Update sparse matrices to sparse arrays

docs/source/mods/max-flow-min-cut.rst

@@ -129,7 +129,7 @@ An example of these inputs with their respective requirements is shown below.
           >>> G, capacities, _, _ = datasets.simple_graph_scipy()
           >>> G.data = capacities.data # Copy capacity data
           >>> G
-          <5x6 sparse matrix of type '<class 'numpy.int64'>'
+          <5x6 sparse array of type '<class 'numpy.int64'>'
                   with 7 stored elements in COOrdinate format>
           >>> print(G)
             (0, 1)    2
@@ -140,7 +140,7 @@ An example of these inputs with their respective requirements is shown below.
             (3, 5)    2
             (4, 5)    2
 
-      We only need the adjacency matrix for the graph (as sparse matrix) where
+      We only need the adjacency matrix for the graph (as a sparse array) where
       each each entry contains the capacity of the edge.
 
 |
@@ -207,7 +207,7 @@ Let us use the data to solve the maximum flow problem.
           >>> obj
           3.0
           >>> sol
-          <5x6 sparse matrix of type '<class 'numpy.float64'>'
+          <5x6 sparse array of type '<class 'numpy.float64'>'
               with 6 stored elements in COOrdinate format>
           >>> print(sol)
             (0, 1)    1.0
@@ -218,7 +218,7 @@ Let us use the data to solve the maximum flow problem.
             (4, 5)    2.0
 
       The ``max_flow`` function returns the value of the maximum flow as well a
-      sparse matrix with the amount of non-zero flow in each edge in the
+      sparse array with the amount of non-zero flow in each edge in the
       solution.
 
 The solution for this example is shown in the figure below. The edge labels

src/gurobi_optimods/max_flow.py

@@ -96,19 +96,19 @@ def _max_flow_scipy(G, source, sink, **kwargs):
     data = np.append(G.data, max_flow)
     from_arc = np.append(G.row, sink)
     to_arc = np.append(G.col, source)
-    G = sp.coo_matrix((data, (from_arc, to_arc)), dtype=float)
+    G = sp.coo_array((data, (from_arc, to_arc)), dtype=float)
 
-    capacities = sp.coo_matrix(G)
+    capacities = sp.coo_array(G)
 
     costs = np.zeros(G.row.shape, dtype=float)
     costs[-1] = -1
-    costs = sp.coo_matrix((costs, (G.row, G.col)), dtype=float)
+    costs = sp.coo_array((costs, (G.row, G.col)), dtype=float)
     demands = np.zeros(G.shape[1], dtype=float)
     # Solve
     obj, flow = min_cost_flow_scipy(G, capacities, costs, demands, **kwargs)
     G = _remove_dummy_edge(G, source, sink)
     flow = _remove_dummy_edge(flow, source, sink)
-    return -obj, sp.coo_matrix(flow)
+    return -obj, sp.coo_array(flow)
 
 
 def _max_flow_networkx(G, source, sink, **kwargs):

src/gurobi_optimods/min_cut.py

@@ -154,7 +154,7 @@ def _min_cut_scipy(G, source, sink, create_env):
 
     from_arc = np.append(G.row, sink)
     to_arc = np.append(G.col, source)
-    G = sp.coo_matrix((data, (from_arc, to_arc)), dtype=float)
+    G = sp.coo_array((data, (from_arc, to_arc)), dtype=float)
 
     capacities = data
 
@@ -170,7 +170,7 @@ def _min_cut_scipy(G, source, sink, create_env):
     ones = np.ones(from_arc.shape)
     data = np.column_stack((ones * -1.0, ones)).reshape(-1, order="C")
 
-    A = sp.csc_matrix((data, indices, indptr))
+    A = sp.csc_array((data, indices, indptr))
 
     logger.info(
         f"Solving min-cut problem with {A.shape[0]} nodes and " f"{A.shape[1]-1} edges"