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Move set_normalized_coefficient to src/variables.jl to better type arguments #3661

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Jan 29, 2024
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Move set_normalized_coefficient to src/variables.jl to better type arguments #3661

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227 changes: 0 additions & 227 deletions src/constraints.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -730,233 +730,6 @@ function add_constraint(
return con_ref
end

"""
set_normalized_coefficient(
constraint::ConstraintRef,
variable::GenericVariableRef,
value,
)

Set the coefficient of `variable` 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 + 3x <= 2`,
`set_normalized_coefficient(con, x, 4)` will create the constraint `4x <= 2`.

## Example

```jldoctest; filter=r"≤|<="
julia> model = Model();

julia> @variable(model, x)
x

julia> @constraint(model, con, 2x + 3x <= 2)
con : 5 x ≤ 2

julia> set_normalized_coefficient(con, x, 4)

julia> con
con : 4 x ≤ 2
```
"""
function set_normalized_coefficient(
con_ref::ConstraintRef{<:AbstractModel,MOI.ConstraintIndex{F,S}},
variable,
value,
) where {
S,
T,
F<:Union{MOI.ScalarAffineFunction{T},MOI.ScalarQuadraticFunction{T}},
}
model = owner_model(con_ref)
MOI.modify(
backend(model),
index(con_ref),
MOI.ScalarCoefficientChange(index(variable), convert(T, value)),
)
model.is_model_dirty = true
return
end

"""
set_normalized_coefficients(
con_ref::ConstraintRef,
variable,
new_coefficients::Vector{Tuple{Int64,T}},
)

Set the coefficients of `variable` in the constraint `con_ref` to
`new_coefficients`, where each element in `new_coefficients` is a tuple which
maps the row to a new coefficient.

Note that prior to this step, during constraint creation, JuMP will aggregate
multiple terms containing the same variable.

## Example

```jldoctest; filter=r"≤|<="
julia> model = Model();

julia> @variable(model, x)
x

julia> @constraint(model, con, [2x + 3x, 4x] in MOI.Nonnegatives(2))
con : [5 x, 4 x] ∈ MathOptInterface.Nonnegatives(2)

julia> set_normalized_coefficients(con, x, [(1, 2.0), (2, 5.0)])

julia> con
con : [2 x, 5 x] ∈ MathOptInterface.Nonnegatives(2)
```
"""
function set_normalized_coefficients(
con_ref::ConstraintRef{<:AbstractModel,<:MOI.ConstraintIndex{F}},
variable,
new_coefficients::Vector{Tuple{Int64,T}},
) where {T,F<:Union{MOI.VectorAffineFunction{T},MOI.VectorQuadraticFunction{T}}}
model = owner_model(con_ref)
MOI.modify(
backend(model),
index(con_ref),
MOI.MultirowChange(index(variable), new_coefficients),
)
model.is_model_dirty = true
return
end

"""
normalized_coefficient(
constraint::ConstraintRef,
variable::GenericVariableRef,
)

Return the coefficient associated with `variable` 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)
x

julia> @constraint(model, con, 2x + 3x <= 2)
con : 5 x ≤ 2

julia> normalized_coefficient(con, x)
5.0
```
"""
function normalized_coefficient(
con_ref::ConstraintRef{<:AbstractModel,<:MOI.ConstraintIndex{F}},
variable,
) where {F<:Union{MOI.ScalarAffineFunction,MOI.ScalarQuadraticFunction}}
return coefficient(constraint_object(con_ref).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)

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