This is MOO: Modelica / Model Optimizer v.0.1.0, a flexible and extensible framework for solving optimization problems. MOO provides a generic Nonlinear Programming (NLP) layer with built-in scaling support and a generic NLP solver interface. While primarily designed for dynamic optimization in the Modelica ecosystem (General Dynamic Optimization Problems - GDOPs, training of Physics-enhanced Neural ODEs - PeN-ODEs), it is equally applicable to other domains, e.g. model predictive control (MPC).

Clone with submodules:

git clone --recurse-submodules [email protected]:AMIT-HSBI/MOO.git

MOO uses CMake to compile.

Install with your favorite package manager

• LAPACK

  • Debian / Ubuntu: apt install liblapack-dev
  • Latest OpenBLAS build from source: add -DUSE_SYSTEM_LAPACK=OFF -DDOWNLOAD_LAPACK=ON to the CMake configure command

• gfortran (TODO: check if Flang build works, is this necessary?)

  • Debian / Ubuntu: apt install gfortran

• METIS

  • Debian / Ubuntu apt install libmetis-dev
  • if METIS is not available: add -DMUMPS_HAS_METIS=OFF to the CMake configure command

• HSL

  • add -DIPOPT_HAS_HSL to the CMake configure command (package should be found via PkgConfig)

cmake -S . -B build -DCMAKE_INSTALL_PREFIX=install

Possible configuration arguments:

• MOO_WITH_RADAU: Build with RADAU fortran code to perform simulations (default: ON)
• MOO_WITH_GDOPT: Build with GDOPT interface (default: ON, WIP)
• MOO_WITH_C_INTERFACE: Build MOO with the generic C interface (default: ON, WIP)

cmake --build build --parallel <Nr. of cores> --target all

Add -DMOO_TESTS=ON to the CMake configure command. After building run the tests:

cmake --build build --target test

Use act to test the GitHub workflows locally:

act -P ubuntu-latest=catthehacker/ubuntu:full-latest --artifact-server-path $PWD/.artifacts

The Modelica/Model Optimizer (MOO) is distributed under the GNU Lesser General Public License (LGPL) Version 3. See the full license text for detailed terms and conditions: LGPL-3.0

The most generic problem directly implemented in MOO is a General Dynamic Optimization Problem (GDOP) with free control variables $u(t)$, free static parameters $p$, free final time $t_0$ and free final time $t_f$ of the form:

$$ \begin{aligned} \min_{u(t), p, t_0, t_f}\quad & M(x_0, x_f, u_f, p, t_0, t_f)

• \int_{t_0}^{t_f} L\bigl(x(t), u(t), p\bigr), dt \[6pt] \text{s.t.}\quad & \frac{dx}{dt} = f\bigl(x(t), u(t), p\bigr), \quad t \in [t_0, t_f], \[4pt] & g^L \le g\bigl(x(t), u(t), p\bigr) \le g^U, \quad t \in [t_0, t_f], \[4pt] & r^L \le r\bigl(x_0, x_f, u_f, p, t_0, t_f\bigr) \le r^U, \[4pt] & x^L \le x(t) \le x^U, \quad u^L \le u(t) \le u^U, \quad t \in [t_0, t_f] \ & p^L \le p \le p^U, \quad t_0^L \le t_0 \le t_0^U, \quad t_f^L \le t_f \le t_f^U. \end{aligned} $$

If the problem is given on a fixed time horizon $[t_0, t_f]$, the library can also solve problems of the form:

$$ \begin{aligned} \min_{u(t), p}\quad & M(x_0, x_f, u_f, p)

• \int_{t_0}^{t_f} L\bigl(x(t), u(t), p, t\bigr), dt \[6pt] \text{s.t.}\quad & \frac{dx}{dt} = f\bigl(x(t), u(t), p, t\bigr), \quad t \in [t_0, t_f], \[4pt] & g^L \le g\bigl(x(t), u(t), p, t\bigr) \le g^U, \quad t \in [t_0, t_f], \[4pt] & r^L \le r\bigl(x_0, x_f, u_f, p\bigr) \le r^U, \[4pt] & x^L \le x(t) \le x^U, \quad u^L \le u(t) \le u^U, \quad t \in [t_0, t_f] \ & p^L \le p \le p^U. \quad \end{aligned} $$

In this version, the user is also allowed to use the provided time variable $t$ in the functions $L, f, g$.