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Use a linear assignment solver for one-to-one matching with pairwise costs; use min-cost flow when capacities, supplies and demands, or a broader network are part of the problem. Assignment can also be modeled as min-cost flow, so the practical choice is usually the formulation that matches your constraints and the solver API—not a claim that one method is always faster.

Start with the shape of the constraints

Ask whether you are pairing items from two groups, or routing units through a network.

  • Pairing: Each selected worker–job or item–task pair has a cost, and each item can be used at most once. This is the linear assignment problem.
  • Routing: Nodes provide or require quantities, and directed arcs have capacities and costs. This is the broader min-cost flow model.

A standard assignment can be represented as a flow network: connect a source to workers, workers to eligible tasks, and tasks to a sink; give assignment arcs their pair costs. OR-Tools documents this reduction in its assignment-as-minimum-cost-flow example. If the problem has no meaningful network constraints beyond the one-to-one pairing, a direct assignment API is usually the clearer expression.

How the models differ

Question Linear assignment Min-cost flow
What is being optimized? Total cost of selected pairs, with each row and column used at most once. See SciPy’s assignment API. Total cost of sending flow through a directed graph subject to node demands and edge capacities. See NetworkX’s min-cost-flow API.
Natural input A dense row-by-column cost matrix, or a sparse bipartite graph if the solver provides that API. A graph whose nodes have supplies or demands and whose arcs have costs and capacities.
When it fits best One-to-one allocation without additional network requirements. Multiple units per entity, capacity limits, or conservation of supply and demand across a network.
Unmatched items Depends on the API and matrix shape; confirm its cardinality convention against your policy. Depends on the specified supplies, demands, capacities, and feasibility of the network.

Choose based on your actual use case

Use linear assignment for a pairwise allocation

Choose assignment when each worker can take at most one job and each job can go to at most one worker, with a cost for each allowed pairing. SciPy’s linear_sum_assignment accepts rectangular matrices; when the two sides differ in size, not every row or column must be assigned. Its documented objective is to minimize the total cost of selected row-column pairs. The current development documentation describes a modified Jonker–Volgenant implementation, but implementation details can vary by release, so consult the documentation for the SciPy version you deploy.

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Google OR-Tools also documents a separate linear sum assignment solver. A dedicated API keeps the model close to the problem statement and avoids adding flow machinery that is not needed.

Use min-cost flow when quantities or routes matter

Choose min-cost flow when an entity can supply or receive multiple units, when links have capacities, or when the problem requires quantities to move through a network while respecting node supply and demand. These are native parts of the flow model, rather than details you have to approximate as individual one-to-one pairs.

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For NetworkX specifically, min_cost_flow works with node demands and edge capacities and costs. Its documentation requires total node demand to sum to zero for feasibility, and warns that its implementation is not guaranteed for floating-point edge weights or demands because of roundoff and overflow. Treat that as a NetworkX-specific limitation, not a general statement about every min-cost-flow solver.

Do not assume every side constraint is a flow constraint

Assignment plus capacities or network conservation can often be modeled naturally as flow. But arbitrary extra rules do not automatically fit ordinary min-cost flow. If a constraint cannot be expressed through the network’s arcs, capacities, supplies, and demands, check whether the chosen solver supports it or whether a different optimization model is needed.

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Handle sparse eligibility and unmatched items explicitly

When only some pairs are allowed

If most worker–task combinations are forbidden, a dense matrix may be an awkward representation. SciPy provides min_weight_full_bipartite_matching for sparse bipartite graphs. It seeks a matching with cardinality equal to the smaller partition and raises an error if that full matching cannot be found. NetworkX’s minimum_weight_full_matching offers the same rectangular full-assignment interpretation and delegates the calculation to SciPy. See the SciPy sparse matching documentation and NetworkX’s full matching documentation.

When some items may remain unmatched

“At most once” does not by itself say how many matches the application requires. Decide whether the goal is a perfect matching, a full matching of the smaller side, or a smaller number of assignments that reflects the business rules. Rectangular assignment APIs may leave items on the larger side unassigned; a sparse full-matching API may instead fail if it cannot match every vertex in the smaller partition. If you need a different policy, encode that policy deliberately and verify the solver’s behavior rather than relying on a default.

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Check solver and numeric behavior before implementation

  • Input type: Determine whether your data is best represented as a dense matrix or a sparse allowed-edge graph, and use an API designed for that representation.
  • Cardinality: Confirm exactly which items must be matched and what happens when no feasible full matching exists.
  • Numeric support: Check the documentation for your chosen library’s handling of costs and demands. NetworkX’s floating-point warning applies to its min-cost-flow implementation; it should not be generalized to other libraries.
  • Version: Solver algorithms and API behavior are version-specific. Check the documentation for the version installed in production.
  • Performance: No universal runtime winner is established by the cited documentation. If speed matters, benchmark equivalent formulations using representative inputs, the intended library version, and the numeric types you will actually use.
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A practical decision checklist

  1. Write down the entities and rules. If the rule is “each item gets at most one partner,” start with linear assignment. If units travel through capacity-limited links, start with min-cost flow.
  2. Specify the matching requirement. State whether the solution must match both sides, the smaller side, or only a chosen number of items.
  3. Choose the representation. Use a cost matrix for dense pairwise costs; use a sparse matching API when only a subset of pairs is allowed. Use a network for supplies, demands, and arc capacities.
  4. Verify the library’s semantics. Check the exact API’s cardinality, infeasibility behavior, and numeric caveats in the documentation for your version.
  5. Benchmark only if performance is decisive. Compare equivalent models on representative cases instead of assuming one formulation is faster.

Bottom line

For plain one-to-one matching, use a linear assignment solver. For capacity-constrained movement through a network, use min-cost flow. When assignment is only one part of a larger network, flow may be the more natural representation; choose based on the constraints you must express and the behavior of the specific solver you plan to use.

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