Add bertsekas algo for assignment problem #600
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CodSpeed Performance ReportMerging #600 will not alter performanceComparing Summary
Benchmarks breakdown
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I added a benchmark, using criterion, to compare the Since the Bertsekas auction can give sub-optimal results (this is dependent on
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 Thank you!! |
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The preliminary results are encouraging, this is interesting to read. |
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Pull Request Overview
This pull request adds a new implementation of the Bertsekas assignment algorithm alongside benchmarks that compare its performance and score to the Hungarian algorithm. Key changes include:
- A new method in Matrix to access a row as a slice.
- The introduction of the bertsekas module in src/lib.rs.
- Examples and benchmarks to evaluate the Bertsekas approach versus the Kuhn-Munkres implementation.
Reviewed Changes
Copilot reviewed 6 out of 6 changed files in this pull request and generated 1 comment.
Show a summary per file
| File | Description |
|---|---|
| src/matrix.rs | Adds a get_row method to extract a slice for a row. |
| src/lib.rs | Introduces a new module for the Bertsekas algorithm. |
| examples/assignment.rs | Provides a runnable example comparing both algorithms. |
| benches/kuhn_munkres_vs_bertsekas.rs | Adds benchmarks to compare performance and score between algorithms. |
| Cargo.toml | Updates configuration to include the new benchmark; minor formatting adjustments. |
| let elements = size * size; | ||
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| group.throughput(Throughput::Elements(elements as u64)); | ||
| group.bench_function(BenchmarkId::new("Bertekas Auction", size), |b| { |
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Typo in the benchmark name: 'Bertekas Auction' should be corrected to 'Bertsekas Auction' to match the module name and maintain consistency.
| group.bench_function(BenchmarkId::new("Bertekas Auction", size), |b| { | |
| group.bench_function(BenchmarkId::new("Bertsekas Auction", size), |b| { |
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WalkthroughThis 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
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
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Actionable comments posted: 3
🧹 Nitpick comments (5)
src/lib.rs (1)
101-101: Newbertsekasmodule added but not included in the preludeThe new module has been correctly added to the public API. However, it's not included in the prelude (lines 117-137), meaning users will need to import it explicitly.
Consider adding the module to the prelude if it's intended to be a stable part of the library:
pub mod prelude { pub use crate::directed::astar::*; // Other imports... pub use crate::kuhn_munkres::*; pub use crate::matrix::*; + pub use crate::bertsekas::*; pub use crate::undirected::connected_components::*; // Rest of imports... }examples/assignment.rs (2)
7-20: Clarify the random value range or consider including zero or negative values.
Currently, the function generates random integers in the range 1..500. If you need to test zero or negative costs, consider broadening the range or documenting why the range excludes them.
11-11: Handle matrix creation errors gracefully.
Using.unwrap()can cause panics in production. If this example is intended for broader usage, consider more robust error handling (e.g., returning aResult).src/bertsekas.rs (2)
10-13: Consider merging agent and bid vectors for easier maintenance.
Right now, each task has separate vectors: one for agents and one for bids. Storing(agent, bid)tuples in a single vector could simplify lookups and help avoid index misalignment.
357-473: Expand tests for edge cases.
Tests cover basic usage, but consider adding cases with negative or zero costs and more agents than tasks (if supported). This will help verify correctness and detect logical regressions.
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📒 Files selected for processing (6)
Cargo.toml(3 hunks)benches/kuhn_munkres_vs_bertsekas.rs(1 hunks)examples/assignment.rs(1 hunks)src/bertsekas.rs(1 hunks)src/lib.rs(1 hunks)src/matrix.rs(1 hunks)
🔇 Additional comments (9)
src/matrix.rs (1)
454-462: Well-implemented row access methodThis is a clean implementation of a useful utility method that efficiently provides access to matrix rows as slices, with proper bounds checking.
Cargo.toml (3)
46-48: Good addition of profiling configurationAdding a profiling profile with debug symbols enabled is useful for performance analysis while maintaining the optimizations of a release build.
20-21: Code formatting improvementThe addition of spaces improves the readability of the configuration entries.
75-77: New benchmark for algorithm comparisonThe benchmark entry is well defined and consistent with the project's benchmarking structure.
benches/kuhn_munkres_vs_bertsekas.rs (4)
8-13: Well structured matrix creation for benchmarkingClean implementation of the test matrix creation that provides both integer and floating-point variants.
23-23: Typo in benchmark nameThere's a spelling error in the benchmark name.
- group.bench_function(BenchmarkId::new("Bertekas Auction", size), |b| { + group.bench_function(BenchmarkId::new("Bertsekas Auction", size), |b| {
15-45: Comprehensive benchmark implementationThe benchmark function is well structured with appropriate throughput measurements and configuration. The use of logarithmic scale for visualization is appropriate given the wide range of matrix sizes being tested.
19-36:❌ Incorrect review comment
Potential inconsistency in benchmark matrix types
The benchmark uses floating-point matrices for Bertsekas auction and integer matrices for the Hungarian algorithm, which may make the comparison less direct.
Consider using the same matrix type for both algorithms if they can operate on the same data type, to ensure a fair comparison:
🏁 Script executed:
#!/bin/bash # Check if the Bertsekas algorithm can also work with integer matrices rg -A 5 "fn bertsekas_aaap" --type rustLength of output: 330
Matrix Type Usage is Intentional
The Bertsekas auction algorithm is designed to work with floating‐point matrices (as enforced by its type constraints), while the Hungarian algorithm is implemented for integer matrices. Since these type differences are intrinsic to each algorithm’s design, using different matrix types in the benchmarks is appropriate. If further clarification is needed, consider adding inline comments to document the reasoning for the difference.
Likely an incorrect or invalid review comment.
examples/assignment.rs (1)
27-42:❓ Verification inconclusive
Mind the performance for large matrix sizes.
Looping up to 5000×5000 can be expensive and memory-intensive, especially with random matrix generation. If benchmarks show significant resource usage, consider parallel generation or smaller default sizes.
🏁 Script executed:
#!/bin/bash # Check memory usage for large matrix sizes by searching mentions in the code. rg -A 5 'let sizes:'Length of output: 393
Performance Consideration for Large Matrix Sizes
After verifying that the
sizesarray inexamples/assignment.rsincludes values up to 5000, it's confirmed that benchmarking will indeed handle matrices up to 5000×5000. Given that generating random matrices at these sizes can be both memory- and compute-intensive, please ensure that performance benchmarks are run. If the results show significant resource usage, consider either parallelizing matrix generation or adjusting the default sizes to mitigate potential performance issues.
| // Send a sentinel value, `None`, indicating we are finished sending all bids. | ||
| auction_data.tx.send(None).unwrap(); | ||
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| while let Some(bid) = auction_data.rx.recv().unwrap() { | ||
| auction_data.add_task_bid(bid.agent, bid.task, bid.amount); | ||
| } | ||
| } |
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🛠️ Refactor suggestion
Evaluate the overhead of bid-channel usage.
The design suggests concurrency, but the code processes bids serially. If concurrency is not implemented, you could remove the channel and loop pattern for simpler direct data handling.
| // The # of columns in the matrix corresponds to the # of tasks | ||
| let n = cost_matrix.columns; | ||
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| assert!(m <= n); |
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Contradictory assert versus code handling.
You assert m <= n, but later code (all_assigned) explicitly checks and attempts to handle m > n. Decide if you truly need to handle more agents than tasks or remove the mismatch.
- assert!(m <= n);
+ // Remove or revise accordingly if you intend to handle m > n📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| assert!(m <= n); | |
| // Remove or revise accordingly if you intend to handle m > n |
| while !auction_data.all_assigned() { | ||
| bid_phase(auction_data); | ||
| assignment_phase(auction_data); | ||
| auction_data.scale_epsilon(); | ||
| } | ||
| } |
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💡 Verification agent
🧩 Analysis chain
Check for endless loop scenarios if no feasible assignment exists.
Currently, if no complete assignment is possible, the loop might keep scaling epsilon and never terminate. Ensure the algorithm has a fallback or termination condition in impossible cases (if you plan to support them).
🏁 Script executed:
#!/bin/bash
# Attempt to find usage or definition of fallback logic or a break condition.
rg -A 5 'while !auction_data.all_assigned()'Length of output: 308
Ensure termination for unassignable auction scenarios.
The auction loop in src/bertsekas.rs (lines 350–355) currently relies solely on auction_data.all_assigned() and lacks a fallback for cases where a complete assignment is impossible. This may result in an endless loop where auction_data.scale_epsilon() is called indefinitely. Please consider implementing a termination condition—such as a maximum iteration count, an epsilon threshold, or another fallback strategy—to guarantee that the loop exits gracefully when no feasible assignment exists.
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 theMatrixstruct 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
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