Расширения

JuMP = "1.0.0 - 1.1.1"

julia> struct NewMOIVectorSet <: MOI.AbstractVectorSet
           dimension::Int
       end

julia> Base.copy(x::NewMOIVectorSet) = x

julia> model = Model();

julia> @variable(model, x[1:2]);

julia> @constraint(model, x in NewMOIVectorSet(2))
[x[1], x[2]] ∈ NewMOIVectorSet(2)

julia> struct NewVectorSet <: JuMP.AbstractVectorSet end

julia> JuMP.moi_set(::NewVectorSet, dim::Int) = NewMOIVectorSet(dim)

julia> @constraint(model, x in NewVectorSet())
[x[1], x[2]] ∈ NewMOIVectorSet(2)

julia> struct AddTwice
           info::JuMP.VariableInfo
       end

julia> function JuMP.build_variable(
           _err::Function,
           info::JuMP.VariableInfo,
           ::Type{AddTwice};
           kwargs...
       )
           println("Can also use $kwargs here.")
           return AddTwice(info)
       end

julia> function JuMP.add_variable(
           model::JuMP.Model,
           duplicate::AddTwice,
           name::String,
       )
           a = JuMP.add_variable(
               model,
               JuMP.ScalarVariable(duplicate.info),
               "$(name)_a",
            )
           b = JuMP.add_variable(
               model,
               JuMP.ScalarVariable(duplicate.info),
               "$(name)_b",
            )
           return (a, b)
       end

julia> model = Model();

julia> @variable(model, x[i=1:2], variable_type = AddTwice, kw = i)
Can also use Base.Pairs(:kw => 1) here.
Can also use Base.Pairs(:kw => 2) here.
2-element Vector{Tuple{VariableRef, VariableRef}}:
 (x[1]_a, x[1]_b)
 (x[2]_a, x[2]_b)

julia> num_variables(model)
4

julia> first(x[1])
x[1]_a

julia> last(x[2])
x[2]_b

julia> function MyModel()
           model = Model()
           model.ext[:MyModel] = 1
           return model
       end
MyModel (generic function with 1 method)

julia> model = MyModel()
A JuMP Model
├ solver: none
├ objective_sense: FEASIBILITY_SENSE
├ num_variables: 0
├ num_constraints: 0
└ Names registered in the model: none

julia> model.ext
Dict{Symbol, Any} with 1 entry:
  :MyModel => 1

julia> using JuMP, HiGHS

julia> model = Model(HiGHS.Optimizer);

julia> @variable(model, x >= 1.5, Int);

julia> @objective(model, Min, x);

julia> function silent_hook(model; silent::Bool)
           if silent
               set_silent(model)
           else
               unset_silent(model)
           end
           ## Обязательно установите ignore_optimize_hook = true, либо обращения
           ## к обработчику оптимизации будут происходить рекурсивно!
           return optimize!(model; ignore_optimize_hook = true)
       end
silent_hook (generic function with 1 method)

julia> set_optimize_hook(model, silent_hook)
silent_hook (generic function with 1 method)

julia> optimize!(model; silent = true)

julia> optimize!(model; silent = false)
Coefficient ranges:
  Cost   [1e+00, 1e+00]
  Bound  [2e+00, 2e+00]
Assessing feasibility of MIP using primal feasibility and integrality tolerance of       1e-06
Solution has               num          max          sum
Col     infeasibilities      0            0            0
Integer infeasibilities      0            0            0
Row     infeasibilities      0            0            0
Row     residuals            0            0            0
Presolving model
0 rows, 0 cols, 0 nonzeros  0s
0 rows, 0 cols, 0 nonzeros  0s
Presolve: Optimal

Solving report
  Status            Optimal
  Primal bound      2
  Dual bound        2
  Gap               0% (tolerance: 0.01%)
  Solution status   feasible
                    2 (objective)
                    0 (bound viol.)
                    0 (int. viol.)
                    0 (row viol.)
  Timing            0.00 (total)
                    0.00 (presolve)
                    0.00 (postsolve)
  Nodes             0
  LP iterations     0 (total)
                    0 (strong br.)
                    0 (separation)
                    0 (heuristics)

julia> struct Foo end

julia> function Containers.container(f::Function, indices, ::Type{Foo})
           return reverse([f(i...) for i in indices])
       end

julia> model = Model();

julia> @variable(model, x[1:3], container = Foo)
3-element Vector{VariableRef}:
 x[3]
 x[2]
 x[1]

julia> x[1]
x[3]

julia> @variable(model, y[1:3, 1:2], container = Foo)
3×2 Matrix{VariableRef}:
 y[3,2]  y[3,1]
 y[2,2]  y[2,1]
 y[1,2]  y[1,1]

julia> y[1, 1]
y[3,2]

julia> @variable(model, z[i=1:3; isodd(i)], container = Foo)
2-element Vector{VariableRef}:
 z[3]
 z[1]

julia> z[2]
z[1]

julia> model = Model();

julia> @variable(model, x[1:3])
3-element Vector{VariableRef}:
x[1]
x[2]
x[3]

julia> y = reverse(x)
3-element Vector{VariableRef}:
x[3]
x[2]
x[1]

function all_names_slow(model)
    names = Set{String}()
    for ci in all_constraints(model)
        push!(names, name(ci))
    end
    return names
end

function _function_barrier(names, model, ::Type{F}, ::Type{S}) where {F,S}
    for ci in all_constraints(model, F, S)
        push!(names, name(ci))
    end
    return
end

function all_names_fast(model)
    names = Set{String}()
    for (F, S) in list_of_constraint_types(model)
        _function_barrier(names, model, F, S)
    end
    return names
end