1 files changed, 63 insertions, 24 deletions

diff --git a/sumofsquares/cvxopt.py b/sumofsquares/cvxopt.py
index cc7d555..a703104 100644
--- a/sumofsquares/cvxopt.py
+++ b/sumofsquares/cvxopt.py
@@ -32,17 +32,43 @@ def solve_cone(
         state, 
         variables, 
         cost, 
-        s_inequality,
-        # q_inequality = tuple(), 
+        s_inequality = tuple(),
+        q_inequality = tuple(), 
         l_inequality = tuple(), 
-        previous_result=None, 
+        # previous_result=None, 
         print_info=False, 
         print_matrix=False,
     ):
-    state, matrix_repr = polymatrix.to_matrix_repr(
-        (cost,) + l_inequality + s_inequality,
-        polymatrix.v_stack(variables),
-    ).apply(state)
+
+    try:    
+        state, matrix_repr = polymatrix.to_matrix_repr(
+            (cost,) + l_inequality + q_inequality + s_inequality,
+            polymatrix.v_stack(variables),
+        ).apply(state)
+    except:
+        state, matrix_repr = polymatrix.to_matrix_repr(
+            (cost,),
+            polymatrix.v_stack(variables),
+        ).apply(state)
+    
+        if len(l_inequality):
+            state, matrix_repr = polymatrix.to_matrix_repr(
+                l_inequality,
+                polymatrix.v_stack(variables),
+            ).apply(state)
+    
+        if len(q_inequality):
+            state, matrix_repr = polymatrix.to_matrix_repr(
+                q_inequality,
+                polymatrix.v_stack(variables),
+            ).apply(state)
+
+        if len(s_inequality):
+            state, matrix_repr = polymatrix.to_matrix_repr(
+                s_inequality,
+                polymatrix.v_stack(variables),
+            ).apply(state)
+    
     
     # maximum degree of cost function must be 2
     max_degree_cost = matrix_repr.data[0].get_max_degree()
@@ -58,9 +84,12 @@ def solve_cone(
     q = cost[1] / 2
         
     Gl = matrix_repr.data[1:1+len(l_inequality)]     
-    l = sum(G[0].shape[0] for G in Gl)
+    dim_l = sum(G[0].shape[0] for G in Gl)
+
+    Gq = matrix_repr.data[1+len(l_inequality):1+len(l_inequality)+len(q_inequality)]     
+    dim_q = list(G[0].shape[0] for G in Gq)
 
-    Gs = matrix_repr.data[1+len(l_inequality):]  
+    Gs = matrix_repr.data[1+len(l_inequality)+len(q_inequality):]  
     
     def gen_square_matrix_dim():
         for Gs_raw in Gs:
@@ -70,52 +99,62 @@ def solve_cone(
             
             yield int(dim)
     
-    s = list(gen_square_matrix_dim())
+    dim_s = list(gen_square_matrix_dim())
     
     G = np.vstack(tuple(-v[1] for v in matrix_repr.data[1:]))
     h = np.vstack(tuple(v[0] for v in matrix_repr.data[1:]))
 
     if print_info:
         print(f'number of variables: {G.shape[1]}')
-        print(f'PSD matrix dimensions: {s}')
+        print(f'{dim_l=}, {dim_q=}, {dim_s=}')
 
     if print_matrix:
         print(f'cost={q}')
         print(f'G={G}')
         print(f'h={h}')
     
-    if previous_result is None:
-        primalstart = None
+    # if previous_result is None:
+    #     primalstart = None
         
-    else:
-        primalstart = {
-            'x': cvxopt.matrix(previous_result.x),
-            'y': cvxopt.matrix(previous_result.y),
-            's': cvxopt.matrix(previous_result.s),
-            'z': cvxopt.matrix(previous_result.z),
-        }
+    # else:
+    #     primalstart = {
+    #         'x': cvxopt.matrix(previous_result.x),
+    #         'y': cvxopt.matrix(previous_result.y),
+    #         's': cvxopt.matrix(previous_result.s),
+    #         'z': cvxopt.matrix(previous_result.z),
+    #     }
 
     if max_degree_cost == 1:
         return_val = cvxopt.solvers.conelp(
             c=cvxopt.matrix(q.reshape(-1, 1)),
             G=cvxopt.matrix(G), 
             h=cvxopt.matrix(h),
-            dims={'l': 0, 'q': [], 's': s},
-            primalstart=primalstart,
+            dims={'l': dim_l, 'q': dim_q, 's': dim_s},
+            # primalstart=primalstart,
         )
         
     else:
         # cost[2] is a 1 x n*n vector
         nP = int(np.sqrt(cost[2].shape[1]))
         P = cost[2].reshape(nP, nP).toarray()
+
+        if print_matrix:
+            print(f'{P=}')
+
+        if print_matrix:
+            print(f'{dim_l=}, {dim_q=}, {dim_s=}')
+            print(f'{P=}')
+            print(f'{q=}')
+            print(f'{G=}')
+            print(f'{h=}')
         
         return_val = cvxopt.solvers.coneqp(
             P=cvxopt.matrix(P), 
             q=cvxopt.matrix(q.reshape(-1, 1)),
             G=cvxopt.matrix(G), 
             h=cvxopt.matrix(h),
-            dims={'l': l, 'q': [], 's': s,},
-            initvals=primalstart,
+            dims={'l': dim_l, 'q': dim_q, 's': dim_s},
+            # initvals=primalstart,
         )
         
     x = np.array(return_val['x']).reshape(-1)