Code from GitHub example fails
Nobody has claimed this yet.
Assessment
- Difficulty
- 3/5
- Estimated time
- 1-2 days
- Newbie friendliness
- 35/100
- Issue type
- Bug
- Clarity
- Mostly clear
- Activity status
- Stale
- Tech stack
- julia
- Domain
- documentation
Research direction
Locate the introductory example referenced in the issue and run it with Julia 1.9.2 and GraphsMatching 0.2.0, starting from the failing with_optimizer call. Update the example so it uses the currently supported optimizer API, then rerun it to confirm the matching result; separately determine whether the scipy sparse full bipartite matching question belongs in this issue.
Written by the indexing model from the issue text.
Description
On Julia 1.9.2 with GraphsMatching 0.2.0, the code from the introductory example does not run:
julia> using Graphs, GraphsMatching
julia> g = complete_graph(3)
julia> w = zeros(3,3)
julia> w[1,2] = 1
julia> w[3,2] = 1
julia> w[1,3] = 1
julia> match = maximum_weight_matching(g, with_optimizer(Cbc.Optimizer, logLevel=0), w)
ERROR: UndefVarError: `with_optimizer` not defined
After digging around, I succeeded with:
julia> match = maximum_weight_matching(g, JuMP.optimizer_with_attributes(Cbc.Optimizer,"LogLevel"=>0), w)
Welcome to the CBC MILP Solver
Version: 2.10.8
Build Date: Jan 1 1970
command line - Cbc_C_Interface -LogLevel 0 -solve -quit (default strategy 1)
MatchingResult{Float64}(1.0, [2, 1, -1])
On an unrelated note, I have a question: does the graph optimization ecosystem in Julia cover the functionality of scipy.sparse.csgraph.min_weight_full_bipartite_matching?
- Dominant language
- Julia
- Stars
- 20
- Forks
- 8
- PR merge metrics
- No merged PRs in 30d
Getting set up
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First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
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