SCS can directly be requested as a solver

peterwittek · Nov 16, 2016 · 37dee14 · 37dee14
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Showing 4 changed files with 41 additions and 15 deletions.
diff --git a/README.rst b/README.rst
@@ -35,7 +35,7 @@ Optional dependencies include:
 - `SciPy <http://scipy.org/>`_ yields faster execution with the default CPython interpreter.
 - `PICOS <http://picos.zib.de/>`_ is necessary for using the Cvxopt solver and for converting the problem to a PICOS instance.
 - `MOSEK <http://www.mosek.com/>`_ Python module is necessary to work with the MOSEK solver.
-- `CVXPY <http://cvxpy.org/>`_ is required for converting the problem to or by solving it by CVXPY.
+- `CVXPY <http://cvxpy.org/>`_ is required for converting the problem to or by solving it by CVXPY or by SCS.
 - `Cvxopt <http://cvxopt.org/>`_ is required by both Chompack and PICOS.
 - `Chompack <http://chompack.readthedocs.io/en/latest/>`_ improves the sparsity of the chordal graph extension.
 

diff --git a/doc/source/revision_history.rst b/doc/source/revision_history.rst
@@ -3,7 +3,7 @@ Revision History
 Since 1.11.1:
   - New: Pass the optional `momentsubstitutions=` parameter to the `get_relaxation` method to substitute out specific moments.
   - Changed: Warning message is displayed if the equality constraints are linearly dependent.
-  - Changed: CVXPY support improved, solver parameters passed on correctly.
+  - Changed: CVXPY support improved, solver parameters passed on correctly. SCS can directly be requested as a solver.
   - Fixed: Chordal graph extension works with blank objective functions and commuting variables.
   - Fixed: Parallel computations produce weird deadlocks less frequently.
 

diff --git a/ncpol2sdpa/sdp_relaxation.py b/ncpol2sdpa/sdp_relaxation.py
@@ -74,8 +74,8 @@ def solve(self, solver=None, solverparameters=None):
         :param sdpRelaxation: The SDP relaxation to be solved.
         :type sdpRelaxation: :class:`ncpol2sdpa.SdpRelaxation`.
         :param solver: The solver to be called, either `None`, "sdpa", "mosek",
-                       or "cvxopt". The default is `None`, which triggers
-                       autodetect.
+                       "cvxpy", "scs", or "cvxopt". The default is `None`,
+                       which triggers autodetect.
         :type solver: str.
         :param solverparameters: Parameters to be passed to the solver. Actual
                                  options depend on the solver:
@@ -95,6 +95,15 @@ def solve(self, solver=None, solverparameters=None):
                                  Cvxopt:
                                  Refer to the PICOS documentation. All
                                  arguments are passed on.
+
+                                 Cvxpy:
+                                 Refer to the Cvxpy documentation. All
+                                 arguments are passed on.
+
+                                 SCS:
+                                 Refer to the Cvxpy documentation. All
+                                 arguments are passed on.
+
         :type solverparameters: dict of str.
         """
         if self.F is None:

diff --git a/ncpol2sdpa/solver_common.py b/ncpol2sdpa/solver_common.py
@@ -27,6 +27,7 @@ def autodetect_solvers(solverparameters):
         pass
     else:
         solvers.append("cvxpy")
+        solvers.append("scs")
     try:
         import mosek
     except ImportError:
@@ -45,13 +46,13 @@ def autodetect_solvers(solverparameters):
 def solve_sdp(sdp, solver=None, solverparameters=None):
     """Call a solver on the SDP relaxation. Upon successful solution, it
     returns the primal and dual objective values along with the solution
-    matrices. It also sets these values in the `sdp` object, along
-    with some status information.
+    matrices.
 
-    :param sdp: The SDP relaxation to be solved.
-    :type sdp: :class:`ncpol2sdpa.sdp`.
-    :param solver: The solver to be called, either `None`, "sdpa", "mosek", or
-                   "cvxopt". The default is `None`, which triggers autodetect.
+    :param sdpRelaxation: The SDP relaxation to be solved.
+    :type sdpRelaxation: :class:`ncpol2sdpa.SdpRelaxation`.
+    :param solver: The solver to be called, either `None`, "sdpa", "mosek",
+                   "cvxpy", "scs", or "cvxopt". The default is `None`,
+                   which triggers autodetect.
     :type solver: str.
     :param solverparameters: Parameters to be passed to the solver. Actual
                              options depend on the solver:
@@ -62,15 +63,23 @@ def solve_sdp(sdp, solver=None, solverparameters=None):
                                  Specify the executable for SDPA. E.g.,
                                  `"executable":"/usr/local/bin/sdpa"`, or
                                  `"executable":"sdpa_gmp"`
-                               - `"paramsfile"`: Specify the parameter file.
+                               - `"paramsfile"`: Specify the parameter file
 
                              Mosek:
-                             Refer to the Mosek documentation. All arguments
-                             are passed on.
+                             Refer to the Mosek documentation. All
+                             arguments are passed on.
 
                              Cvxopt:
-                             Refer to the PICOS documentation. All arguments
-                             are passed on.
+                             Refer to the PICOS documentation. All
+                             arguments are passed on.
+
+                             Cvxpy:
+                             Refer to the Cvxpy documentation. All
+                             arguments are passed on.
+
+                             SCS:
+                             Refer to the Cvxpy documentation. All
+                             arguments are passed on.
     :type solverparameters: dict of str.
     :returns: tuple of the primal and dual optimum, and the solutions for the
               primal and dual.
@@ -102,6 +111,14 @@ def solve_sdp(sdp, solver=None, solverparameters=None):
     elif solver == "cvxpy":
         primal, dual, x_mat, y_mat, status = \
           solve_with_cvxpy(sdp, solverparameters)
+    elif solver == "scs":
+        if solverparameters is None:
+            solverparameters_ = {"solver": "SCS"}
+        else:
+            solverparameters_ = solverparameters.copy()
+            solverparameters_["solver"] = "SCS"
+        primal, dual, x_mat, y_mat, status = \
+          solve_with_cvxpy(sdp, solverparameters_)
     elif solver == "mosek":
         primal, dual, x_mat, y_mat, status = \
           solve_with_mosek(sdp, solverparameters)