Merge pull request #358 from ArnoStrouwen/fixtuts

start fixing tutorials for strict docs

Author: ChrisRackauckas

docs/Project.toml

@@ -2,6 +2,7 @@
 AmplNLWriter = "7c4d4715-977e-5154-bfe0-e096adeac482"
 Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4"
 FiniteDiff = "6a86dc24-6348-571c-b903-95158fe2bd41"
+Flux = "587475ba-b771-5e3f-ad9e-33799f191a9c"
 ForwardDiff = "f6369f11-7733-5829-9624-2563aa707210"
 Ipopt = "b6b21f68-93f8-5de0-b562-5493be1d77c9"
 Ipopt_jll = "9cc047cb-c261-5740-88fc-0cf96f7bdcc7"
@@ -15,7 +16,9 @@ OptimizationMOI = "fd9f6733-72f4-499f-8506-86b2bdd0dea1"
 OptimizationNLopt = "4e6fcdb7-1186-4e1f-a706-475e75c168bb"
 OptimizationOptimJL = "36348300-93cb-4f02-beb5-3c3902f8871e"
 OptimizationOptimisers = "42dfb2eb-d2b4-4451-abcd-913932933ac1"
+OrdinaryDiffEq = "1dea7af3-3e70-54e6-95c3-0bf5283fa5ed"
 ReverseDiff = "37e2e3b7-166d-5795-8a7a-e32c996b4267"
+SciMLSensitivity = "1ed8b502-d754-442c-8d5d-10ac956f44a1"
 Tracker = "9f7883ad-71c0-57eb-9f7f-b5c9e6d3789c"
 Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f"
 

docs/src/tutorials/minibatch.md

@@ -6,13 +6,13 @@
     [Optimisers.jl page](@ref optimisers) for details on the installation and usage.
 
 ```@example
-using Flux, Optimization, OptimizationOptimisers, OrdinaryDiffEq, DiffEqSensitivity
+using Flux, Optimization, OptimizationOptimisers, OrdinaryDiffEq, SciMLSensitivity
 
 function newtons_cooling(du, u, p, t)
     temp = u[1]
     k, temp_m = p
     du[1] = dT = -k*(temp-temp_m)
-  end
+end
 
 function true_sol(du, u, p, t)
     true_p = [log(2)/8.0, 100.0]
@@ -67,5 +67,4 @@ optfun = OptimizationFunction((θ, p, batch, time_batch) -> loss_adjoint(θ, bat
 optprob = OptimizationProblem(optfun, pp)
 using IterTools: ncycle
 res1 = Optimization.solve(optprob, Optimisers.ADAM(0.05), ncycle(train_loader, numEpochs), callback = callback)
-@test 10res1.minimum < l1
 ```

docs/src/tutorials/rosenbrock.md

@@ -53,10 +53,8 @@ sol = solve(prob, Optim.KrylovTrustRegion())
 
 # Now derivative-based optimizers with various constraints
 
-    cons = (res,x,p) -> res .= [x[1]^2 + x[2]^2]
-    optf = OptimizationFunction(rosenbrock, Optimization.AutoForwardDiff();cons= cons)
-    #prob = OptimizationProblem(optf, x0, _p)
-    #sol = solve(prob, IPNewton()) # No lcons or rcons, so constraints not satisfied
+cons = (res,x,p) -> res .= [x[1]^2 + x[2]^2]
+optf = OptimizationFunction(rosenbrock, Optimization.AutoForwardDiff();cons= cons)
 
 prob = OptimizationProblem(optf, x0, _p, lcons = [-Inf], ucons = [Inf])
 sol = solve(prob, IPNewton()) # Note that -Inf < x[1]^2 + x[2]^2 < Inf is always true
@@ -73,6 +71,14 @@ prob = OptimizationProblem(optf, x0, _p, lcons = [0.5], ucons = [0.5],
 sol = solve(prob, IPNewton()) # Notice now that x[1]^2 + x[2]^2 ≈ 0.5:
                               # cons(sol.minimizer, _p) = 0.49999999999999994
 
+function con_c(res,x,p)
+    res .= [x[1]^2 + x[2]^2]
+end
+
+optf = OptimizationFunction(rosenbrock, Optimization.AutoForwardDiff();cons= con_c)
+prob = OptimizationProblem(optf, x0, _p, lcons = [-Inf], ucons = [0.25^2])
+sol = solve(prob, IPNewton()) # -Inf < cons_circ(sol.minimizer, _p) = 0.25^2
+
 function con2_c(res,x,p)
     res .= [x[1]^2 + x[2]^2, x[2]*sin(x[1])-x[1]]
 end
@@ -81,10 +87,7 @@ optf = OptimizationFunction(rosenbrock, Optimization.AutoForwardDiff();cons= con
 prob = OptimizationProblem(optf, x0, _p, lcons = [-Inf,-Inf], ucons = [Inf,Inf])
 sol = solve(prob, IPNewton())
 
-cons_circ = (x,p) -> res .= [x[1]^2 + x[2]^2]
-optf = OptimizationFunction(rosenbrock, Optimization.AutoForwardDiff();cons= cons_circ)
-prob = OptimizationProblem(optf, x0, _p, lcons = [-Inf], ucons = [0.25^2])
-sol = solve(prob, IPNewton()) # -Inf < cons_circ(sol.minimizer, _p) = 0.25^2
+
 
 # Now let's switch over to OptimizationOptimisers with reverse-mode AD
 
@@ -107,7 +110,7 @@ prob = OptimizationProblem(optf, x0, _p)
 sol = solve(prob, Opt(:LN_BOBYQA, 2))
 sol = solve(prob, Opt(:LD_LBFGS, 2))
 
-## Add some box constarints and solve with a few NLopt.jl methods
+## Add some box constraints and solve with a few NLopt.jl methods
 
 prob = OptimizationProblem(optf, x0, _p, lb=[-1.0, -1.0], ub=[0.8, 0.8])
 sol = solve(prob, Opt(:LD_LBFGS, 2))

docs/src/tutorials/symbolic.md

@@ -26,7 +26,7 @@ for the loss function:
 
 ```@example modelingtoolkit
 loss = (a - x)^2 + b * (y - x^2)^2
-sys = OptimizationSystem(loss,[x,y],[a,b])
+@named sys = OptimizationSystem(loss,[x,y],[a,b])
 ```
 
 In order to turn it into a problem for numerical solutions, we need to specify what