Implement max flow and min cut

docs/source/api.rst

@@ -4,9 +4,15 @@ API Reference
 .. automodule:: gurobi_optimods.bipartite_matching
    :members: maximum_bipartite_matching
 
+.. automodule:: gurobi_optimods.max_flow
+   :members: max_flow
+
 .. automodule:: gurobi_optimods.min_cost_flow
    :members: min_cost_flow_pandas, min_cost_flow_networkx, min_cost_flow_scipy
 
+.. automodule:: gurobi_optimods.min_cut
+   :members: min_cut, MinCutResult
+
 .. automodule:: gurobi_optimods.mwis
    :members: maximum_weighted_independent_set
 

docs/source/gallery.rst

@@ -18,6 +18,12 @@ The OptiMods Gallery
         :text-align: center
         :img-top: mods/icons/lad-regression.png
 
+    .. grid-item-card:: Maximum-Flow/Mininum-Cut
+        :link: mods/max-flow-min-cut
+        :link-type: doc
+        :text-align: center
+        :img-top: mods/figures/max-flow-min-cut.png
+
     .. grid-item-card:: Minimum-Cost Flow
         :link: mods/min-cost-flow
         :link-type: doc
@@ -68,6 +74,7 @@ The OptiMods Gallery
    mods/bipartite-matching
    mods/lad-regression
    mods/min-cost-flow
+   mods/max-flow-min-cut
    mods/mwis
    mods/opf/opf
    mods/portfolio

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

@@ -0,0 +1,312 @@
+Maximum Flow / Minimum Cut
+==========================
+
+The maximum flow problem finds the total flow that can be routed through a
+capacitated network from a given source vertex to a given sink vertex. The
+minimum cut problem finds a set of edges in the same graph with minimal combined
+capacity that, when removed, would disconnect the source from the sink. The two
+problems are related through duality: the maximum flow is equal to the capacity
+of the minimum cut.
+
+The first algorithm proposed to solve this problem was the Ford-Fulkerson
+algorithm :footcite:p:`ford1956maximal`. :footcite:t:`goldberg1988new` later
+proposed the famous push-relabel algorithm, and more recently,
+:footcite:t:`orlin2013max` and other authors have proposed polynomial-time
+algorithms. The maximum flow problem is a special case of the minimum cost flow
+problem, so it can also be solved efficiently using the network simplex
+algorithm :footcite:p:`caliskan2012faster` making a linear programming (LP)
+solver a suitable approach.
+
+Problem Specification
+---------------------
+
+Let :math:`G` be a graph with set of vertices :math:`V` and edges :math:`E`.
+Each edge :math:`(i,j)\in E` has capacity :math:`B_{ij}\in\mathbb{R}`. Given a
+source vertex :math:`s\in V` and a sink vertex :math:`t\in V` the two problems
+can be stated as follows:
+
+- Maximum Flow: Find the flow with maximum value from source to sink such that
+  the flow is capacity feasible.
+- Minimum Cut: Find the set of edges which disconnects the source and sink such
+  that the total capacity of the cut is minimised.
+
+.. dropdown:: Background: Optimization Model
+
+    Let us define a set of continuous variables :math:`x_{ij}` to represent
+    the amount of non-negative (:math:`\geq 0`) flow going through an edge
+    :math:`(i,j) \in E`.
+
+    The mathematical formulation for the maximum flow can be stated as:
+
+    .. math::
+
+        \begin{alignat}{2}
+          \max \quad        & \sum_{j \in \delta^+(s)} x_{sj} \\
+          \mbox{s.t.} \quad & \sum_{j \in \delta^-(i)} x_{ji} = \sum_{j \in \delta^+(i)} x_{ij} & \quad\forall i \in V\setminus\{s,t\} \\
+                            & 0 \leq x_{ij} \le B_{ij} & \forall (i, j) \in E \\
+        \end{alignat}
+
+    where :math:`\delta^+(\cdot)` (:math:`\delta^-(\cdot)`) denotes the
+    outgoing (incoming) neighbours of a given vertex.
+
+    The objective maximises the total outgoing flow from the source vertex. The
+    first set of constraint ensure flow balance for all vertices. That is, for a
+    given node that is neither the source or the sink, the outgoing flow must be
+    equal to the incoming flow. The last set of constraints ensure
+    non-negativity of the variables and that the capacity of each edge is not
+    exceeded.
+
+    The minimum cut problem formulation is the dual of the maximum flow
+    formulation. The minimum cut is found by solving the maximum flow problem
+    and extracting the constraint dual values.
+
+    Particularly, we can get the edges in the cutset by checking the non-zero
+    dual values of the capacity constraints (the last constraints), with these,
+    we can find the partions by checking the predecessors and successors
+    vertices of these edges.
+
+|
+
+Code and Inputs
+---------------
+
+This Mod accepts input graphs of any of the following types:
+
+* pandas: using a ``pd.DataFrame``;
+* NetworkX: using a ``nx.DiGraph`` or ``nx.Graph``;
+* SciPy.sparse: using a ``sp.sparray`` matrix.
+
+An example of these inputs with their respective requirements is shown below.
+
+.. tabs::
+
+  .. group-tab:: pandas
+
+      .. doctest:: pandas
+          :options: +NORMALIZE_WHITESPACE
+
+          >>> from gurobi_optimods import datasets
+          >>> edge_data, _ = datasets.simple_graph_pandas()
+          >>> edge_data[["capacity"]]
+                         capacity
+          source target
+          0      1              2
+                 2              2
+          1      3              1
+          2      3              1
+                 4              2
+          3      5              2
+          4      5              2
+
+      The ``edge_data`` DataFrame is indexed by ``source`` and ``target`` nodes
+      and contains columns labelled ``capacity`` with the edge attributes.
+
+  .. group-tab:: NetworkX
+
+      .. doctest:: networkx
+          :options: +NORMALIZE_WHITESPACE
+
+          >>> from gurobi_optimods import datasets
+          >>> G = datasets.simple_graph_networkx()
+          >>> for u, v, capacity in G.edges.data("capacity"):
+          ...     print(f"{u} -> {v}: {capacity = }")
+          0 -> 1: capacity = 2
+          0 -> 2: capacity = 2
+          1 -> 3: capacity = 1
+          2 -> 3: capacity = 1
+          2 -> 4: capacity = 2
+          3 -> 5: capacity = 2
+          4 -> 5: capacity = 2
+
+      Edges have attributes ``capacity``.
+
+  .. group-tab:: scipy.sparse
+
+      .. doctest:: scipy
+          :options: +NORMALIZE_WHITESPACE
+
+          >>> from gurobi_optimods import datasets
+          >>> G, capacities, _, _ = datasets.simple_graph_scipy()
+          >>> G.data = capacities.data # Copy capacity data
+          >>> G
+          <5x6 sparse matrix of type '<class 'numpy.int64'>'
+                  with 7 stored elements in COOrdinate format>
+          >>> print(G)
+            (0, 1)    2
+            (0, 2)    2
+            (1, 3)    1
+            (2, 3)    1
+            (2, 4)    2
+            (3, 5)    2
+            (4, 5)    2
+
+      We only need the adjacency matrix for the graph (as sparse matrix) where
+      each each entry contains the capacity of the edge.
+
+|
+
+Solution
+--------
+
+Maximum Flow
+^^^^^^^^^^^^
+
+Let us use the data to solve the maximum flow problem.
+
+.. tabs::
+
+  .. group-tab:: pandas
+
+      .. doctest:: pandas
+          :options: +NORMALIZE_WHITESPACE
+
+          >>> from gurobi_optimods.max_flow import max_flow
+          >>> obj, flow = max_flow(edge_data, 0, 5, verbose=False) # Find max-flow between nodes 0 and 5
+          >>> obj
+          3.0
+          >>> flow
+          source  target
+          0       1         1.0
+                  2         2.0
+          1       3         1.0
+          2       3         1.0
+                  4         1.0
+          3       5         2.0
+          4       5         1.0
+          Name: flow, dtype: float64
+
+      The ``max_flow`` function returns the cost of the solution as well
+      as ``pd.Series`` with the flow per edge. Similarly as the input
+      DataFrame the resulting series is indexed by ``source`` and ``target``.
+      In this case, the resulting maximum flow has value 3.
+
+  .. group-tab:: NetworkX
+
+      .. doctest:: networkx
+          :options: +NORMALIZE_WHITESPACE
+
+          >>> from gurobi_optimods.max_flow import max_flow
+          >>> obj, sol = max_flow(G, 0, 5, verbose=False)
+          >>> obj
+          3.0
+          >>> type(sol)
+          <class 'networkx.classes.digraph.DiGraph'>
+          >>> list(sol.edges(data=True))
+          [(0, 1, {'flow': 1.0}), (0, 2, {'flow': 2.0}), (1, 3, {'flow': 1.0}), (2, 3, {'flow': 1.0}), (2, 4, {'flow': 1.0}), (3, 5, {'flow': 2.0}), (4, 5, {'flow': 1.0})]
+
+      The ``max_flow`` function returns the cost of the solution
+      as well as a dictionary indexed by edge with the non-zero flow.
+
+  .. group-tab:: scipy.sparse
+
+      .. doctest:: scipy
+          :options: +NORMALIZE_WHITESPACE
+
+          >>> from gurobi_optimods.max_flow import max_flow
+          >>> obj, sol = max_flow(G, 0, 5, verbose=False)
+          >>> obj
+          3.0
+          >>> sol
+          <5x6 sparse matrix of type '<class 'numpy.float64'>'
+              with 6 stored elements in COOrdinate format>
+          >>> print(sol)
+            (0, 1)    1.0
+            (0, 2)    2.0
+            (1, 3)    1.0
+            (2, 4)    2.0
+            (3, 5)    1.0
+            (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
+      solution.
+
+The solution for this example is shown in the figure below. The edge labels
+denote the edge capacity and resulting flow: :math:`x^*_{ij}/B_{ij}`. All
+edges in the maximum flow solution carry some flow, totalling at 3.0 at the
+sink.
+
+.. image:: figures/max-flow.png
+  :width: 600
+  :alt: Maximum Flow Solution.
+  :align: center
+
+Minimum Cut
+^^^^^^^^^^^
+
+Let us use the data to solve the minimum cut problem.
+
+.. tabs::
+
+  .. group-tab:: pandas
+
+      .. doctest:: pandas
+          :options: +NORMALIZE_WHITESPACE
+
+          >>> from gurobi_optimods.min_cut import min_cut
+          >>> res = min_cut(edge_data, 0, 5, verbose=False)
+          >>> res
+          MinCutResult(cut_value=3.0, partition=({0, 1}, {2, 3, 4, 5}), cutset={(0, 2), (1, 3)})
+          >>> res.cut_value
+          3.0
+          >>> res.partition
+          ({0, 1}, {2, 3, 4, 5})
+          >>> res.cutset
+          {(0, 2), (1, 3)}
+
+      The ``min_cut`` function returns a ``MinCutResult`` which contains the
+      cutset value, the partition of the nodes and the edges in the cutset.
+
+
+  .. group-tab:: NetworkX
+
+      .. doctest:: networkx
+          :options: +NORMALIZE_WHITESPACE
+
+          >>> from gurobi_optimods.min_cut import min_cut
+          >>> res = min_cut(G, 0, 5, verbose=False)
+          >>> res
+          MinCutResult(cut_value=3.0, partition=({0, 1}, {2, 3, 4, 5}), cutset={(0, 2), (1, 3)})
+          >>> res.cut_value
+          3.0
+          >>> res.partition
+          ({0, 1}, {2, 3, 4, 5})
+          >>> res.cutset
+          {(0, 2), (1, 3)}
+
+      The ``min_cut`` function returns a ``MinCutResult`` which contains the
+      cutset value, the partition of the nodes and the edges in the cutset.
+
+  .. group-tab:: scipy.sparse
+
+      .. doctest:: scipy
+          :options: +NORMALIZE_WHITESPACE
+
+          >>> from gurobi_optimods.min_cut import min_cut
+          >>> res = min_cut(G, 0, 5, verbose=False)
+          >>> res
+          MinCutResult(cut_value=3.0, partition=({0, 1}, {2, 3, 4, 5}), cutset={(0, 2), (1, 3)})
+          >>> res.cut_value
+          3.0
+          >>> res.partition
+          ({0, 1}, {2, 3, 4, 5})
+          >>> res.cutset
+          {(0, 2), (1, 3)}
+
+      The ``min_cut`` function returns a ``MinCutResult`` which contains the
+      cutset value, the partition of the nodes and the edges in the cutset.
+
+The solution for the minimum cut problem is shown in the figure below. The edges
+in the cutset are shown in blue (with their capacity values shown), and the
+nodes in the partitions are shown in blue (nodes 0 and 1) and in green (nodes 2,
+3, 4 and 5). The capacity of the minimum cut is :math:`B_{0,2}+B_{1,3}=2+1=3`
+which is also the value of the maximum flow. We can also see that if we remove
+the blue edges we would be left with a disconnected graph with the two
+partitions.
+
+.. image:: figures/min-cut.png
+  :width: 600
+  :alt: Minimum Cut solution.
+  :align: center
+
+.. footbibliography::

docs/source/refs/graphs.bib

@@ -43,3 +43,43 @@ @article{kovacs2015minimum
   year={2015},
   publisher={Taylor \& Francis}
 }
+
+@article{ford1956maximal,
+  title={Maximal flow through a network},
+  author={Ford, Lester Randolph and Fulkerson, Delbert R},
+  journal={Canadian journal of Mathematics},
+  volume={8},
+  pages={399--404},
+  year={1956},
+  publisher={Cambridge University Press}
+}
+
+@article{goldberg1988new,
+  title={A new approach to the maximum-flow problem},
+  author={Goldberg, Andrew V and Tarjan, Robert E},
+  journal={Journal of the ACM (JACM)},
+  volume={35},
+  number={4},
+  pages={921--940},
+  year={1988},
+  publisher={ACM New York, NY, USA}
+}
+
+@inproceedings{orlin2013max,
+  title={Max flows in O(nm) time, or better},
+  author={Orlin, James B},
+  booktitle={Proceedings of the forty-fifth annual ACM symposium on Theory of computing},
+  pages={765--774},
+  year={2013}
+}
+
+@article{caliskan2012faster,
+  title={A faster polynomial algorithm for the constrained maximum flow problem},
+  author={Çalışkan, Cenk},
+  journal={Computers \& operations research},
+  volume={39},
+  number={11},
+  pages={2634--2641},
+  year={2012},
+  publisher={Elsevier}
+}