fix docs

Author: SebastianM-C

docs/src/getting_started.md

@@ -14,15 +14,15 @@ The simplest copy-pasteable code using a quasi-Newton method (LBFGS) to solve th
 
 ```@example intro
 # Import the package and define the problem to optimize
-using OptimizationLBFGSB, Zygote
+using Optimization, OptimizationLBFGSB, Zygote
 rosenbrock(u, p) = (p[1] - u[1])^2 + p[2] * (u[2] - u[1]^2)^2
 u0 = zeros(2)
 p = [1.0, 100.0]
 
 optf = OptimizationFunction(rosenbrock, AutoZygote())
 prob = OptimizationProblem(optf, u0, p)
 
-sol = solve(prob, LBFGS())
+sol = solve(prob, OptimizationLBFGSB.LBFGS())
 ```
 
 ```@example intro
@@ -134,7 +134,7 @@ looks like:
 using ForwardDiff
 optf = OptimizationFunction(rosenbrock, Optimization.AutoForwardDiff())
 prob = OptimizationProblem(optf, u0, p)
-sol = solve(prob, BFGS())
+sol = solve(prob, OptimizationOptimJL.BFGS())
 ```
 
 We can inspect the `original` to see the statistics on the number of steps
@@ -157,7 +157,7 @@ We can demonstrate this via:
 using Zygote
 optf = OptimizationFunction(rosenbrock, Optimization.AutoZygote())
 prob = OptimizationProblem(optf, u0, p)
-sol = solve(prob, BFGS())
+sol = solve(prob, OptimizationOptimJL.BFGS())
 ```
 
 ## Setting Box Constraints
@@ -170,7 +170,7 @@ optimization with box constraints by rebuilding the OptimizationProblem:
 
 ```@example intro
 prob = OptimizationProblem(optf, u0, p, lb = [-1.0, -1.0], ub = [1.0, 1.0])
-sol = solve(prob, BFGS())
+sol = solve(prob, OptimizationOptimJL.BFGS())
 ```
 
 For more information on handling constraints, in particular equality and

docs/src/tutorials/remakecomposition.md

@@ -11,7 +11,7 @@ The SciML interface provides a `remake` function which allows you to recreate th
 Let's look at a 10 dimensional schwefel function in the hypercube $x_i \in [-500, 500]$.
 
 ```@example polyalg
-using OptimizationLBFGSB, Random
+using Optimization, OptimizationLBFGSB, Random
 using OptimizationBBO, ReverseDiff
 
 Random.seed!(122333)
@@ -24,7 +24,7 @@ function f_schwefel(x, p = [418.9829])
     return result
 end
 
-optf = OptimizationFunction(f_schwefel, Optimization.AutoReverseDiff(compile = true))
+optf = OptimizationFunction(f_schwefel, AutoReverseDiff(compile = true))
 
 x0 = ones(10) .* 200.0
 prob = OptimizationProblem(