Subset selection

struct PortfolioOptimizationBenchmark <: AbstractBenchmark

Benchmark problem for the portfolio optimization problem.

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

Fields

• d::Int64: number of assets

• p::Int64: size of feature vectors

• deg::Int64: hypermarameter for data generation

• ν::Float32: another hyperparameter, should be positive

• Σ::Matrix{Float32}: covariance matrix

• γ::Float32: maximum variance of portfolio

• L::Matrix{Float32}: useful for dataset generation

• f::Vector{Float32}: useful for dataset generation

PortfolioOptimizationBenchmark(
;
    d,
    p,
    deg,
    ν,
    seed
) -> PortfolioOptimizationBenchmark

Constructor for PortfolioOptimizationBenchmark.

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

Generate a dataset of labeled instances for the portfolio optimization problem.

generate_maximizer(
    bench::PortfolioOptimizationBenchmark
) -> DecisionFocusedLearningBenchmarks.PortfolioOptimization.var"#portfolio_maximizer#3"{Float32, Matrix{Float32}, Int64}

Create a function solving the MIQP formulation of the portfolio optimization problem.

generate_statistical_model(
    bench::PortfolioOptimizationBenchmark
) -> Flux.Dense{typeof(identity), Matrix{Float32}}

Initialize a linear model for bench using Flux.