Remove concatenation of constraints in SOSProblem.apply

1 files changed, 29 insertions, 18 deletions

diff --git a/sumofsquares/problems.py b/sumofsquares/problems.py
index 2ba9ee4..a646d0e 100644
--- a/sumofsquares/problems.py
+++ b/sumofsquares/problems.py
@@ -262,26 +262,29 @@ class SOSProblem(Problem):
                 raise NotImplementedError(f"Cannot process constraint of type {type(c)} (yet).")
 
         # Convert Expressions into PolyMatrix objects
-        # Concatenate constraints so that there is only a big constraint per cone.
         pm_constraints: list[Constraint[PolyMatrixMixin]] = []
+        for c in constraints:
+            state, pm = c.expression.apply(state)
+            pm_constraints.append(replace(c, expression=pm))
 
         # TODO: can we get rid of for loop inside InternalSOSProblem.to_conic_problem?
-        for (ctype, group) in groupby(constraints, key=type):
-            if ctype in (EqualToZero, NonNegative):
-                state, pm = poly.v_stack((c.expression for c in group)).apply(state)
-                pm_constraints.append(ctype(pm))
+        # Concatenate constraints so that there is only a big constraint per cone.
+        # for (ctype, group) in groupby(constraints, key=type):
+        #     if ctype in (EqualToZero, NonNegative):
+        #         state, pm = poly.v_stack((c.expression for c in group)).apply(state)
+        #         pm_constraints.append(ctype(pm))
 
-            elif ctype is PositiveSemiDefinite:
-                expressions = (c.expression for c in group)
-                state, pm = poly.block_diag(expressions).apply(state)
-                pm_constraints.append(ctype(pm))
+        #     elif ctype is PositiveSemiDefinite:
+        #         expressions = (c.expression for c in group)
+        #         state, pm = poly.block_diag(expressions).apply(state)
+        #         pm_constraints.append(ctype(pm))
 
-            elif ctype is ExponentialCone:
-                state, pm = poly.v_stack((c.expression for c in group)).apply(state)
-                pm_constraints.append(ctype(pm))
+        #     elif ctype is ExponentialCone:
+        #         state, pm = poly.v_stack((c.expression for c in group)).apply(state)
+        #         pm_constraints.append(ctype(pm))
 
-            else:
-                raise NotImplementedError(f"Cannot process constraint of type {ctype} (yet).")
+        #     else:
+        #         raise NotImplementedError(f"Cannot process constraint of type {ctype} (yet).")
 
         return state, InternalSOSProblem(cost, tuple(pm_constraints),
                                          tuple(variables), polynomial_variables,
@@ -386,7 +389,7 @@ class InternalSOSProblem(Problem):
 
             elif isinstance(c, ExponentialCone):
                 # Interpret row-wise
-                dims["ep"].extend(1 for _ in range(nrows))
+                dims["ep"].extend(tuple(1 for _ in range(nrows)))
                 for i in range(nrows):
                     constraints["e"].append((
                         tuple(m[i, :] for m in linear),
@@ -400,10 +403,18 @@ class InternalSOSProblem(Problem):
             raise ValueError("Optimization problem is unconstrained!")
 
         if verbose:
-            # print("Conic problem has shapes: \n"
-            #       f"\t {q.shape = }\n")
-            pass
+            logstr = ""
+            if P is not None:
+                logstr += f"{P.shape = }\n"
+
+            if q is not None:
+                logstr += f"{q.shape = }\n"
+
+            for ctype, d in dims.items():
+                if d:
+                    logstr += f" {ctype}: {sum(d)} {tuple(d)}\n"
 
+            print("Conic problem has shapes: \n", logstr)
 
         return ConicProblem(P=P, q=q, constraints=constraints,
                             dims=dims, is_qp=is_qp,