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 # spock.jl
 
-A proximal solver for the solution of multistage risk-averse optimal control problems
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+`spock.jl` is an efficient Julia implementation of the Spock algorithm for multistage risk-averse optimal control problems. It largely benefits from warm-starts and is amenable to massive parallelization.
+
+This solver handles risk-averse optimal control problems with:
+
+  - Linear dynamics
+  - Quadratic stage and terminal costs
+  - Conic risk measures
+  - Convex input-state constraints
+
+## Installation
+
+Make sure to install the dependencies in the `Project.toml` and in your script import
+
+```
+include("src/spock.jl")
+```
+
+
+## Examples
+
+The `examples/server_heat` folder contains example code for a test system, modeling the heat of servers in a data center. The figures in the IFAC 2023 submission can be reproduced by running the scripts `residuals.jl`, `scaling.jl` and `mpc_simulation.jl`.