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giaf
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Description

High-performance interior-point-method QP solvers

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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.

HPIPM (and BLASFEO, which is a dependency), comes with both

make
and
cmake
build systems. The preferred one is
make
, which can be used to compile and run any library, interface and example in any language.
make
is also used in the continuous integration
travis
scripts.
cmake
can only be used to compile the libraries, while the interested user should compile interfaces and run examples by him/herself by taking inspiration form the commands in the various
Makefile
s.

Getting Started:

The best way to get started with HPIPM is to check out the examples in

/hpipm/examples/
. HPIPM can be directly used from
C
, but there are also interfaces to Python and Matlab. Depending on which level you want to use HPIPM, check out the following section below. The QP notation used in HPIPM can be found in the
doc
folder.

C

In order to run the C examples in

/hpipm/examples/C/
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
(default installation folder:
/opt/blasfeo
; a different one is chose,
BLASFEO_PATH
in HPIPM's
Makefile.rule
should be updated accordingly) 3) From the HPIPM root folder, run
make static_library && make examples
4) In a terminal, navigate to /hpipm/examples/c/ and run getting_started.out to solve a simple OCP-structured QP.

MATLAB and Octave

Linux

The interface for Matlab and Octave is based on mex files. 1) Clone BLASFEO on your machine:

git clone https://github.com/giaf/blasfeo.git
2) From the BLASFEO root folder, run
make shared_library -j 4 && sudo make install_shared
3) From the HPIPM root folder, run
make shared_library -j 4 && sudo make install_shared
4) In a terminal, navigate to the folder
hpipm/interfaces/matlab_octave
. Set the needed environment flags by running
source env.sh
(you may need to change the
BLASFEO_MAIN_FOLDER
, or to make it equal to the
BLASFEO_PATH
) in that folder. Compile the interface by running
make all -j 4
(for Octave), or
make compile_mex_with_matlab
(for Matlab). 5) In a terminal, navigate to the folder
hpipm/examples/matlab_octave
. Set the needed environment flags by running
source env.sh
(you may need to change the
BLASFEO_MAIN_FOLDER
, or to make it equal to the
BLASFEO_PATH
) in that folder. Run an instance of Matlab or Octave from the same terminal. Get started by running the examples in that folder.

MATLAB on Windows

The interface for Matlab and Octave is based on mex files. 1) Clone BLASFEO on your machine:

git clone https://github.com/giaf/blasfeo.git
2) Install Microsoft Visual C++ 3) From the BLASFEO root folder, run
mkdir build
cd build
cmake ..
cmake --build .
Copy
blasfeo.lib
from
build/Debug/
to
lib/
.

4) From the HPIPM root folder, run

mkdir build
cd build
cmake ..
cmake --build .
Copy
hpipm.lib
from
build/Debug/
to
lib/
.

5) Open Matlab and navigate to the folder

hpipm/interfaces/matlab_octave
. Set the needed environment flags by running
env.m
(you may need to change the
BLASFEO_MAIN_FOLDER
, or to make it equal to the
BLASFEO_PATH
) in that folder. Compile the interface by running
compile_mex_all.m
. 6) In Matlab, navigate to the folder
hpipm/examples/matlab_octave
. Get started by running the examples in that folder. You may need to add folder
hpipm/interfaces/matlab_octave
to the Matlab path.

Simulink

The QP model is read from the file

qp_data.c
, which can be generated using the C, matlab/octave or python interfaces. 1) Follow the steps 1)-4) for the MATLAB interface. 2) In a terminal, navigate to the folder
hpipm/examples/simulink
. Run
make_sfun.m
to compile the S-function, and
load_paramaters.m
to load some parameters used in the simulink model (e.g. horizon length, number of inputs and states) form
qp_data.c
. 3) Open the simulink model
hpipm_simulink_getting_started.slx
and start the simulation.

Python

If you would like to try out the Python interface, check out the examples in

/hpipm/examples/python/
after going through the following steps: 1) Clone BLASFEO on your machine:
git clone https://github.com/giaf/blasfeo.git
2) From the BLASFEO root folder, run
make shared_library -j4 && sudo make install_shared
3) From the HPIPM root folder, run
make shared_library -j4 && sudo make install_shared
4) In a terminal, navigate to
/hpipm/interfaces/python/hpipm_python
and run
pip install
or
pip3 install
(depending on your python version). 5) In a terminal, navigate to
/hpipm/examples/python
. Set the needed environment flags by running
source env.sh
(you may need to change the
BLASFEO_MAIN_FOLDER
, or to make it equal to the
BLASFEO_PATH
) in that folder. Alternatively you can make sure yourself that the location of the installed shared libraries is known to the system e.g. by running
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/opt/blasfeo/lib:/opt/hpipm/lib
(possibly after updating it to the chosen installation directories). Finally, run
python example_qp_getting_started.py
or
python3 example_qp_getting_started.py
(depending on your python version) to solve a simple OCP-structured QP.

References:


Notes:

  • HPIPM relies on the high-performance linear algebra library BLASFEO. BLASFEO provides several implementations optimized for different computer architectures, and it makes heavy use of assembly code. If you get the error
    Illegal instruciton
    at running time, you are probably using a BLASFEO version (
    TARGET
    ) unsupported by your CPU.

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