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# spock.jl | ||
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A proximal solver for the solution of multistage risk-averse optimal control problems | ||
`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. | ||
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This solver handles risk-averse optimal control problems with: | ||
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- Linear dynamics | ||
- Quadratic stage and terminal costs | ||
- Conic risk measures | ||
- Convex input-state constraints | ||
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## Installation | ||
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Make sure to install the dependencies in the `Project.toml` and in your script import | ||
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``` | ||
include("src/spock.jl") | ||
``` | ||
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## Examples | ||
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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`. |