Better indentation.

Author: SebKrantz

README.md

@@ -42,15 +42,15 @@ import MathOptInterface as MOI
 import MathOptSymbolicAD
 
 param[:model_attr] = Dict(:backend => (MOI.AutomaticDifferentiationBackend(), 
-                                        MathOptSymbolicAD.DefaultBackend())) 
+                                       MathOptSymbolicAD.DefaultBackend())) 
                                 # Or:  MathOptSymbolicAD.ThreadedBackend()
 ```
 
 * It is recommended to use Coin-HSL linear solvers for [Ipopt](https://github.com/jump-dev/Ipopt.jl) to speed up computations. In my opinion the simplest way to use them is to get a (free for academics) license and download the binaries [here](https://licences.stfc.ac.uk/product/coin-hsl), extract them somewhere, and then set the `hsllib` (place here the path to where you extracted `libhsl.dylib`, it may also be called `libcoinhsl.dylib`, in which case you may have to rename it to `libhsl.dylib`) and `linear_solver` options as follows:
 
 ```julia
 param[:optimizer_attr] = Dict(:hsllib => "/usr/local/lib/libhsl.dylib", # Adjust path
-                                :linear_solver => "ma57") # Use ma57, ma86 or ma97
+                              :linear_solver => "ma57") # Use ma57, ma86 or ma97
 ```
 
 The [Ipopt.jl README](https://github.com/jump-dev/Ipopt.jl?tab=readme-ov-file#linear-solvers) suggests to use the larger LibHSL package for which there exists a Julia module and proceed similarly. In addition, users may try an [optimized BLAS](https://github.com/jump-dev/Ipopt.jl?tab=readme-ov-file#blas-and-lapack) and see if it yields significant performance gains (and let me know if it does). 

docs/src/index.md

@@ -37,15 +37,15 @@ import MathOptInterface as MOI
 import MathOptSymbolicAD
 
 param[:model_attr] = Dict(:backend => (MOI.AutomaticDifferentiationBackend(), 
-                                        MathOptSymbolicAD.DefaultBackend())) 
+                                       MathOptSymbolicAD.DefaultBackend())) 
                                 # Or:  MathOptSymbolicAD.ThreadedBackend()
 ```
 
 * It is recommended to use Coin-HSL linear solvers for [Ipopt](https://github.com/jump-dev/Ipopt.jl) to speed up computations. In my opinion the simplest way to use them is to get a (free for academics) license and download the binaries [here](https://licences.stfc.ac.uk/product/coin-hsl), extract them somewhere, and then set the `hsllib` (place here the path to where you extracted `libhsl.dylib`, it may also be called `libcoinhsl.dylib`, in which case you may have to rename it to `libhsl.dylib`) and `linear_solver` options as follows:
 
 ```julia
 param[:optimizer_attr] = Dict(:hsllib => "/usr/local/lib/libhsl.dylib", # Adjust path
-                                :linear_solver => "ma57") # Use ma57, ma86 or ma97
+                              :linear_solver => "ma57") # Use ma57, ma86 or ma97
 ```
 
 The [Ipopt.jl README](https://github.com/jump-dev/Ipopt.jl?tab=readme-ov-file#linear-solvers) suggests to use the larger LibHSL package for which there exists a Julia module and proceed similarly. In addition, users may try an [optimized BLAS](https://github.com/jump-dev/Ipopt.jl?tab=readme-ov-file#blas-and-lapack) and see if it yields significant performance gains (and let me know if it does).