwargs...)
-    return cache
-end
-
-"""
-```julia
-solve!(cache::AbstractOptimizationCache)
-```
-
-Solves the given optimization cache.
-
-See also [`init(prob::OptimizationProblem, alg, args...; kwargs...)`](@ref)
-"""
-function solve!(cache::AbstractOptimizationCache)::AbstractOptimizationSolution
-    __solve(cache)
-end
-
 # Algorithm compatibility checking function
 function _check_opt_alg(prob::SciMLBase.OptimizationProblem, alg; kwargs...)
     !SciMLBase.allowsbounds(alg) && (!isnothing(prob.lb) || !isnothing(prob.ub)) &&