rem multistartopt from docs project fro now

Author: Vaibhavdixit02

docs/Project.toml

@@ -24,7 +24,6 @@ OptimizationGCMAES = "6f0a0517-dbc2-4a7a-8a20-99ae7f27e911"
 OptimizationMOI = "fd9f6733-72f4-499f-8506-86b2bdd0dea1"
 OptimizationManopt = "e57b7fff-7ee7-4550-b4f0-90e9476e9fb6"
 OptimizationMetaheuristics = "3aafef2f-86ae-4776-b337-85a36adf0b55"
-OptimizationMultistartOptimization = "e4316d97-8bbb-4fd3-a7d8-3851d2a72823"
 OptimizationNLPModels = "064b21be-54cf-11ef-1646-cdfee32b588f"
 OptimizationNLopt = "4e6fcdb7-1186-4e1f-a706-475e75c168bb"
 OptimizationNOMAD = "2cab0595-8222-4775-b714-9828e6a9e01b"
@@ -67,7 +66,6 @@ OptimizationGCMAES = "0.3"
 OptimizationMOI = "0.5"
 OptimizationManopt = "0.0.4"
 OptimizationMetaheuristics = "0.3"
-OptimizationMultistartOptimization = "0.3"
 OptimizationNLPModels = "0.0.2"
 OptimizationNLopt = "0.3"
 OptimizationNOMAD = "0.3"

docs/src/optimization_packages/multistartoptimization.md

@@ -31,7 +31,7 @@ constraint equations. However, lower and upper constraints set by `lb` and `ub`
 
 The Rosenbrock function can be optimized using `MultistartOptimization.TikTak()` with 100 initial points and the local method `NLopt.LD_LBFGS()` as follows:
 
-```@example MultiStart
+```julia
 using Optimization, OptimizationMultistartOptimization, OptimizationNLopt
 rosenbrock(x, p) = (p[1] - x[1])^2 + p[2] * (x[2] - x[1]^2)^2
 x0 = zeros(2)
@@ -43,7 +43,7 @@ sol = solve(prob, MultistartOptimization.TikTak(100), NLopt.LD_LBFGS())
 
 You can use any `Optimization` optimizers you like. The global method of the `MultistartOptimization` is a positional argument and followed by the local method. For example, we can perform a multistartoptimization with LBFGS as the optimizer using either the `NLopt.jl` or `Optim.jl` implementation as follows. Moreover, this interface allows you to access and adjust all the optimizer settings as you normally would:
 
-```@example MultiStart
+```julia
 using OptimizationOptimJL
 f = OptimizationFunction(rosenbrock, Optimization.AutoForwardDiff())
 prob = Optimization.OptimizationProblem(f, x0, p, lb = [-1.0, -1.0], ub = [1.0, 1.0])

docs/src/optimization_packages/optimization.md

@@ -89,4 +89,4 @@ optf = OptimizationFunction(loss, AutoZygote())
 prob = OptimizationProblem(optf, ps_ca, data)
 
 res = Optimization.solve(prob, Optimization.Sophia(), callback = callback)
-```
+```