Add sections to Documentation

oxfordcontrol · May 4, 2024 · 7793f63 · 7793f63
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diff --git a/docs/src/index.md b/docs/src/index.md
@@ -21,8 +21,17 @@ with decision variables ``x \in \mathbb{R}^n``, ``s \in \mathbb{R}^m`` and data
 * __Arbitrary precision types__: You can solve problems with any floating point precision.
 * __Open Source__: Our code is available on [GitHub](https://github.com/oxfordcontrol/COSMO.jl) and distributed under the Apache 2.0 Licence
 
+!!! Audience: User
+
+    COSMO has both a native interface and can also be interfaced through JuMP. If you are interested in using COSMO to solve your optimization problem, we recommend using JuMP to define your problem. Install this package as described below, take a look at our [Examples](@ref) and the [JuMP documentation](https://jump.dev/JuMP.jl/stable/) for inspiration.
+
+!!! Audience: Researcher / Developer
+
+    If you are interested in COSMO's algorithm, chordal decomposition or acceleration methods take a look at [Method](@ref), [Chordal Decomposition](@ref), and [Acceleration](@ref). If you want low-level control over the algorithm, it can make sense to start with COSMO's native interface.
+
+
 ## Installation
-COSMO can be installed using the Julia package manager for Julia `v1.0` and higher. Inside the Julia REPL, type `]` to enter the Pkg REPL mode then run
+COSMO can be installed using the Julia package manager for Julia `v1.6` and higher. Inside the Julia REPL, type `]` to enter the Pkg REPL mode then run
 
 `pkg> add COSMO`
 

diff --git a/docs/src/literate/arbitrary_precision.jl b/docs/src/literate/arbitrary_precision.jl
@@ -38,4 +38,4 @@ model = COSMO.Model()
 # - We suggest to use the pure Julia QDLDL linear system solver (`kkt_solver = QdldlKKTSolver`) when working with arbitrary precision types as some of the other available solvers don't support all available precisions.
 
 #md # !!! note
-#md #     `JuMP` does not currently support arbitrary precision. However, if you want to use `COSMO` directly with `MathOptInterface`, you can use: `COSMO.Optimizer{<: AbstractFloat}` as your optimizer. Again, the problem data precision of your MathOptInterface-model has to agree with the optimizer's precision.
+#md #    If you want to use `COSMO` directly with `MathOptInterface`, you can use: `COSMO.Optimizer{<: AbstractFloat}` as your optimizer. Again, the problem data precision of your MathOptInterface-model has to agree with the optimizer's precision.