Add ZeroOne and Integer support to lp_matrix_data

docs/src/manual/models.md

@@ -447,9 +447,9 @@ matrix form of a linear program.
 ```jldoctest
 julia> begin
            model = Model()
-           @variable(model, x >= 1)
+           @variable(model, x >= 1, Bin)
            @variable(model, 2 <= y)
-           @variable(model, 3 <= z <= 4)
+           @variable(model, 3 <= z <= 4, Int)
            @constraint(model, x == 5)
            @constraint(model, 2x + 3y <= 6)
            @constraint(model, -4y >= 5z + 7)
@@ -461,10 +461,10 @@ julia> data = lp_matrix_data(model);
 
 julia> data.A
 4×3 SparseArrays.SparseMatrixCSC{Float64, Int64} with 7 stored entries:
- 1.0    ⋅     ⋅ 
+ 1.0    ⋅     ⋅
   ⋅   -4.0  -5.0
- 2.0   3.0    ⋅ 
- 1.0   1.0    ⋅ 
+ 2.0   3.0    ⋅
+ 1.0   1.0    ⋅
 
 julia> data.b_lower
 4-element Vector{Float64}:
@@ -503,8 +503,22 @@ julia> data.c_offset
 
 julia> data.sense
 MAX_SENSE::OptimizationSense = 1
+
+julia> data.integers
+1-element Vector{Int64}:
+ 3
+
+julia> data.binaries
+1-element Vector{Int64}:
+ 1
 ```
 
+!!! warning
+    [`lp_matrix_data`](@ref) is intentionally limited in the types of problems
+    that it supports and the structure of the matrices it outputs. It is mainly
+    intended as a pedagogical and debugging tool. It should not be used to
+    interface solvers, see [Implementing a solver interface](@ref) instead.
+
 ## Backends
 
 !!! info

src/lp_matrix_data.jl

@@ -18,6 +18,8 @@ struct LPMatrixData{T}
     c::Vector{T}
     c_offset::T
     sense::MOI.OptimizationSense
+    integers::Vector{Int}
+    binaries::Vector{Int}
     variables::Vector{GenericVariableRef{T}}
     affine_constraints::Vector{ConstraintRef}
     variable_constraints::Vector{ConstraintRef}
@@ -35,6 +37,7 @@ struct storing data for an equivalent linear program in the form:
       & x_l \\le x \\le x_u
 \\end{aligned}
 ```
+where elements in `x` may be continuous, integer, or binary variables.
 
 ## Fields
 
@@ -50,9 +53,13 @@ The struct returned by [`lp_matrix_data`](@ref) has the fields:
    the value of `typemin(T)` is used.
  * `x_upper::Vector{T}`: the dense vector of variable upper bounds. If missing,
    the value of `typemax(T)` is used.
- * `c::Vector{T}`: the dense vector of linear objective coefficiennts
+ * `c::Vector{T}`: the dense vector of linear objective coefficients
  * `c_offset::T`: the constant term in the objective function.
  * `sense::MOI.OptimizationSense`: the objective sense of the model.
+ * `integers::Vector{Int}`: the sorted list of column indices that are integer
+   variables.
+ * `binaries::Vector{Int}`: the sorted list of column indices that are binary
+   variables.
  * `variables::Vector{GenericVariableRef{T}}`: a vector of [`GenericVariableRef`](@ref),
    corresponding to order of the columns in the matrix form.
  * `affine_constraints::Vector{ConstraintRef}`: a vector of [`ConstraintRef`](@ref),
@@ -62,9 +69,6 @@ The struct returned by [`lp_matrix_data`](@ref) has the fields:
 
 The models supported by [`lp_matrix_data`](@ref) are intentionally limited
 to linear programs.
-
-If your model has integrality, use [`relax_integrality`](@ref) to remove integer
-restrictions before calling [`lp_matrix_data`](@ref).
 """
 function lp_matrix_data(model::GenericModel{T}) where {T}
     variables = all_variables(model)
@@ -80,6 +84,8 @@ function lp_matrix_data(model::GenericModel{T}) where {T}
         I = Int[],
         J = Int[],
         V = T[],
+        integers = Int[],
+        binaries = Int[],
         variable_to_column = columns,
         bound_constraints = ConstraintRef[],
         affine_constraints = ConstraintRef[],
@@ -97,6 +103,8 @@ function lp_matrix_data(model::GenericModel{T}) where {T}
         cache.c,
         cache.c_offset[],
         MOI.get(model, MOI.ObjectiveSense()),
+        sort!(cache.integers),
+        sort!(cache.binaries),
         variables,
         cache.affine_constraints,
         cache.bound_constraints,
@@ -128,6 +136,32 @@ function _fill_standard_form(
     return
 end
 
+function _fill_standard_form(
+    model::GenericModel{T},
+    ::Type{GenericVariableRef{T}},
+    ::Type{MOI.Integer},
+    cache::Any,
+) where {T}
+    for c in all_constraints(model, GenericVariableRef{T}, MOI.Integer)
+        c_obj = constraint_object(c)
+        push!(cache.integers, cache.variable_to_column[c_obj.func])
+    end
+    return
+end
+
+function _fill_standard_form(
+    model::GenericModel{T},
+    ::Type{GenericVariableRef{T}},
+    ::Type{MOI.ZeroOne},
+    cache::Any,
+) where {T}
+    for c in all_constraints(model, GenericVariableRef{T}, MOI.ZeroOne)
+        c_obj = constraint_object(c)
+        push!(cache.binaries, cache.variable_to_column[c_obj.func])
+    end
+    return
+end
+
 function _fill_standard_form(
     model::GenericModel{T},
     ::Type{F},

test/test_lp_matrix_data.jl

@@ -10,9 +10,9 @@ using Test
 
 function test_standard_matrix_form()
     model = Model()
-    @variable(model, x >= 1)
+    @variable(model, x >= 1, Bin)
     @variable(model, 2 <= y)
-    @variable(model, 3 <= z <= 4)
+    @variable(model, 3 <= z <= 4, Int)
     @constraint(model, x == 5)
     @constraint(model, 2x + 3y <= 6)
     @constraint(model, -4y >= 5z + 7)
@@ -27,11 +27,17 @@ function test_standard_matrix_form()
     @test a.c == [2, 0, 0]
     @test a.c_offset == 1
     @test a.sense == MOI.MAX_SENSE
+    @test a.integers == [3]
+    @test a.binaries == [1]
     @objective(model, Min, y)
+    unset_binary(x)
+    set_integer(x)
     b = lp_matrix_data(model)
     b.sense == MOI.MIN_SENSE
     b.c == [0, 1, 0]
     b.c_offset == 0
+    @test b.integers == [1, 3]
+    @test b.binaries == Int[]
     return
 end
 
@@ -73,10 +79,11 @@ end
 
 function test_standard_matrix_form_bad_constraint()
     model = Model()
-    @variable(model, x, Bin)
+    @variable(model, x[1:3])
+    @constraint(model, x in SecondOrderCone())
     @test_throws(
         ErrorException(
-            "Unsupported constraint type in `lp_matrix_data`: $(VariableRef) -in- $(MOI.ZeroOne)",
+            "Unsupported constraint type in `lp_matrix_data`: $(Vector{VariableRef}) -in- $(MOI.SecondOrderCone)",
         ),
         lp_matrix_data(model),
     )