Python interface for SCS 2.0.0 and higher.
To install using pip (recommended) use:
pip install scs
To install SCS from source:
git clone --recursive https://github.com/bodono/scs-python.git
cd scs-python
python setup.py install
You may need sudo
privileges for a global installation. Running SCS requires
numpy and scipy to be installed. You can install the gpu interface using
python setup.py install --scs --gpu
To test that SCS installed correctly and you have nose
installed, run
nosetests
you can also solve a random cone problem (without nose
) using
python test/solve_random_cone_prob.py
After installing the SCS interface, you import SCS using
import scs
This module provides a single function scs
with the following call signature:
sol = scs(data, cone, use_indirect=True, gpu=False, verbose=True, normalize=True, max_iters=5000, scale=1, eps=1e-5, cg_rate=2, alpha=1.5, rho_x=1e-3, acceleration_lookback=20)
The argument data
is a python dictionary with three elements A
, b
, and c
where b
and c
are 1d
NUMPY arrays and A
is a SCIPY sparse matrix in
CSC format; if they are not of the proper format, SCS will attempt to convert
them.
The argument cone
is a dictionary with fields f
, l
, q
, s
, ep
, ed
,
and p
(all of which are optional) corresponding to the supported cone types.
The returned object is a dictionary containing the keys 'x'
, 'y'
, 's'
, and
'info'
. The first three are NUMPY arrays containing the respective solution
vector. The 'info'
value is a dictionary with solver information.