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This is HPIPM, a high-performance interior-point method solver for dense, optimal control- and tree-structured convex quadratic programs. It provides efficient implementations of dense and structure-exploiting algorithms to solve small to medium scale problems arising in model predictive control and embedded optimization in general and it relies on the high-performance linear algebra package BLASFEO.

Getting Started:

The best way to get started with HPIPM is to check out the examples in /hpipm/examples/c/ and /hpipm/examples/python/. In order to run the C example, follow the steps below:

  1. clone BLASFEO on your machine: 'git clone https://github.com/giaf/blasfeo.git'
  2. from the BLASFEO root folder, run 'make static_library & sudo make install_static'
  3. from the HPIPM root folder, run 'make static_library & make examples'
  4. cd to /hpipm/examples/c/ and run getting_started.out to solve a simple OCP-structured QP.

If you would like to try out the Python interface, you will need to proceed as follows:

  1. clone BLASFEO on your machine: 'git clone https://github.com/giaf/blasfeo.git'
  2. from the BLASFEO root folder, run 'make shared_library & sudo make install_shared'
  3. from the HPIPM root folder, run 'make shared_library & sudo make install_shared'
  4. make sure that the location of the installed shared libraries is known to the system by running 'export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/opt/blasfeo/lib:/opt/hpipm/lib'. If you would like to avoid running this command whenever opening a new shell. You can add the commands above to your .bashrc.
  5. cd to /hpipm/interfaces/python/hpipm_python and run 'pip3 install .'
  6. cd to /hpipm/examples/python and run 'python3 getting_started.py' to solve a simple OCP-structured QP.

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  • C 90.7%
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  • Makefile 2.0%
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