diff --git a/Project.toml b/Project.toml
index 312e7964b..787aa4e6d 100644
--- a/Project.toml
+++ b/Project.toml
@@ -19,6 +19,7 @@ SparseArrays = "2f01184e-e22b-5df5-ae63-d93ebab69eaf"
 TerminalLoggers = "5d786b92-1e48-4d6f-9151-6b4477ca9bed"
 
 [sources]
+SciMLBase = {url = "https://github.com/SciML/SciMLBase.jl", rev = "master"}
 OptimizationBase = {path = "lib/OptimizationBase"}
 OptimizationLBFGSB = {path = "lib/OptimizationLBFGSB"}
 OptimizationMOI = {path = "lib/OptimizationMOI"}
diff --git a/lib/OptimizationBase/src/OptimizationBase.jl b/lib/OptimizationBase/src/OptimizationBase.jl
index 244f086ce..c478202d9 100644
--- a/lib/OptimizationBase/src/OptimizationBase.jl
+++ b/lib/OptimizationBase/src/OptimizationBase.jl
@@ -7,7 +7,7 @@ using Reexport
 using ArrayInterface, Base.Iterators, SparseArrays, LinearAlgebra
 import SciMLBase: OptimizationProblem,
                   OptimizationFunction, ObjSense,
-                  MaxSense, MinSense, OptimizationStats
+                  MaxSense, MinSense, OptimizationStats, NonConcreteEltypeError
 export ObjSense, MaxSense, MinSense
 
 using FastClosures
diff --git a/lib/OptimizationBase/src/solve.jl b/lib/OptimizationBase/src/solve.jl
index 70688bb54..580db338e 100644
--- a/lib/OptimizationBase/src/solve.jl
+++ b/lib/OptimizationBase/src/solve.jl
@@ -29,6 +29,147 @@ 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 SciMLBase.solve(prob::OptimizationProblem, alg, args...;
+        kwargs...)::SciMLBase.AbstractOptimizationSolution
+    if supports_opt_cache_interface(alg)
+        SciMLBase.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...)
+        SciMLBase.__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 SciMLBase.init(prob::OptimizationProblem, alg, args...; kwargs...)::SciMLBase.AbstractOptimizationCache
+    if prob.u0 !== nothing && !isconcretetype(eltype(prob.u0))
+        throw(NonConcreteEltypeError(eltype(prob.u0)))
+    end
+    _check_opt_alg(prob::OptimizationProblem, alg; kwargs...)
+    cache = SciMLBase.__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 SciMLBase.solve!(cache::SciMLBase.AbstractOptimizationCache)::SciMLBase.AbstractOptimizationSolution
+    SciMLBase.__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)) &&
@@ -64,15 +205,15 @@ end
 # Base solver dispatch functions (these will be extended by specific solver packages)
 supports_opt_cache_interface(alg) = false
 
-function __solve(cache::SciMLBase.AbstractOptimizationCache)::SciMLBase.AbstractOptimizationSolution
+function SciMLBase.__solve(cache::SciMLBase.AbstractOptimizationCache)::SciMLBase.AbstractOptimizationSolution
     throw(OptimizerMissingError(cache.opt))
 end
 
-function __init(prob::SciMLBase.OptimizationProblem, alg, args...;
+function SciMLBase.__init(prob::SciMLBase.OptimizationProblem, alg, args...;
         kwargs...)::SciMLBase.AbstractOptimizationCache
     throw(OptimizerMissingError(alg))
 end
 
-function __solve(prob::SciMLBase.OptimizationProblem, alg, args...; kwargs...)
+function SciMLBase.__solve(prob::SciMLBase.OptimizationProblem, alg, args...; kwargs...)
     throw(OptimizerMissingError(alg))
 end
\ No newline at end of file