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README.md

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# spock.jl
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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.
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This solver handles risk-averse optimal control problems with:
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- Linear dynamics
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- Quadratic stage and terminal costs
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- Conic risk measures
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- 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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```
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include("src/spock.jl")
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```
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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`.

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