drop some deps

Author: baggepinnen

docs/Project.toml

@@ -12,9 +12,8 @@ ImplicitDifferentiation = "57b37032-215b-411a-8a7c-41a003a55207"
 Ipopt = "b6b21f68-93f8-5de0-b562-5493be1d77c9"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 MonteCarloMeasurements = "0987c9cc-fe09-11e8-30f0-b96dd679fdca"
-Optimization = "7f7a1694-90dd-40f0-9382-eb1efda571ba"
 OptimizationGCMAES = "6f0a0517-dbc2-4a7a-8a20-99ae7f27e911"
-OptimizationMOI = "fd9f6733-72f4-499f-8506-86b2bdd0dea1"
+OptimizationIpopt = "43fad042-7963-4b32-ab19-e2a4f9a67124"
 Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80"
 StaticArrays = "90137ffa-7385-5640-81b9-e52037218182"
 TrajectoryLimiters = "9792e600-fe43-4e4e-833b-462f466b8006"

docs/src/examples/automatic_differentiation.md

@@ -108,8 +108,8 @@ We start by defining a helper function `plot_optimized` that will evaluate the p
 The constraint function `constraints` enforces the peak of the sensitivity function to be below `Msc`. Finally, we use [Optimization.jl](https://github.com/SciML/Optimization.jl) to optimize the cost function and tell it to use ForwardDiff.jl to compute the gradient of the cost function. The optimizer we use in this example is `Ipopt`.
 
 ```@example autodiff
-using Optimization, Statistics, LinearAlgebra
-using Ipopt, OptimizationMOI; MOI = OptimizationMOI.MOI
+using Statistics, LinearAlgebra
+using OptimizationIpopt
 
 function plot_optimized(P, params, res, systems)
     fig = plot(layout=(1,3), size=(1200,400), bottommargin=2Plots.mm)
@@ -170,15 +170,18 @@ Msc = 1.3        # Constraint on Ms
 
 params  = [kp, ki, kd, 0.01] # Initial guess for parameters
 
-solver = Ipopt.Optimizer()
-MOI.set(solver, MOI.RawOptimizerAttribute("print_level"), 0)
-MOI.set(solver, MOI.RawOptimizerAttribute("max_iter"), 200)
-MOI.set(solver, MOI.RawOptimizerAttribute("acceptable_tol"), 1e-1)
-MOI.set(solver, MOI.RawOptimizerAttribute("acceptable_constr_viol_tol"), 1e-2)
-MOI.set(solver, MOI.RawOptimizerAttribute("acceptable_iter"), 5)
-MOI.set(solver, MOI.RawOptimizerAttribute("hessian_approximation"), "limited-memory")
+solver = IpoptOptimizer(;
+    acceptable_tol = 1e-1,
+    acceptable_iter = 5,
+    hessian_approximation = "limited-memory",
+    additional_options = Dict(
+        "print_level" => 0,
+        "max_iter" => 200,
+        "acceptable_constr_viol_tol" => 1e-2,
+    ),
+)
 
-fopt = OptimizationFunction(cost, Optimization.AutoForwardDiff(); cons=constraints)
+fopt = OptimizationFunction(cost, OptimizationIpopt.AutoForwardDiff(); cons=constraints)
 
 prob = OptimizationProblem(fopt, params, (P, systemspid);
     lb    = fill(-10.0, length(params)),