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.

References:

• G. Frison, H.H. B. Sørensen, B. Dammann, and J.B. Jørgensen. High-performance small-scale solvers for linear model predictive control. In IEEE European Control Conference, pages 128–133. IEEE, 2014 - https://ieeexplore.ieee.org/document/6981589/

• BLASFEO: Basic Linear Algebra Subroutines For Embedded Optimization G. Frison, D. Kouzoupis, T. Sartor, A. Zanelli, M. Diehl ACM Transactions on Mathematical Software (TOMS) (2018) - https://arxiv.org/abs/1704.02457

• https://github.com/giaf/blasfeo