Fixed-size shortest path

struct FixedSizeShortestPathBenchmark <: AbstractBenchmark

Benchmark problem for the shortest path problem. In this benchmark, all graphs are acyclic directed grids, all of the same size grid_size. Features are given at instance level (one dimensional vector of length p for each graph).

Data is generated using the process described in: https://arxiv.org/abs/2307.13565.

Fields

• graph::Graphs.SimpleGraphs.SimpleDiGraph{Int64}: grid graph instance

• grid_size::Tuple{Int64, Int64}: grid size of graphs

• p::Int64: size of feature vectors

• deg::Int64: degree of formula between features and true weights

• ν::Float32: multiplicative noise for true weights sampled between [1-ν, 1+ν], should be between 0 and 1

FixedSizeShortestPathBenchmark(
;
    grid_size,
    p,
    deg,
    ν
) -> FixedSizeShortestPathBenchmark

Constructor for FixedSizeShortestPathBenchmark.

generate_dataset(
    bench::FixedSizeShortestPathBenchmark;
    ...
) -> Vector
generate_dataset(
    bench::FixedSizeShortestPathBenchmark,
    dataset_size::Int64;
    seed,
    type
) -> Vector

Generate dataset for the shortest path problem.

generate_maximizer(
    bench::FixedSizeShortestPathBenchmark;
    use_dijkstra
) -> DecisionFocusedLearningBenchmarks.FixedSizeShortestPath.var"#shortest_path_maximizer#8"{DecisionFocusedLearningBenchmarks.FixedSizeShortestPath.var"#shortest_path_maximizer#5#9"{typeof(Graphs.dijkstra_shortest_paths), Vector{Int64}, Vector{Int64}, Int64, Int64, Graphs.SimpleGraphs.SimpleDiGraph{Int64}}}

Outputs a function that computes the longest path on the grid graph, given edge weights θ as input.

maximizer = generate_maximizer(bench)
maximizer(θ)

generate_statistical_model(
    bench::FixedSizeShortestPathBenchmark
) -> Flux.Chain{T} where T<:Tuple{Flux.Dense{typeof(identity), Matrix{Float32}}}

Initialize a linear model for bench using Flux.