# Complex number support

JuMP has a limited range of support for complex-valued variables and constraints. Since no current solvers natively support complex-valued variables and constraints, JuMP reformulates all complex expressions into their real and imaginary parts.

## Complex-valued variables

Create a complex-valued variable using ComplexPlane:

julia> model = Model();

julia> @variable(model, x in ComplexPlane())
real(x) + (0.0 + 1.0im) imag(x)

Note that x is not a VariableRef; instead, it is an affine expression with Complex{Float64}-valued coefficients:

julia> typeof(x)
GenericAffExpr{ComplexF64, VariableRef}

Behind the scenes, JuMP has created two real-valued variables, with names "real(x)" and "imag(x)":

julia> all_variables(model)
2-element Vector{VariableRef}:
real(x)
imag(x)

julia> name.(all_variables(model))
2-element Vector{String}:
"real(x)"
"imag(x)"

Use the real and imag functions on x to return a real-valued affine expression representing each variable:

julia> typeof(real(x))
AffExpr (alias for GenericAffExpr{Float64, VariableRef})

julia> typeof(imag(x))
AffExpr (alias for GenericAffExpr{Float64, VariableRef})

To create an anonymous variable, use the set keyword argument:

julia> model = Model();

julia> x = @variable(model, set = ComplexPlane())
_ + (0.0 + 1.0im) _

## Complex-valued variable bounds

Because complex-valued variables lack a total ordering, the definition of a variable bound for a complex-valued variable is ambiguous. If you pass a real- or complex-valued argument to keywords such as lower_bound, upper_bound, and start_value, JuMP will apply the real and imaginary parts to the associated real-valued variables.

julia> model = Model();

julia> @variable(
model,
x in ComplexPlane(),
lower_bound = 1.0,
upper_bound = 2.0 + 3.0im,
start = 4im,
)
real(x) + (0.0 + 1.0im) imag(x)

julia> vars = all_variables(model)
2-element Vector{VariableRef}:
real(x)
imag(x)

julia> lower_bound.(vars)
2-element Vector{Float64}:
1.0
0.0

julia> upper_bound.(vars)
2-element Vector{Float64}:
2.0
3.0

julia> start_value.(vars)
2-element Vector{Float64}:
0.0
4.0

## Complex-valued equality constraints

JuMP reformulates complex-valued equality constraints into two real-valued constraints: one representing the real part, and one representing the imaginary part. Thus, complex-valued equality constraints can be solved any solver that supports the real-valued constraint type.

For example:

julia> model = Model(HiGHS.Optimizer);

julia> set_silent(model)

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

julia> @constraint(model, (1 + 2im) * x + 3 * x == 4 + 5im)
(1.0 + 2.0im) x + (3.0 + 0.0im) x = 4.0 + 5.0im

julia> optimize!(model)

julia> value.(x)
2-element Vector{Float64}:
2.5
0.5

is equivalent to

julia> model = Model(HiGHS.Optimizer);

julia> set_silent(model)

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

julia> @constraint(model, 1 * x + 3 * x == 4)  # real component
x + 3 x = 4.0

julia> @constraint(model, 2 * x == 5)  # imag component
2 x = 5.0

julia> optimize!(model)

julia> value.(x)
2-element Vector{Float64}:
2.5
0.5

This also applies if the variables are complex-valued:

julia> model = Model(HiGHS.Optimizer);

julia> set_silent(model)

julia> @variable(model, x in ComplexPlane());

julia> @constraint(model, (1 + 2im) * x + 3 * x == 4 + 5im)
(4.0 + 2.0im) real(x) + (-2.0 + 4.0im) imag(x) = 4.0 + 5.0im

julia> optimize!(model)

julia> value(x)
1.3 + 0.6000000000000001im

which is equivalent to

julia> model = Model(HiGHS.Optimizer);

julia> set_silent(model)

julia> @variable(model, x_real);

julia> @variable(model, x_imag);

julia> @constraint(model, x_real - 2 * x_imag + 3 * x_real == 4)
4 x_real - 2 x_imag = 4.0

julia> @constraint(model, x_imag + 2 * x_real + 3 * x_imag == 5)
2 x_real + 4 x_imag = 5.0

julia> optimize!(model)

julia> value(x_real) + value(x_imag) * im
1.3 + 0.6000000000000001im

## Hermitian PSD Cones

JuMP supports creating matrices where are Hermitian.

julia> model = Model();

julia> @variable(model, H[1:3, 1:3] in HermitianPSDCone())
3×3 Matrix{GenericAffExpr{ComplexF64, VariableRef}}:
real(H[1,1])                                …  real(H[1,3]) + (0.0 + 1.0im) imag(H[1,3])
real(H[1,2]) + (-0.0 - 1.0im) imag(H[1,2])     real(H[2,3]) + (0.0 + 1.0im) imag(H[2,3])
real(H[1,3]) + (-0.0 - 1.0im) imag(H[1,3])     real(H[3,3])

Behind the scenes, JuMP has created nine real-valued decision variables:

julia> all_variables(model)
9-element Vector{VariableRef}:
real(H[1,1])
real(H[1,2])
real(H[2,2])
real(H[1,3])
real(H[2,3])
real(H[3,3])
imag(H[1,2])
imag(H[1,3])
imag(H[2,3])

and a Vector{VariableRef}-in-MOI.HermitianPositiveSemidefiniteConeTriangle constraint:

julia> num_constraints(model, Vector{VariableRef}, MOI.HermitianPositiveSemidefiniteConeTriangle)
1

The MOI.HermitianPositiveSemidefiniteConeTriangle set can be efficiently bridged to MOI.PositiveSemidefiniteConeTriangle, so it can be solved by any solver that supports PSD constraints.

Each element of H is an affine expression with Complex{Float64}-valued coefficients:

julia> typeof(H[1, 1])
GenericAffExpr{ComplexF64, VariableRef}

julia> typeof(H[2, 1])
GenericAffExpr{ComplexF64, VariableRef}

## Hermitian PSD constraints

The HermitianPSDCone can also be used in the @constraint macro:

julia> model = Model();

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

julia> import LinearAlgebra

julia> H = LinearAlgebra.Hermitian([x 1im; -1im -x])
2×2 LinearAlgebra.Hermitian{GenericAffExpr{ComplexF64, VariableRef}, Matrix{GenericAffExpr{ComplexF64, VariableRef}}}:
x           (0.0 + 1.0im)
(0.0 - 1.0im)  (-1.0 - 0.0im) x

julia> @constraint(model, H in HermitianPSDCone())
[x           (0.0 + 1.0im);
(0.0 - 1.0im)  (-1.0 + 0.0im) x] ∈ HermitianPSDCone()