PortfolioOptimisers.jl

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JuMP model optimisation ​

PortfolioOptimisers.jl is based on JuMP, as such it tries to be as flexible as possible.

These types and functions let us define solver and solution interfaces.

PortfolioOptimisers.DictStrA_VecPairStrA Type
julia
const DictStrA_VecPairStrA = Union{<:AbstractDict{<:AbstractString, <:Any},
                                   <:AbstractVector{<:Pair{<:AbstractString, <:Any}}}

Alias for a dictionary or vector of pairs with string keys.

Represents solver settings as either a dictionary mapping strings to values, or a vector of pairs where the first element is a string and the second is any value. Used for passing attribute settings to JuMP solvers.

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PortfolioOptimisers.SlvSettings Type
julia
const SlvSettings = Union{<:Pair{<:AbstractString, <:Any}, <:DictStrA_VecPairStrA}

Alias for solver settings used in JuMP-based optimisation.

Represents solver settings as either a single pair of string key and value, or as a dictionary/vector of pairs with string keys. Used for passing attribute settings to JuMP solvers.

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PortfolioOptimisers.Solver Type
julia
struct Solver{T1, T2, T3, T4, T5} <: AbstractEstimator
    name::T1
    solver::T2
    settings::T3
    check_sol::T4
    add_bridges::T5
end

Container for configuring a JuMP solver and its settings.

The Solver struct encapsulates all information needed to set up and run a JuMP optimisation, including the solver backend, solver-specific settings, solution checks, and bridge options.

Fields

Constructor

julia
Solver(; name::Sym_Str = "", solver::Any = nothing,
       settings::Option{<:SlvSettings} = nothing, check_sol::NamedTuple = (;),
       add_bridges::Bool = true)

Keyword arguments correspond to the fields above.

Validation

  • settings:
    • Dict_Vec: !isempty(settings).

Examples

julia
julia> Solver()
Solver
         name ┼ String: ""
       solver ┼ nothing
     settings ┼ nothing
    check_sol ┼ @NamedTuple{}: NamedTuple()
  add_bridges ┴ Bool: true

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PortfolioOptimisers.VecSlv Type
julia
const VecSlv = AbstractVector{<:Solver}

Alias for a vector of Solver objects.

Represents a collection of solver configurations to be used in JuMP-based optimisation routines. Enables sequential or fallback solver strategies by passing multiple solver setups.

Related Types

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PortfolioOptimisers.Slv_VecSlv Type
julia
const Slv_VecSlv = Union{<:Solver, <:VecSlv}

Alias for a single Solver or a vector of Solver objects.

Represents either a single solver configuration or a collection of solver configurations for JuMP-based optimisation routines. Enables flexible dispatch for optimisation functions that accept one or multiple solvers.

Related Types

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PortfolioOptimisers.AbstractJuMPResult Type
julia
abstract type AbstractJuMPResult <: AbstractResult end

Abstract supertype for all JuMP-based optimisation result types in PortfolioOptimisers.jl.

All concrete types representing the result of a JuMP model optimisation should subtype AbstractJuMPResult. This enables a consistent interface for handling solver results throughout the package.

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PortfolioOptimisers.JuMPResult Type
julia
struct JuMPResult{T1, T2} <: AbstractJuMPResult
    trials::T1
    success::T2
end

Result type for JuMP model optimisation.

The JuMPResult struct records the outcome of a JuMP optimisation, including trial errors and success status.

Fields

  • trials: Dictionary of solver trials and errors.

  • success: Boolean indicating whether optimisation succeeded.

Constructor

julia
JuMPResult(; trials::AbstractDict, success::Bool)

Keyword arguments correspond to the fields above.

Examples

julia
julia> JuMPResult(; trials = Dict(:HiGHS => Dict(:optimize! => "error")), success = true)
JuMPResult
   trials ┼ Dict{Symbol, Dict{Symbol, String}}: Dict(:HiGHS => Dict(:optimize! => "error"))
  success ┴ Bool: true

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PortfolioOptimisers.set_solver_attributes Function
julia
set_solver_attributes(args...)

Set solver attributes for a JuMP model.

This is a generic fallback that does nothing if no model or settings are provided.

Arguments

  • args...: Arguments (ignored).

Returns

  • nothing.
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julia
set_solver_attributes(model::JuMP.Model, settings::DictStrA_VecPairStrA)

Set multiple solver attributes on a JuMP model.

Iterates over the provided settings and applies each as a solver attribute.

Arguments

  • model: JuMP model.

  • settings: Dictionary or vector of pairs of solver settings.

Returns

  • nothing.

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julia
set_solver_attributes(model::JuMP.Model, settings::Pair)

Set a single solver attribute on a JuMP model.

Arguments

  • model: JuMP model.

  • settings: Pair of attribute name and value.

Returns

  • nothing.

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PortfolioOptimisers.optimise_JuMP_model! Function
julia
optimise_JuMP_model!(model::JuMP.Model, slv::Slv_VecSlv)

Attempt to optimise a JuMP model using one or more configured solvers.

Tries each solver in order, applying settings and checking for solution feasibility. Returns a JuMPResult with trial errors and success status.

Arguments

  • model: JuMP model to optimise.

  • slv: Single Solver or vector of Solver objects.

Returns

  • res::JuMPResult: Result object containing trial errors and success flag.

Details

  • For each solver, sets the optimizer and attributes, runs JuMP.optimize!, and checks solution feasibility.

  • If a solver fails, records the error and tries the next.

  • Stops at the first successful solution.

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