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To benchmark C++ assignment solvers fairly, first make every solver handle the same mathematical problem, then test it on documented placement-relevant matrices, verify every result, and publish enough environment and measurement detail for others to reproduce the comparison. Dense random square matrices alone do not demonstrate performance on realistic placement workloads—and there is no verified public placement-workload suite established by the sources cited here.

Define the assignment problem before comparing solvers

“Assignment solver” can describe implementations that do not solve the same variant. Write down the contract for your benchmark before choosing inputs or interpreting output. OR-Tools models assignment as costs between agents and tasks; its documentation also distinguishes a specialized linear sum assignment solver from more general MIP and CP-SAT formulations. Its example includes a case in which workers may remain unassigned when there are more workers than tasks (OR-Tools linear assignment documentation; assignment example).

  • Shape and cardinality: Are matrices square or rectangular? Must every item on the smaller side be matched, or may assignments remain unmatched?
  • Allowed pairs: Are absent edges forbidden, or are they represented by a penalty? If a solver cannot accept forbidden edges directly, explain how you encode them and how that affects feasibility and cost.
  • Objective and numeric rules: Are costs minimized or maximized? What numeric types, ranges, and precision do the implementations accept?
  • Failure behavior: How does each implementation report an infeasible instance, invalid input, or timeout?

Do not compare objective values or runtimes until those rules align. If a solver requires padding or another matrix transformation, document it and ensure the transformation preserves the intended problem and reported objective.

Build a workload suite that reflects the application

A useful suite varies more than matrix size. Include square and rectangular dimensions, multiple aspect ratios, and both dense and sparse allowed-edge patterns. Vary cost distributions and value ranges; include ties or repeated values when they occur in the placement domain. Published materials demonstrate that benchmarks can examine matrix size and dense or sparse cases, but their results are specific to their implementations and setups. A repository benchmark tests multiple solver implementations by matrix size (Hungarian algorithm benchmark repository), while a separate C++ repository publishes dense and sparse timing tables (C++ Hungarian algorithm repository).

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For placement-specific claims, connect generated or recorded instances to the actual application. Document the source and transformation of any production trace, or describe a generator and the observed properties it is intended to represent. Useful evidence may include dimensions, aspect ratios, allowed-edge density, cost distributions, and recurring structural patterns. Label cases as easy, typical, or difficult only when those labels correspond to observed workload properties. Without documented placement data or a justified generator, describe the suite as synthetic assignment workloads—not as a verified realistic placement benchmark.

Validate correctness before measuring speed

Run independent correctness checks on each result before accepting its timing. The checker should use the original input costs and the benchmark’s stated contract, rather than trusting the solver’s reported objective.

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  • Confirm every returned pair is an allowed assignment.
  • Check that no row or column is used more often than the contract permits.
  • Verify that the result has the required cardinality and respects any unmatched-item rules.
  • Recompute the total objective from the original costs and compare it with the solver’s reported value, accounting for the documented numeric representation.
  • Include infeasible cases if the application can produce them, and verify that each solver reports them consistently with the contract.

For a subset of small instances, compare results with a trusted exact formulation or an enumerator. This gives the validation process an independent reference. These checks are benchmark controls to implement; the cited OR-Tools sources explain assignment semantics but do not prescribe a shared validation protocol.

Measure runtime and memory reproducibly

Publish the conditions needed to interpret and repeat each measurement. Record the processor model, memory, operating system, compiler and version, optimization flags, solver or library versions, and thread count. Also document input generation, random seeds, warm-up and repetition policy, and the timing statistic used. State whether timings include allocation, preprocessing, input conversion, or output validation.

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Unless the benchmark is specifically measuring an end-to-end application workflow, keep input construction and result validation outside the timed region. Measure elapsed time and memory when both affect deployment. Report per-instance results or distributions for each workload stratum alongside aggregate summaries; explain timeouts and outliers rather than silently excluding them. Show scaling across dimensions and density so readers can see where a solver’s behavior changes.

There is no shared controlled C++ measurement protocol established by the cited repositories. Their published numbers are implementation-specific. The sparse-results table includes matrix sizes from 8 through 1024, but that range alone does not make its timings transferable to another machine or placement workload. Check the repository’s own methodology before quoting individual values, and do not turn those results into a universal solver ranking.

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Choose implementations that match the problem

For pure linear assignment, compare specialized assignment algorithms on the same contract. OR-Tools describes its linear sum assignment solver as specialized for simple assignment and contrasts it with MIP and CP-SAT, which can express richer scenarios. Include MIP or CP-SAT when placement rules require that flexibility, but separate model construction and general-purpose solver overhead from the assignment kernel when that distinction matters.

Algorithm names are not enough to establish comparable performance. The OR-Tools C++ reference describes its documented Kuhn–Munkres implementation as “An O(n^4) implementation of the Kuhn-Munkres algorithm (a.k.a. the Hungarian algorithm) for solving the assignment problem,” and advises using graph/linear_assignment.h, whose complexity is usually much smaller (OR-Tools C++ Hungarian reference). This is an algorithmic statement about that documented implementation, not a runtime measurement or a guarantee about every implementation called Hungarian.

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A separate C++ implementation page describes rectangular dimensions and O(rc min(r,c)) complexity while incorporating Jonker–Volgenant ideas (C++ implementation repository). Treat that as a property claimed for that implementation, not as a general guarantee for the algorithm family or for your workload. Compare problem coverage, objective correctness, runtime and memory by workload class, API and integration requirements, and the exact version tested.

Report conclusions narrowly

A useful benchmark conclusion identifies which implementation performed better for which documented workload classes, under which build and machine conditions, and with what correctness coverage. State whether the results include conversion, preprocessing, or allocation, and make the input set and reproduction procedure available when possible. A timing table from a particular repository or computer is evidence about that test—not a universal ranking and not, by itself, evidence about realistic placement performance.

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