Add support for modifying quadratic coefficients

Project.toml

@@ -23,7 +23,7 @@ JuMPDimensionalDataExt = "DimensionalData"
 DimensionalData = "0.24"
 LinearAlgebra = "<0.0.1, 1.6"
 MacroTools = "0.5"
-MathOptInterface = "1.19"
+MathOptInterface = "1.25.2"
 MutableArithmetics = "1.1"
 OrderedCollections = "1"
 Printf = "<0.0.1, 1.6"

docs/Project.toml

@@ -52,7 +52,7 @@ Ipopt = "=1.6.0"
 JSON = "0.21"
 JSONSchema = "1"
 Literate = "2.8"
-MathOptInterface = "=1.25.0"
+MathOptInterface = "=1.25.2"
 MultiObjectiveAlgorithms = "=1.3.2"
 PATHSolver = "=1.7.1"
 Plots = "1"

docs/src/manual/constraints.md

@@ -793,8 +793,7 @@ con : 2 x ∈ [-4, -2]
 
 ### Scalar constraints
 
-To modify the coefficients for a linear term (modifying the coefficient of a
-quadratic term is not supported) in a constraint, use
+To modify the coefficients for a linear term in a constraint, use
 [`set_normalized_coefficient`](@ref). To query the current coefficient, use
 [`normalized_coefficient`](@ref).
 ```jldoctest
@@ -814,6 +813,29 @@ julia> normalized_coefficient(con, x[2])
 0.0
 ```
 
+To modify quadratic terms, pass two variables:
+```jldoctest
+julia> model = Model();
+
+julia> @variable(model, x[1:2]);
+
+julia> @constraint(model, con, x[1]^2 + x[1] * x[2] <= 1)
+con : x[1]² + x[1]*x[2] ≤ 1
+
+julia> set_normalized_coefficient(con, x[1], x[1], 2)
+
+julia> set_normalized_coefficient(con, x[1], x[2], 3)
+
+julia> con
+con : 2 x[1]² + 3 x[1]*x[2] ≤ 1
+
+julia> normalized_coefficient(con, x[1], x[1])
+2.0
+
+julia> normalized_coefficient(con, x[1], x[2])
+3.0
+```
+
 !!! warning
     [`set_normalized_coefficient`](@ref) sets the coefficient of the normalized
     constraint. See [Normalization](@ref) for more details.

docs/src/manual/objective.md

@@ -177,9 +177,25 @@ julia> objective_function(model)
 3 x
 ```
 
-!!! info
-    There is no way to modify the coefficient of a quadratic term. Set a new
-    objective instead.
+Use [`set_objective_coefficient`](@ref) with two variables to modify a quadratic
+objective coefficient:
+```jldoctest
+julia> model = Model();
+
+julia> @variable(model, x);
+
+julia> @variable(model, y);
+
+julia> @objective(model, Min, x^2 + x * y)
+x² + x*y
+
+julia> set_objective_coefficient(model, x, x, 2)
+
+julia> set_objective_coefficient(model, x, y, 3)
+
+julia> objective_function(model)
+2 x² + 3 x*y
+```
 
 ## Modify the objective sense
 

src/constraints.jl

@@ -863,6 +863,105 @@ function normalized_coefficient(
     return _affine_coefficient(con.func, variable)
 end
 
+"""
+    set_normalized_coefficient(
+        constraint::ConstraintRef,
+        variable_1:GenericVariableRef,
+        variable_2:GenericVariableRef,
+        value,
+    )
+
+Set the quadratic coefficient associated with `variable_1` and `variable_2` in
+the constraint `constraint` to `value`.
+
+Note that prior to this step, JuMP will aggregate multiple terms containing the
+same variable. For example, given a constraint `2x^2 + 3x^2 <= 2`,
+`set_normalized_coefficient(con, x, x, 4)` will create the constraint `4x^2 <= 2`.
+
+## Example
+
+```jldoctest; filter=r"≤|<="
+julia> model = Model();
+
+julia> @variable(model, x[1:2]);
+
+julia> @constraint(model, con, 2x[1]^2 + 3 * x[1] * x[2] + x[2] <= 2)
+con : 2 x[1]² + 3 x[1]*x[2] + x[2] ≤ 2
+
+julia> set_normalized_coefficient(con, x[1], x[1], 4)
+
+julia> set_normalized_coefficient(con, x[1], x[2], 5)
+
+julia> con
+con : 4 x[1]² + 5 x[1]*x[2] + x[2] ≤ 2
+```
+"""
+function set_normalized_coefficient(
+    constraint::ConstraintRef{<:AbstractModel,CI},
+    # TODO(odow): these are untyped becasue `constraints.jl` is loaded before
+    #             variables.jl
+    variable_1,
+    variable_2,
+    value::Real,
+) where {T,CI<:MOI.ConstraintIndex{MOI.ScalarQuadraticFunction{T}}}
+    new_value = convert(T, value)
+    if variable_1 == variable_2
+        new_value *= T(2)
+    end
+    model = owner_model(constraint)
+    MOI.modify(
+        backend(model),
+        index(constraint),
+        MOI.ScalarQuadraticCoefficientChange(
+            index(variable_1),
+            index(variable_2),
+            new_value,
+        ),
+    )
+    model.is_model_dirty = true
+    return
+end
+
+"""
+    normalized_coefficient(
+        constraint::ConstraintRef,
+        variable_1::GenericVariableRef,
+        variable_2::GenericVariableRef,
+    )
+
+Return the quadratic coefficient associated with `variable_1` and `variable_2`
+in `constraint` after JuMP has normalized the constraint into its standard form.
+
+See also [`set_normalized_coefficient`](@ref).
+
+## Example
+
+```jldoctest; filter=r"≤|<="
+julia> model = Model();
+
+julia> @variable(model, x[1:2]);
+
+julia> @constraint(model, con, 2x[1]^2 + 3 * x[1] * x[2] + x[2] <= 2)
+con : 2 x[1]² + 3 x[1]*x[2] + x[2] ≤ 2
+
+julia> normalized_coefficient(con, x[1], x[1])
+2.0
+
+julia> normalized_coefficient(con, x[1], x[2])
+3.0
+```
+"""
+function normalized_coefficient(
+    connstraint::ConstraintRef{<:AbstractModel,CI},
+    # TODO(odow): these are untyped becasue `constraints.jl` is loaded before
+    #             variables.jl
+    variable_1,
+    variable_2,
+) where {T,CI<:MOI.ConstraintIndex{MOI.ScalarQuadraticFunction{T}}}
+    con = constraint_object(connstraint)
+    return coefficient(con.func, variable_1, variable_2)
+end
+
 """
     set_normalized_rhs(constraint::ConstraintRef, value)
 

src/objective.jl

@@ -247,11 +247,31 @@ function objective_function(model::GenericModel)
 end
 
 """
-    set_objective_coefficient(model::GenericModel, variable::GenericVariableRef, coefficient::Real)
+    set_objective_coefficient(
+        model::GenericModel,
+        variable::GenericVariableRef,
+        coefficient::Real,
+    )
 
-Set the linear objective coefficient associated with `Variable` to `coefficient`.
+Set the linear objective coefficient associated with `variable` to `coefficient`.
 
 Note: this function will throw an error if a nonlinear objective is set.
+
+## Example
+
+```jldoctest
+julia> model = Model();
+
+julia> @variable(model, x);
+
+julia> @objective(model, Min, 2x + 1)
+2 x + 1
+
+julia> set_objective_coefficient(model, x, 3)
+
+julia> objective_function(model)
+3 x + 1
+```
 """
 function set_objective_coefficient(
     model::GenericModel{T},
@@ -299,3 +319,85 @@ function _set_objective_coefficient(
     )
     return
 end
+
+"""
+    set_objective_coefficient(
+        model::GenericModel{T},
+        variable_1::GenericVariableRef{T},
+        variable_1::GenericVariableRef{T},
+        coefficient::Real,
+    ) where {T}
+
+Set the quadratic objective coefficient associated with `variable_1` and
+`variable_2` to `coefficient`.
+
+Note: this function will throw an error if a nonlinear objective is set.
+
+## Example
+
+```jldoctest
+julia> model = Model();
+
+julia> @variable(model, x[1:2]);
+
+julia> @objective(model, Min, x[1]^2 + x[1] * x[2])
+x[1]² + x[1]*x[2]
+
+julia> set_objective_coefficient(model, x[1], x[1], 2)
+
+julia> set_objective_coefficient(model, x[1], x[2], 3)
+
+julia> objective_function(model)
+2 x[1]² + 3 x[1]*x[2]
+```
+"""
+function set_objective_coefficient(
+    model::GenericModel{T},
+    variable_1::GenericVariableRef{T},
+    variable_2::GenericVariableRef{T},
+    coeff::Real,
+) where {T}
+    if _nlp_objective_function(model) !== nothing
+        error("A nonlinear objective is already set in the model")
+    end
+    coeff_t = convert(T, coeff)::T
+    F = moi_function_type(objective_function_type(model))
+    _set_objective_coefficient(model, variable_1, variable_2, coeff_t, F)
+    model.is_model_dirty = true
+    return
+end
+
+function _set_objective_coefficient(
+    model::GenericModel{T},
+    variable_1::GenericVariableRef{T},
+    variable_2::GenericVariableRef{T},
+    coeff::T,
+    ::Type{F},
+) where {T,F}
+    current_obj = objective_function(model)
+    new_obj = add_to_expression!(coeff * variable_1 * variable_2, current_obj)
+    set_objective_function(model, new_obj)
+    return
+end
+
+function _set_objective_coefficient(
+    model::GenericModel{T},
+    variable_1::GenericVariableRef{T},
+    variable_2::GenericVariableRef{T},
+    coeff::T,
+    ::Type{MOI.ScalarQuadraticFunction{T}},
+) where {T}
+    if variable_1 == variable_2
+        coeff *= T(2)
+    end
+    MOI.modify(
+        backend(model),
+        MOI.ObjectiveFunction{MOI.ScalarQuadraticFunction{T}}(),
+        MOI.ScalarQuadraticCoefficientChange(
+            index(variable_1),
+            index(variable_2),
+            coeff,
+        ),
+    )
+    return
+end

test/test_constraint.jl

@@ -1793,4 +1793,17 @@ function test_indicator_error()
     return
 end
 
+function test_set_normalized_coefficient_quadratic()
+    model = Model()
+    @variable(model, x[1:2])
+    @constraint(model, con, 2x[1]^2 + 3 * x[1] * x[2] + x[2] <= 2)
+    @test normalized_coefficient(con, x[1], x[1]) == 2.0
+    @test normalized_coefficient(con, x[1], x[2]) == 3.0
+    set_normalized_coefficient(con, x[1], x[1], 4)
+    set_normalized_coefficient(con, x[1], x[2], 5)
+    @test normalized_coefficient(con, x[1], x[1]) == 4.0
+    @test normalized_coefficient(con, x[1], x[2]) == 5.0
+    return
+end
+
 end  # module