Merge branch 'master' of https://github.com/giaf/hpipm

README.md

@@ -3,23 +3,42 @@ It provides efficient implementations of dense and structure-exploiting algorith
 
 --------------------------------------------------
 
-Getting Started: 
+## 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.
 
-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'
+### 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`
+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=$LD_LIBRARY_PATH:/opt/blasfeo/lib:/opt/hpipm/lib'. If you would like 
+### Python
+If you would like to try out the Python interface, check out the examples in `/hpipm/examples/python/` after going through the following steps:
+1) clone BLASFEO on your machine: `git clone https://github.com/giaf/blasfeo.git`
+2) from the BLASFEO root folder, run `make shared_library -j4 & sudo make install_shared`
+3) from the HPIPM root folder, run `make shared_library -j4 & sudo make install_shared`
+4) make sure that the location of the installed shared libraries is known to the system by running `export LD_LIBRARY_PATH=$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.
+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.
+
+
+### MATLAB
+Since the HPIPM MATLAB interface is based on the HPIPM Python interface, please first follow the steps in the previous paragraph, "Python".
+In order to be able to run HPIPM from Matlab the following steps are additionally required:
+1) Make sure that the location of the shared libraries `libblasfeo.so` and `libhpipm.so` is known to the system, see step 4) for Python.
+2) Check the paths are defined in the terminal using: `echo $LD_LIBRARY_PATH`
+3) Start Matlab from a terminal in which the `LD_LIBRARY_PATH` is defined correctly
+4) Make sure that hpipm_python has been installed for the Python version used by MATLAB.
+You can check which version is used by running the Matlab command `py.sys.path`.
+Use the Matlab command `pyversion <path_to_the_python_interpreter>` to change the Python interpreter used by Matlab.
+5) Add `hpipm/interfaces/matlab/hpipm_matlab` to your MATLAB path.
+6) Get started using HPIPM from Matlab by looking at the examples in `/hpipm/examples/matlab/`.
+
 
 --------------------------------------------------
 

examples/matlab/getting_started.m

@@ -1,7 +1,7 @@
 % pyversion /usr/bin/python3
 % py.sys.path
 
-clear all
+clearvars
 close all
 clc
 
@@ -44,8 +44,6 @@
 
 Jx = [1, 0; 0, 1];
 x0 = [1; 1];
-Jsx = [1, 0; 0, 1];
-Zl = [1e5, 0; 0, 1e5];
 Zu = [1e5, 0; 0, 1e5];
 zl = [1e5; 1e5];
 zu = [1e5; 1e5];

interfaces/python/hpipm_python/hpipm_python/wrapper/hpipm_ocp_qp.py

@@ -167,11 +167,19 @@ def __init__(self, dim):
 
 
 	def set(self, field, value, idx=None):
+		# cast to np array
 		if type(value) is not np.ndarray:
-			if type(value) is int:
+			if (type(value) is int) or (type(value) is float):
 				value_ = value
 				value = np.array((1,))
 				value[0] = value_
+
+		# reshape np arrays with empty shape, that occur when using hpipm_matlab
+		if type(value) is np.ndarray and value.shape == ():
+			value_ = value
+			value = np.array((1,))
+			value[0] = value_
+
 		# non-native setters (not implemented as C APIs)
 		setter_map = {
 			"Jx" : self.set_Jx,