fix docs for Optimization@v5

Author: SebastianM-C

docs/src/optimization_packages/optimization.md

@@ -18,15 +18,15 @@ Optimization.Sophia
 
 ```@example L-BFGS
 
-using Optimization, Zygote
+using Optimization, OptimizationLBFGS, Zygote
 
 rosenbrock(x, p) = (p[1] - x[1])^2 + p[2] * (x[2] - x[1]^2)^2
 x0 = zeros(2)
 p = [1.0, 100.0]
 
 optf = OptimizationFunction(rosenbrock, AutoZygote())
 prob = Optimization.OptimizationProblem(optf, x0, p)
-sol = solve(prob, Optimization.LBFGS())
+sol = solve(prob, LBFGS())
 ```
 
 ### With nonlinear and bounds constraints
@@ -41,7 +41,7 @@ optf = OptimizationFunction(rosenbrock, AutoZygote(), cons = con2_c)
 prob = OptimizationProblem(optf, x0, p, lcons = [1.0, -Inf],
     ucons = [1.0, 0.0], lb = [-1.0, -1.0],
     ub = [1.0, 1.0])
-res = solve(prob, Optimization.LBFGS(), maxiters = 100)
+res = solve(prob, LBFGS(), maxiters = 100)
 ```
 
 ### Train NN with Sophia

docs/src/tutorials/certification.md

@@ -7,7 +7,7 @@ This works with the `structural_analysis` keyword argument to `OptimizationProbl
 We'll use a simple example to illustrate the convexity structure certification process.
 
 ```@example symanalysis
-using SymbolicAnalysis, Zygote, LinearAlgebra, Optimization
+using SymbolicAnalysis, Zygote, LinearAlgebra, Optimization, OptimizationLBFGS
 
 function f(x, p = nothing)
     return exp(x[1]) + x[1]^2
@@ -16,7 +16,7 @@ end
 optf = OptimizationFunction(f, Optimization.AutoForwardDiff())
 prob = OptimizationProblem(optf, [0.4], structural_analysis = true)
 
-sol = solve(prob, Optimization.LBFGS(), maxiters = 1000)
+sol = solve(prob, LBFGS(), maxiters = 1000)
 ```
 
 The result can be accessed as the `analysis_results` field of the solution.

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 Optimization, Random
+using OptimizationLBFGS, Random
 using OptimizationBBO, ReverseDiff
 
 Random.seed!(122333)
@@ -47,7 +47,7 @@ This is a good start can we converge to the global optimum?
 
 ```@example polyalg
 prob = remake(prob, u0 = res1.minimizer)
-res2 = solve(prob, Optimization.LBFGS(), maxiters = 100)
+res2 = solve(prob, LBFGS(), maxiters = 100)
 
 @show res2.objective
 ```