Remove linesearch references in IPNewton documentation

Rationale: #695 (comment)

Author: ederag

docs/src/optimization_packages/optim.md

@@ -57,15 +57,6 @@ For a more extensive documentation of all the algorithms and options, please con
 
   - [`Optim.IPNewton()`](https://julianlsolvers.github.io/Optim.jl/stable/#algo/ipnewton/)
 
-      + `linesearch` specifies the line search algorithm (for more information, consult [this source](https://github.com/JuliaNLSolvers/LineSearches.jl) and [this example](https://julianlsolvers.github.io/LineSearches.jl/latest/examples/generated/optim_linesearch.html))
-        
-          * available line search algorithms:
-          * `HaegerZhang`
-          * `MoreThuente`
-          * `BackTracking`
-          * `StrongWolfe`
-          * `Static`
-    
       + `μ0` specifies the initial barrier penalty coefficient as either a number or `:auto`
       + `show_linesearch` is an option to turn on linesearch verbosity.
       + Defaults:
@@ -87,6 +78,8 @@ prob = Optimization.OptimizationProblem(prob, x0, p, lcons = [-5.0], ucons = [10
 sol = solve(prob, IPNewton())
 ```
 
+See also in the `Optim.jl` documentation the [Nonlinear constrained optimization](https://julianlsolvers.github.io/Optim.jl/stable/#examples/generated/ipnewton_basics/) example using `IPNewton`.
+
 ### Derivative-Free
 
 Derivative-free optimizers are optimizers that can be used even in cases where no derivatives or automatic differentiation is specified. While they tend to be less efficient than derivative-based optimizers, they can be easily applied to cases where defining derivatives is difficult. Note that while these methods do not support general constraints, all support bounds constraints via `lb` and `ub` in the `Optimization.OptimizationProblem`.