a few tweaks

Author: ChrisRackauckas

docs/src/examples/kepler_problem.md

@@ -112,7 +112,7 @@ analysis_plot2(sol_, H, L)
 There is a significant fluctuation in the first integrals, when there is no mainfold projection.
 
 ```@example kepler
-function first_integrals_manifold(residual,u)
+function first_integrals_manifold(residual,u,p,t)
     residual[1:2] .= initial_first_integrals[1] - H(u[1:2], u[3:4])
     residual[3:4] .= initial_first_integrals[2] - L(u[1:2], u[3:4])
 end
@@ -125,7 +125,7 @@ analysis_plot2(sol5, H, L)
 We can see that thanks to the manifold projection, the first integrals' variation is very small, although we are using `RK4` which is not symplectic. But wait, what if we only project to the energy conservation manifold?
 
 ```@example kepler
-function energy_manifold(residual,u)
+function energy_manifold(residual,u,p,t)
     residual[1:2] .= initial_first_integrals[1] - H(u[1:2], u[3:4])
     residual[3:4] .= 0
 end

docs/src/examples/min_and_max.md

@@ -59,7 +59,7 @@ function is `f`:
 
 ```@example minmax
 using Optimization, OptimizationNLopt, ForwardDiff
-optf = OptimizationFunction(f, AutoForwardDiff())
+optf = OptimizationFunction(f, Optimization.AutoForwardDiff())
 min_guess = 18.0
 optprob = OptimizationProblem(optf, min_guess)
 opt = solve(optprob, NLopt.LD_LBFGS())
@@ -75,7 +75,7 @@ println(opt.u)
 To get the maximum, we just minimize the negative of the function:
 
 ```@example minmax
-optf = OptimizationFunction(f, AutoForwardDiff())
+optf = OptimizationFunction(f, Optimization.AutoForwardDiff())
 min_guess = 22.0
 optprob2 = OptimizationProblem(optf, min_guess)
 opt2 = solve(optprob2, NLopt.LD_LBFGS())