Python interface for SCS 2.0.0 and higher.

To install using pip (recommended) use:

pip install scs

If you run into an error like this:

RuntimeError: Found /usr/lib/libcblas.dylib, but that file is a symbolic link to
the MacOS Accelerate framework, which is not supported by NumPy

you can try:

brew install openblas
OPENBLAS="$(brew --prefix openblas)" 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 pytest installed, run

pytest

After installing the SCS interface, you import SCS using

import scs

This module provides a single function solve with the following call signature:

sol = scs.solve(data,
                cone,
                use_indirect=False,
                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=10,
                write_data_filename=None)

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.

Warm-starting SCS with a guess of the primal-dual solution can reduce the total solve time. This is useful, for example, when solving several similar problems sequentially. To do this simply add fields to the data dictionary passed to scs.solve with additional fields x, y, and s (or any subset thereof) where x and s correspond to NUMPY arrays containing the primal solution guesses and y corresponds to a NUMPY array containing the dual solution guess.