ENH: optimize usage of tmps in matrix_representation

odl/operator/oputils.py

@@ -90,27 +90,29 @@ def matrix_representation(op):
     # Generate the matrix
     matrix = np.zeros([np.sum(n), np.sum(m)])
     tmp_ran = op.range.element()  # Store for reuse in loop
-    tmp_dom = op.domain.element()  # Store for reuse in loop
+    tmp_dom = op.domain.zero()  # Store for reuse in loop
     index = 0
+    last_i = last_j = 0
 
     for i in range(num_dom):
         for j in range(m[i]):
-            tmp_dom.set_zero()
             if op_dom_is_prod_space:
+                tmp_dom[last_i][last_j] = 0.0
                 tmp_dom[i][j] = 1.0
             else:
+                tmp_dom[last_j] = 0.0
                 tmp_dom[j] = 1.0
             op(tmp_dom, out=tmp_ran)
             if op_ran_is_prod_space:
-                tmp_result = np.empty(np.sum(n))
                 tmp_idx = 0
                 for k in range(num_ran):
-                    tmp_result[tmp_idx: tmp_idx + op.range[k].size] = \
-                        tmp_ran[k].asarray()
+                    matrix[tmp_idx: tmp_idx + op.range[k].size, index] = (
+                        tmp_ran[k])
                     tmp_idx += op.range[k].size
             else:
-                tmp_result = tmp_ran.asarray()
-            matrix[:, index] = tmp_result
+                matrix[:, index] = tmp_ran.asarray()
             index += 1
+            last_j = j
+            last_i = i
 
     return matrix