Add bertsekas algo for assignment problem

[smu160]

Hi,

Apologies for the delay. I added a basic implementation with random cost matrices that compare to the hungarian algorithm. I'll add benchmarks, but I think I'll create a non-criterion based benchmark in order to account for possibly sub-optimal solutions. That is, we want to not only compare the speedup, but how close the total score is to what the hungarian algorithm provides.

I didn't have the time just yet to fix the lack of parallelism. However, the structure for concurrency is already there. That is why you'll see the use of mspc::channel. I also need to use the Matrix struct that is used by the current hungarian algorithm implementation. Luckily, that should only improve performance given the 1D representation. I'll keep working on this. Please feel free to let me know what you think.

Thank you!!

Fix #586

Summary by CodeRabbit

• New Features
  • Added a new profiling configuration for detailed performance builds.
  • Expanded benchmarking capabilities to compare assignment algorithm performance over varying test sizes.
  • Introduced an example tool that demonstrates algorithm benchmarking with printed performance metrics.
  • Integrated an auction-based assignment algorithm into the public library.
  • Enhanced matrix functionality with improved row access for easier data handling.

[codspeed-hq[bot]]

CodSpeed Performance Report

Merging #600 will not alter performance

_{Comparing smu160:bertsekas (322f8a1) with main (e8e81df)}

Summary

✅ 36 untouched benchmarks

🆕 14 new benchmarks

Benchmarks breakdown

	Benchmark	main	smu160:bertsekas	Change
🆕	Bertekas Auction[1000]	N/A	18.7 ms	N/A
🆕	Bertekas Auction[100]	N/A	343.1 µs	N/A
🆕	Bertekas Auction[10]	N/A	24.1 µs	N/A
🆕	Bertekas Auction[200]	N/A	1.1 ms	N/A
🆕	Bertekas Auction[20]	N/A	30.9 µs	N/A
🆕	Bertekas Auction[500]	N/A	5.8 ms	N/A
🆕	Bertekas Auction[50]	N/A	124.8 µs	N/A
🆕	Hungarian Algorithm[1000]	N/A	5.8 s	N/A
🆕	Hungarian Algorithm[100]	N/A	1.3 ms	N/A
🆕	Hungarian Algorithm[10]	N/A	16.1 µs	N/A
🆕	Hungarian Algorithm[200]	N/A	7.2 ms	N/A
🆕	Hungarian Algorithm[20]	N/A	42.9 µs	N/A
🆕	Hungarian Algorithm[500]	N/A	434.7 ms	N/A
🆕	Hungarian Algorithm[50]	N/A	355 µs	N/A

[smu160]

Preliminary benchmarks for #586.

[smu160]

@samueltardieu

I added a benchmark, using criterion, to compare the kuhn_munkres implementation with the bertsekas implementation.

Since the Bertsekas auction can give sub-optimal results (this is dependent on $\epsilon$ and the scaling factor), I included plots I rendered via matplotlib to compare the run-time as well as the overall score of the assignments. The underlying "benchmark" for these plots can be found in examples/assignment.rs.

cargo criterion --bench kuhn_munkres_vs_bertsekas --output-format bencher
    Finished `bench` profile [optimized] target(s) in 0.02s
test Assignment Problem/Bertekas Auction/10 ... bench:        3346 ns/iter (+/- 902)
test Assignment Problem/Hungarian Algorithm/10 ... bench:         777 ns/iter (+/- 126)
test Assignment Problem/Bertekas Auction/20 ... bench:        7051 ns/iter (+/- 1342)
test Assignment Problem/Hungarian Algorithm/20 ... bench:        2634 ns/iter (+/- 362)
test Assignment Problem/Bertekas Auction/50 ... bench:       20769 ns/iter (+/- 2251)
test Assignment Problem/Hungarian Algorithm/50 ... bench:       17671 ns/iter (+/- 2471)
test Assignment Problem/Bertekas Auction/100 ... bench:       54119 ns/iter (+/- 4672)
test Assignment Problem/Hungarian Algorithm/100 ... bench:      174590 ns/iter (+/- 38993)
test Assignment Problem/Bertekas Auction/200 ... bench:      169806 ns/iter (+/- 15348)

Warning: Unable to complete 100 samples in 5.0s. You may wish to increase target time to 5.4s, enable flat sampling, or reduce sample count to 60.
test Assignment Problem/Hungarian Algorithm/200 ... bench:      989247 ns/iter (+/- 128546)
test Assignment Problem/Bertekas Auction/500 ... bench:      888877 ns/iter (+/- 52426)
test Assignment Problem/Hungarian Algorithm/500 ... bench:    45738048 ns/iter (+/- 2187718)
test Assignment Problem/Bertekas Auction/1000 ... bench:     2360069 ns/iter (+/- 317069)

Warning: Unable to complete 100 samples in 5.0s. You may wish to increase target time to 47.1s, or reduce sample count to 10.
test Assignment Problem/Hungarian Algorithm/1000 ... bench:   492148479 ns/iter (+/- 10239431)

This PR is definitely still a work in progress, but I thought I'd share these preliminary results with you so you can have a clearer picture.

Note that the code is setup for parallelism already via the use of channels. Thus, these benchmark results contain a lot of unnecessary overhead for the bertseka's algorithm implementation, without the advantage of using multiple threads.

In addition this is a very basic implementation of this algorithm. We can actually converge on the solution much quicker if we have a forward/reverse auction. Luckily, this current implementation can be easily extended to facilitate this.

Lastly, please note this current implementation does not support asymmetric assignment problems. Thus, we always have to have $N == M$, where $N$ is the # of agents, and $M$ is the # of tasks. The addition of the reverse auction (see the above paper) would facilitate assignment problems where $N < M$.

Thank you!!

[samueltardieu]

The preliminary results are encouraging, this is interesting to read.

[samueltardieu]

@coderabbitai review

[coderabbitai[bot]]

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[coderabbitai[bot]]

Walkthrough

This pull request updates project configurations and introduces new benchmarking and algorithm functionalities. The Cargo.toml file is reformatted, a new profiling configuration is added, and a benchmark entry is introduced. Additionally, new Rust files are added to benchmark assignment algorithms and provide an example usage. The auction algorithm—implementing Dmitri Bertsekas’s approach—is introduced with supporting data structures and methods, and its public API is exposed via a new module. A utility method for matrix row access is also added.

Changes

File(s)	Summary
Cargo.toml	Reformatted the pre-release-replacements, added a [profile.profiling] section inheriting from release, and inserted a new benchmark entry.
benches/kuhn_munkres_vs_bertsekas.rs, examples/assignment.rs	Introduced new benchmark files: one using Criterion to compare the Hungarian algorithm against the Bertsekas Auction, and an example demo for assignment problems.
src/bertsekas.rs, src/lib.rs	Added a new implementation of the Bertsekas Auction Algorithm including new structs, methods (e.g., initialization, bidding, scoring), and tests; exposed via the public module.
src/matrix.rs	Added a public method get_row to retrieve a specific row as a slice from a matrix.

Sequence Diagram(s)

sequenceDiagram
    participant U as User
    participant BM as Benchmark (Criterion)
    participant CM as Matrix Creator
    participant HA as Hungarian Algorithm
    participant AA as Auction Algorithm
    participant R as Results Logger

    U->>BM: Trigger benchmark run
    BM->>CM: Generate matrices for given sizes
    CM->>HA: Provide integer matrix for Hungarian algorithm
    CM->>AA: Provide floating-point matrix for Auction algorithm
    HA->>BM: Return performance metrics
    AA->>BM: Return performance metrics
    BM->>R: Record and plot throughput data

sequenceDiagram
    participant A as Agent
    participant AU as Auction Context
    participant T as Task Manager

    A->>AU: Submit bid for a task
    AU->>T: Evaluate bid and update task assignment
    T-->>AU: Confirm assignment update
    AU->>A: Notify bid outcome

Assessment against linked issues

Objective	Addressed	Explanation
Implement a faster maximum weight matching solver using the auction algorithm (#586)	✅
Benchmark and compare the auction algorithm against the Hungarian algorithm (#586)	✅
Validate algorithm correctness with integrated tests (#586)	✅

Poem

I hopped through lines of code so light,
Auction bids and benchmarks in plain sight.
Matrices sang with rows unfurled,
Algorithms danced in a joyful world.
With every tweak, my code took flight—
A rabbit’s cheer in code so bright! 🐰✨

✨ Finishing Touches

• [ ] 📝 Generate Docstrings

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