Constraints

JuMP is based on the MathOptInterface (MOI) API. Because of this, JuMP uses the following standard form to represent problems:

\[\begin{align} & \min_{x \in \mathbb{R}^n} & f_0(x) \\ & \;\;\text{s.t.} & f_i(x) & \in \mathcal{S}_i & i = 1 \ldots m \end{align}\]

Each constraint, $f_i(x) \in \mathcal{S}_i$, is composed of a function and a set. For example, instead of calling $a^\top x \le b$ a less-than-or-equal-to constraint, we say that it is a scalar-affine-in-less-than constraint, where the function $a^\top x$ belongs to the less-than set $(-\infty, b]$. We use the shorthand function-in-set to refer to constraints composed of different types of functions and sets.

This page explains how to write various types of constraints in JuMP. For nonlinear constraints, see Nonlinear Modeling instead.

Add a constraint

Add a constraint to a JuMP model using the @constraint macro. The syntax to use depends on the type of constraint you wish to add.

Add a linear constraint

Create linear constraints using the @constraint macro:

julia> model = Model();

julia> @variable(model, x[1:3]);

julia> @constraint(model, c1, sum(x) <= 1)
c1 : x[1] + x[2] + x[3] ≤ 1

julia> @constraint(model, c2, x[1] + 2 * x[3] >= 2)
c2 : x[1] + 2 x[3] ≥ 2

julia> @constraint(model, c3, sum(i * x[i] for i in 1:3) == 3)
c3 : x[1] + 2 x[2] + 3 x[3] = 3

julia> @constraint(model, c4, 4 <= 2 * x[2] <= 5)
c4 : 2 x[2] ∈ [4, 5]

Normalization

JuMP normalizes constraints by moving all of the terms containing variables to the left-hand side and all of the constant terms to the right-hand side. Thus, we get:

julia> model = Model();

julia> @variable(model, x);

julia> @constraint(model, c, 2x + 1 <= 4x + 4)
c : -2 x ≤ 3

Add a quadratic constraint

In addition to affine functions, JuMP also supports constraints with quadratic terms. For example:

julia> model = Model();

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

julia> @variable(model, t >= 0)
t

julia> @constraint(model, my_q, x[1]^2 + x[2]^2 <= t^2)
my_q : x[1]² + x[2]² - t² ≤ 0
Tip

Because solvers can take advantage of the knowledge that a constraint is quadratic, prefer adding quadratic constraints using @constraint, rather than @NLconstraint.

Vectorized constraints

You can also add constraints to JuMP using vectorized linear algebra. For example:

julia> model = Model();

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

julia> A = [1 2; 3 4]
2×2 Matrix{Int64}:
 1  2
 3  4

julia> b = [5, 6]
2-element Vector{Int64}:
 5
 6

julia> @constraint(model, con_vector, A * x == b)
con_vector : [x[1] + 2 x[2] - 5, 3 x[1] + 4 x[2] - 6] ∈ MathOptInterface.Zeros(2)

julia> @constraint(model, con_scalar, A * x .== b)
2-element Vector{ConstraintRef{Model, MathOptInterface.ConstraintIndex{MathOptInterface.ScalarAffineFunction{Float64}, MathOptInterface.EqualTo{Float64}}, ScalarShape}}:
 con_scalar : x[1] + 2 x[2] = 5
 con_scalar : 3 x[1] + 4 x[2] = 6

The two constraints, == and .== are similar, but subtly different. The first creates a single constraint that is a MOI.VectorAffineFunction in MOI.Zeros constraint. The second creates a vector of MOI.ScalarAffineFunction in MOI.EqualTo constraints.

Which formulation to choose depends on the solver, and what you want to do with the constraint object con_vector or con_scalar.

  • If you are using a conic solver, expect the dual of con_vector to be a Vector{Float64}, and do not intend to delete a row in the constraint, choose the == formulation.
  • If you are using a solver that expects a list of scalar constraints, for example HiGHS, or you wish to delete part of the constraint or access a single row of the constraint, for example, dual(con_scalar[2]), then use the broadcast .==.

JuMP reformulates both constraints into the other form if needed by the solver, but choosing the right format for a particular solver is more efficient.

You can also use <=, .<= , >=, and .>= as comparison operators in the constraint.

julia> @constraint(model, A * x <= b)
[x[1] + 2 x[2] - 5, 3 x[1] + 4 x[2] - 6] ∈ MathOptInterface.Nonpositives(2)

julia> @constraint(model, A * x .<= b)
2-element Vector{ConstraintRef{Model, MathOptInterface.ConstraintIndex{MathOptInterface.ScalarAffineFunction{Float64}, MathOptInterface.LessThan{Float64}}, ScalarShape}}:
 x[1] + 2 x[2] ≤ 5
 3 x[1] + 4 x[2] ≤ 6

julia> @constraint(model, A * x >= b)
[x[1] + 2 x[2] - 5, 3 x[1] + 4 x[2] - 6] ∈ MathOptInterface.Nonnegatives(2)

julia> @constraint(model, A * x .>= b)
2-element Vector{ConstraintRef{Model, MathOptInterface.ConstraintIndex{MathOptInterface.ScalarAffineFunction{Float64}, MathOptInterface.GreaterThan{Float64}}, ScalarShape}}:
 x[1] + 2 x[2] ≥ 5
 3 x[1] + 4 x[2] ≥ 6

Vectorized matrix constraints

In most cases, you cannot use the non-broadcasting syntax for general matrices. For example:

julia> model = Model();

julia> @variable(model, X[1:2, 1:2])
2×2 Matrix{VariableRef}:
 X[1,1]  X[1,2]
 X[2,1]  X[2,2]

julia> @constraint(model, X >= 0)
ERROR: At none:1: `@constraint(model, X >= 0)`: Unsupported matrix in vector-valued set. Did you mean to use the broadcasting syntax `.>=` instead? Alternatively, perhaps you are missing a set argument like `@constraint(model, X >= 0, PSDCone())` or `@constraint(model, X >= 0, HermmitianPSDCone())`.
Stacktrace:
[...]

Instead, to represent matrix inequalities you must always use the element-wise broadcasting .==, .>=, or .<=, or use the Set inequality syntax.

There are two exceptions: if the result of the left-hand side minus the right-hand side is a LinearAlgebra.Symmetric matrix or a LinearAlgebra.Hermitian matrix, you may use the non-broadcasting equality syntax:

julia> using LinearAlgebra

julia> model = Model();

julia> @variable(model, X[1:2, 1:2], Symmetric)
2×2 Symmetric{VariableRef, Matrix{VariableRef}}:
 X[1,1]  X[1,2]
 X[1,2]  X[2,2]

julia> @constraint(model, X == LinearAlgebra.I)
[X[1,1] - 1  X[1,2];
 X[1,2]      X[2,2] - 1] ∈ Zeros()

Despite the model showing the matrix in Zeros, this will add only three rows to the constraint matrix because the symmetric constraints are redundant. In contrast, the broadcasting syntax adds four linear constraints:

julia> @constraint(model, X .== LinearAlgebra.I)
2×2 Matrix{ConstraintRef{Model, MathOptInterface.ConstraintIndex{MathOptInterface.ScalarAffineFunction{Float64}, MathOptInterface.EqualTo{Float64}}, ScalarShape}}:
 X[1,1] = 1  X[1,2] = 0
 X[1,2] = 0  X[2,2] = 1

The same holds for LinearAlgebra.Hermitian matrices:

julia> using LinearAlgebra

julia> model = Model();

julia> @variable(model, X[1:2, 1:2] in HermitianPSDCone())
2×2 Hermitian{GenericAffExpr{ComplexF64, VariableRef}, Matrix{GenericAffExpr{ComplexF64, VariableRef}}}:
 real(X[1,1])                    real(X[1,2]) + imag(X[1,2]) im
 real(X[1,2]) - imag(X[1,2]) im  real(X[2,2])

julia> @constraint(model, X == LinearAlgebra.I)
[real(X[1,1]) - 1                real(X[1,2]) + imag(X[1,2]) im;
 real(X[1,2]) - imag(X[1,2]) im  real(X[2,2]) - 1] ∈ Zeros()

julia> @constraint(model, X .== LinearAlgebra.I)
2×2 Matrix{ConstraintRef{Model, MathOptInterface.ConstraintIndex{MathOptInterface.ScalarAffineFunction{ComplexF64}, MathOptInterface.EqualTo{ComplexF64}}, ScalarShape}}:
 real(X[1,1]) = 1                    real(X[1,2]) + imag(X[1,2]) im = 0
 real(X[1,2]) - imag(X[1,2]) im = 0  real(X[2,2]) = 1

Containers of constraints

The @constraint macro supports creating collections of constraints. We'll cover some brief syntax here; read the Constraint containers section for more details:

Create arrays of constraints:

julia> model = Model();

julia> @variable(model, x[1:3]);

julia> @constraint(model, c[i=1:3], x[i] <= i^2)
3-element Vector{ConstraintRef{Model, MathOptInterface.ConstraintIndex{MathOptInterface.ScalarAffineFunction{Float64}, MathOptInterface.LessThan{Float64}}, ScalarShape}}:
 c[1] : x[1] ≤ 1
 c[2] : x[2] ≤ 4
 c[3] : x[3] ≤ 9

julia> c[2]
c[2] : x[2] ≤ 4

Sets can be any Julia type that supports iteration:

julia> model = Model();

julia> @variable(model, x[1:3]);

julia> @constraint(model, c[i=2:3, ["red", "blue"]], x[i] <= i^2)
2-dimensional DenseAxisArray{ConstraintRef{Model, MathOptInterface.ConstraintIndex{MathOptInterface.ScalarAffineFunction{Float64}, MathOptInterface.LessThan{Float64}}, ScalarShape},2,...} with index sets:
    Dimension 1, 2:3
    Dimension 2, ["red", "blue"]
And data, a 2×2 Matrix{ConstraintRef{Model, MathOptInterface.ConstraintIndex{MathOptInterface.ScalarAffineFunction{Float64}, MathOptInterface.LessThan{Float64}}, ScalarShape}}:
 c[2,red] : x[2] ≤ 4  c[2,blue] : x[2] ≤ 4
 c[3,red] : x[3] ≤ 9  c[3,blue] : x[3] ≤ 9

julia> c[2, "red"]
c[2,red] : x[2] ≤ 4

Sets can depend upon previous indices:

julia> model = Model();

julia> @variable(model, x[1:3]);

julia> @constraint(model, c[i=1:3, j=i:3], x[i] <= j)
JuMP.Containers.SparseAxisArray{ConstraintRef{Model, MathOptInterface.ConstraintIndex{MathOptInterface.ScalarAffineFunction{Float64}, MathOptInterface.LessThan{Float64}}, ScalarShape}, 2, Tuple{Int64, Int64}} with 6 entries:
  [1, 1]  =  c[1,1] : x[1] ≤ 1
  [1, 2]  =  c[1,2] : x[1] ≤ 2
  [1, 3]  =  c[1,3] : x[1] ≤ 3
  [2, 2]  =  c[2,2] : x[2] ≤ 2
  [2, 3]  =  c[2,3] : x[2] ≤ 3
  [3, 3]  =  c[3,3] : x[3] ≤ 3

and you can filter elements in the sets using the ; syntax:

julia> model = Model();

julia> @variable(model, x[1:9]);

julia> @constraint(model, c[i=1:9; mod(i, 3) == 0], x[i] <= i)
JuMP.Containers.SparseAxisArray{ConstraintRef{Model, MathOptInterface.ConstraintIndex{MathOptInterface.ScalarAffineFunction{Float64}, MathOptInterface.LessThan{Float64}}, ScalarShape}, 1, Tuple{Int64}} with 3 entries:
  [3]  =  c[3] : x[3] ≤ 3
  [6]  =  c[6] : x[6] ≤ 6
  [9]  =  c[9] : x[9] ≤ 9

Registered constraints

When you create constraints, JuMP registers them inside the model using their corresponding symbol. Get a registered name using model[:key]:

julia> model = Model()
A JuMP Model
Feasibility problem with:
Variables: 0
Model mode: AUTOMATIC
CachingOptimizer state: NO_OPTIMIZER
Solver name: No optimizer attached.

julia> @variable(model, x)
x

julia> @constraint(model, my_c, 2x <= 1)
my_c : 2 x ≤ 1

julia> model
A JuMP Model
Feasibility problem with:
Variable: 1
`AffExpr`-in-`MathOptInterface.LessThan{Float64}`: 1 constraint
Model mode: AUTOMATIC
CachingOptimizer state: NO_OPTIMIZER
Solver name: No optimizer attached.
Names registered in the model: my_c, x

julia> model[:my_c] === my_c
true

Anonymous constraints

To reduce the likelihood of accidental bugs, and because JuMP registers constraints inside a model, creating two constraints with the same name is an error:

julia> model = Model();

julia> @variable(model, x)
x

julia> @constraint(model, c, 2x <= 1)
c : 2 x ≤ 1

julia> @constraint(model, c, 2x <= 1)
ERROR: An object of name c is already attached to this model. If this
    is intended, consider using the anonymous construction syntax, for example,
    `x = @variable(model, [1:N], ...)` where the name of the object does
    not appear inside the macro.

    Alternatively, use `unregister(model, :c)` to first unregister
    the existing name from the model. Note that this will not delete the
    object; it will just remove the reference at `model[:c]`.
[...]

A common reason for encountering this error is adding constraints in a loop.

As a work-around, JuMP provides anonymous constraints. Create an anonymous constraint by omitting the name argument:

julia> model = Model();

julia> @variable(model, x);

julia> c = @constraint(model, 2x <= 1)
2 x ≤ 1

Create a container of anonymous constraints by dropping the name in front of the [:

julia> model = Model();

julia> @variable(model, x[1:3]);

julia> c = @constraint(model, [i = 1:3], x[i] <= i)
3-element Vector{ConstraintRef{Model, MathOptInterface.ConstraintIndex{MathOptInterface.ScalarAffineFunction{Float64}, MathOptInterface.LessThan{Float64}}, ScalarShape}}:
 x[1] ≤ 1
 x[2] ≤ 2
 x[3] ≤ 3

Constraint names

In addition to the symbol that constraints are registered with, constraints have a String name that is used for printing and writing to file formats.

Get and set the name of a constraint using name(::JuMP.ConstraintRef) and set_name(::JuMP.ConstraintRef, ::String):

julia> model = Model(); @variable(model, x);

julia> @constraint(model, con, x <= 1)
con : x ≤ 1

julia> name(con)
"con"

julia> set_name(con, "my_con_name")

julia> con
my_con_name : x ≤ 1

Override the default choice of name using the base_name keyword:

julia> model = Model(); @variable(model, x);

julia> con = @constraint(model, [i=1:2], x <= i, base_name = "my_con")
2-element Vector{ConstraintRef{Model, MathOptInterface.ConstraintIndex{MathOptInterface.ScalarAffineFunction{Float64}, MathOptInterface.LessThan{Float64}}, ScalarShape}}:
 my_con[1] : x ≤ 1
 my_con[2] : x ≤ 2

Note that names apply to each element of the container, not to the container of constraints:

julia> name(con[1])
"my_con[1]"

julia> set_name(con[1], "c")

julia> con
2-element Vector{ConstraintRef{Model, MathOptInterface.ConstraintIndex{MathOptInterface.ScalarAffineFunction{Float64}, MathOptInterface.LessThan{Float64}}, ScalarShape}}:
 c : x ≤ 1
 my_con[2] : x ≤ 2
Tip

For some models, setting the string name of each constraint can take a non-trivial portion of the total time required to build the model. Turn off String names by passing set_string_name = false to @constraint:

julia> model = Model();

julia> @variable(model, x);

julia> @constraint(model, con, x <= 2, set_string_name = false)
x ≤ 2

See Disable string names for more information.

Retrieve a constraint by name

Retrieve a constraint from a model using constraint_by_name:

julia> constraint_by_name(model, "c")
c : x ≤ 1

If the name is not present, nothing will be returned:

julia> constraint_by_name(model, "bad_name")

You can only look up individual constraints using constraint_by_name. Something like this will not work:

julia> model = Model(); @variable(model, x);

julia> con = @constraint(model, [i=1:2], x <= i, base_name = "my_con")
2-element Vector{ConstraintRef{Model, MathOptInterface.ConstraintIndex{MathOptInterface.ScalarAffineFunction{Float64}, MathOptInterface.LessThan{Float64}}, ScalarShape}}:
 my_con[1] : x ≤ 1
 my_con[2] : x ≤ 2

julia> constraint_by_name(model, "my_con")

To look up a collection of constraints, do not use constraint_by_name. Instead, register them using the model[:key] = value syntax:

julia> model = Model(); @variable(model, x);

julia> model[:con] = @constraint(model, [i=1:2], x <= i, base_name = "my_con")
2-element Vector{ConstraintRef{Model, MathOptInterface.ConstraintIndex{MathOptInterface.ScalarAffineFunction{Float64}, MathOptInterface.LessThan{Float64}}, ScalarShape}}:
 my_con[1] : x ≤ 1
 my_con[2] : x ≤ 2

julia> model[:con]
2-element Vector{ConstraintRef{Model, MathOptInterface.ConstraintIndex{MathOptInterface.ScalarAffineFunction{Float64}, MathOptInterface.LessThan{Float64}}, ScalarShape}}:
 my_con[1] : x ≤ 1
 my_con[2] : x ≤ 2

String names, symbolic names, and bindings

It's common for new users to experience confusion relating to constraints. Part of the problem is the difference between the name that a constraint is registered under and the String name used for printing.

Here's a summary of the differences:

  • Constraints are created using @constraint.
  • Constraints can be named or anonymous.
  • Named constraints have the form @constraint(model, c, expr). For named constraints:
    • The String name of the constraint is set to "c".
    • A Julia variable c is created that binds c to the JuMP constraint.
    • The name :c is registered as a key in the model with the value c.
  • Anonymous constraints have the form c = @constraint(model, expr). For anonymous constraints:
    • The String name of the constraint is set to "".
    • You control the name of the Julia variable used as the binding.
    • No name is registered as a key in the model.
  • The base_name keyword can override the String name of the constraint.
  • You can manually register names in the model via model[:key] = value.

Here's an example of the differences:

julia> model = Model();

julia> @variable(model, x)
x

julia> c_binding = @constraint(model, 2x <= 1, base_name = "c")
c : 2 x ≤ 1

julia> model
A JuMP Model
Feasibility problem with:
Variable: 1
`AffExpr`-in-`MathOptInterface.LessThan{Float64}`: 1 constraint
Model mode: AUTOMATIC
CachingOptimizer state: NO_OPTIMIZER
Solver name: No optimizer attached.
Names registered in the model: x

julia> c
ERROR: UndefVarError: `c` not defined

julia> c_binding
c : 2 x ≤ 1

julia> name(c_binding)
"c"

julia> model[:c_register] = c_binding
c : 2 x ≤ 1

julia> model
A JuMP Model
Feasibility problem with:
Variable: 1
`AffExpr`-in-`MathOptInterface.LessThan{Float64}`: 1 constraint
Model mode: AUTOMATIC
CachingOptimizer state: NO_OPTIMIZER
Solver name: No optimizer attached.
Names registered in the model: c_register, x

julia> model[:c_register]
c : 2 x ≤ 1

julia> model[:c_register] === c_binding
true

julia> c
ERROR: UndefVarError: `c` not defined

The @constraints macro

If you have many @constraint calls, use the @constraints macro to improve readability:

julia> model = Model();

julia> @variable(model, x);

julia> @constraints(model, begin
           2x <= 1
           c, x >= -1
       end)
(2 x ≤ 1, c : x ≥ -1)

julia> print(model)
Feasibility
Subject to
 c : x ≥ -1
 2 x ≤ 1

The @constraints macro returns a tuple of the constraints that were defined.

Duality

JuMP adopts the notion of conic duality from MathOptInterface. For linear programs, a feasible dual on a >= constraint is nonnegative and a feasible dual on a <= constraint is nonpositive. If the constraint is an equality constraint, it depends on which direction is binding.

Warning

JuMP's definition of duality is independent of the objective sense. That is, the sign of feasible duals associated with a constraint depends on the direction of the constraint and not whether the problem is maximization or minimization. This is a different convention from linear programming duality in some common textbooks. If you have a linear program, and you want the textbook definition, you probably want to use shadow_price and reduced_cost instead.

The dual value associated with a constraint in the most recent solution can be accessed using the dual function. Use has_duals to check if the model has a dual solution available to query. For example:

julia> model = Model(HiGHS.Optimizer);

julia> set_silent(model)

julia> @variable(model, x)
x

julia> @constraint(model, con, x <= 1)
con : x ≤ 1

julia> @objective(model, Min, -2x)
-2 x

julia> has_duals(model)
false

julia> optimize!(model)

julia> has_duals(model)
true

julia> dual(con)
-2.0

julia> @objective(model, Max, 2x)
2 x

julia> optimize!(model)

julia> dual(con)
-2.0

To help users who may be less familiar with conic duality, JuMP provides shadow_price, which returns a value that can be interpreted as the improvement in the objective in response to an infinitesimal relaxation (on the scale of one unit) in the right-hand side of the constraint. shadow_price can be used only on linear constraints with a <=, >=, or == comparison operator.

In the example above, dual(con) returned -2.0 regardless of the optimization sense. However, in the second case when the optimization sense is Max, shadow_price returns:

julia> shadow_price(con)
2.0

Duals of variable bounds

To query the dual variables associated with a variable bound, first obtain a constraint reference using one of UpperBoundRef, LowerBoundRef, or FixRef, and then call dual on the returned constraint reference. The reduced_cost function may simplify this process as it returns the shadow price of an active bound of a variable (or zero, if no active bound exists).

julia> model = Model(HiGHS.Optimizer);

julia> set_silent(model)

julia> @variable(model, x <= 1)
x

julia> @objective(model, Min, -2x)
-2 x

julia> optimize!(model)

julia> dual(UpperBoundRef(x))
-2.0

julia> reduced_cost(x)
-2.0

Modify a constant term

This section explains how to modify the constant term in a constraint. There are multiple ways to achieve this goal; we explain three options.

Option 1: change the right-hand side

Use set_normalized_rhs to modify the right-hand side (constant) term of a linear or quadratic constraint. Use normalized_rhs to query the right-hand side term.

julia> model = Model();

julia> @variable(model, x);

julia> @constraint(model, con, 2x <= 1)
con : 2 x ≤ 1

julia> set_normalized_rhs(con, 3)

julia> con
con : 2 x ≤ 3

julia> normalized_rhs(con)
3.0
Warning

set_normalized_rhs sets the right-hand side term of the normalized constraint. See Normalization for more details.

Option 2: use fixed variables

If constraints are complicated, for example, they are composed of a number of components, each of which has a constant term, then it may be difficult to calculate what the right-hand side term is in the standard form.

For this situation, JuMP includes the ability to fix variables to a value using the fix function. Fixing a variable sets its lower and upper bound to the same value. Thus, changes in a constant term can be simulated by adding a new variable and fixing it to different values. Here is an example:

julia> model = Model();

julia> @variable(model, x);

julia> @variable(model, const_term)
const_term

julia> @constraint(model, con, 2x <= const_term + 1)
con : 2 x - const_term ≤ 1

julia> fix(const_term, 1.0)

The constraint con is now equivalent to 2x <= 2.

Warning

Fixed variables are not replaced with constants when communicating the problem to a solver. Therefore, even though const_term is fixed, it is still a decision variable, and so const_term * x is bilinear.

Option 3: modify the function's constant term

The third option is to use add_to_function_constant. The constant given is added to the function of a func-in-set constraint. In the following example, adding 2 to the function has the effect of removing 2 to the right-hand side:

julia> model = Model();

julia> @variable(model, x);

julia> @constraint(model, con, 2x <= 1)
con : 2 x ≤ 1

julia> add_to_function_constant(con, 2)

julia> con
con : 2 x ≤ -1

julia> normalized_rhs(con)
-1.0

In the case of interval constraints, the constant is removed from each bound:

julia> model = Model();

julia> @variable(model, x);

julia> @constraint(model, con, 0 <= 2x + 1 <= 2)
con : 2 x ∈ [-1, 1]

julia> add_to_function_constant(con, 3)

julia> con
con : 2 x ∈ [-4, -2]

Modify a variable coefficient

Scalar constraints

To modify the coefficients for a linear term in a constraint, use set_normalized_coefficient. To query the current coefficient, use normalized_coefficient.

julia> model = Model();

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

julia> @constraint(model, con, 2x[1] + x[2] <= 1)
con : 2 x[1] + x[2] ≤ 1

julia> set_normalized_coefficient(con, x[2], 0)

julia> con
con : 2 x[1] ≤ 1

julia> normalized_coefficient(con, x[2])
0.0

To modify quadratic terms, pass two variables:

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 sets the coefficient of the normalized constraint. See Normalization for more details.

Vector constraints

To modify the coefficients of a vector-valued constraint, use set_normalized_coefficient.

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_coefficient(con, x, [(1, 3.0)])

julia> con
con : [3 x, 4 x] ∈ MathOptInterface.Nonnegatives(2)

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

julia> con
con : [2 x, 5 x] ∈ MathOptInterface.Nonnegatives(2)

Delete a constraint

Use delete to delete a constraint from a model. Use is_valid to check if a constraint belongs to a model and has not been deleted.

julia> model = Model();

julia> @variable(model, x);

julia> @constraint(model, con, 2x <= 1)
con : 2 x ≤ 1

julia> is_valid(model, con)
true

julia> delete(model, con)

julia> is_valid(model, con)
false

Deleting a constraint does not unregister the symbolic reference from the model. Therefore, creating a new constraint of the same name will throw an error:

julia> @constraint(model, con, 2x <= 1)
ERROR: An object of name con is already attached to this model. If this
    is intended, consider using the anonymous construction syntax, for example,
    `x = @variable(model, [1:N], ...)` where the name of the object does
    not appear inside the macro.

    Alternatively, use `unregister(model, :con)` to first unregister
    the existing name from the model. Note that this will not delete the
    object; it will just remove the reference at `model[:con]`.
[...]

After calling delete, call unregister to remove the symbolic reference:

julia> unregister(model, :con)

julia> @constraint(model, con, 2x <= 1)
con : 2 x ≤ 1
Info

delete does not automatically unregister because we do not distinguish between names that are automatically registered by JuMP macros, and names that are manually registered by the user by setting values in object_dictionary. In addition, deleting a constraint and then adding a new constraint of the same name is an easy way to introduce bugs into your code.

Start values

Provide a starting value (also called warmstart) for a constraint's primal and dual solutions using set_start_value and set_dual_start_value.

Query the starting value for a constraint's primal and dual solution using start_value and dual_start_value. If no start value has been set, the methods will return nothing.

julia> model = Model();

julia> @variable(model, x)
x

julia> @constraint(model, con, x >= 10)
con : x ≥ 10

julia> start_value(con)

julia> set_start_value(con, 10.0)

julia> start_value(con)
10.0

julia> dual_start_value(con)

julia> set_dual_start_value(con, 2)

julia> dual_start_value(con)
2.0

Vector-valued constraints require a vector:

julia> model = Model();

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

julia> @constraint(model, con, x in SecondOrderCone())
con : [x[1], x[2], x[3]] in MathOptInterface.SecondOrderCone(3)

julia> dual_start_value(con)

julia> set_dual_start_value(con, [1.0, 2.0, 3.0])

julia> dual_start_value(con)
3-element Vector{Float64}:
 1.0
 2.0
 3.0
Tip

To simplify setting start values for all variables and constraints in a model, see set_start_values. The Primal and dual warm-starts tutorial also gives a detailed description of how to iterate over constraints in the model to set custom start values.

Constraint containers

Like Variable containers, JuMP provides a mechanism for building groups of constraints compactly. References to these groups of constraints are returned in containers. Three types of constraint containers are supported: Arrays, DenseAxisArrays, and SparseAxisArrays. We explain each of these in the following.

Tip

You can read more about containers in the Containers section.

Arrays

One way of adding a group of constraints compactly is the following:

julia> model = Model();

julia> @variable(model, x);

julia> @constraint(model, con[i = 1:3], i * x <= i + 1)
3-element Vector{ConstraintRef{Model, MathOptInterface.ConstraintIndex{MathOptInterface.ScalarAffineFunction{Float64}, MathOptInterface.LessThan{Float64}}, ScalarShape}}:
 con[1] : x ≤ 2
 con[2] : 2 x ≤ 3
 con[3] : 3 x ≤ 4

JuMP returns references to the three constraints in an Array that is bound to the Julia variable con. This array can be accessed and sliced as you would with any Julia array:

julia> con[1]
con[1] : x ≤ 2

julia> con[2:3]
2-element Vector{ConstraintRef{Model, MathOptInterface.ConstraintIndex{MathOptInterface.ScalarAffineFunction{Float64}, MathOptInterface.LessThan{Float64}}, ScalarShape}}:
 con[2] : 2 x ≤ 3
 con[3] : 3 x ≤ 4

Anonymous containers can also be constructed by dropping the name (for example, con) before the square brackets:

julia> con = @constraint(model, [i = 1:2], i * x <= i + 1)
2-element Vector{ConstraintRef{Model, MathOptInterface.ConstraintIndex{MathOptInterface.ScalarAffineFunction{Float64}, MathOptInterface.LessThan{Float64}}, ScalarShape}}:
 x ≤ 2
 2 x ≤ 3

Just like @variable, JuMP will form an Array of constraints when it can determine at parse time that the indices are one-based integer ranges. Therefore con[1:b] will create an Array, but con[a:b] will not. A special case is con[Base.OneTo(n)] which will produce an Array. If JuMP cannot determine that the indices are one-based integer ranges (for example, in the case of con[a:b]), JuMP will create a DenseAxisArray instead.

DenseAxisArrays

The syntax for constructing a DenseAxisArray of constraints is very similar to the syntax for constructing a DenseAxisArray of variables.

julia> model = Model();

julia> @variable(model, x);

julia> @constraint(model, con[i = 1:2, j = 2:3], i * x <= j + 1)
2-dimensional DenseAxisArray{ConstraintRef{Model, MathOptInterface.ConstraintIndex{MathOptInterface.ScalarAffineFunction{Float64}, MathOptInterface.LessThan{Float64}}, ScalarShape},2,...} with index sets:
    Dimension 1, Base.OneTo(2)
    Dimension 2, 2:3
And data, a 2×2 Matrix{ConstraintRef{Model, MathOptInterface.ConstraintIndex{MathOptInterface.ScalarAffineFunction{Float64}, MathOptInterface.LessThan{Float64}}, ScalarShape}}:
 con[1,2] : x ≤ 3    con[1,3] : x ≤ 4
 con[2,2] : 2 x ≤ 3  con[2,3] : 2 x ≤ 4

SparseAxisArrays

The syntax for constructing a SparseAxisArray of constraints is very similar to the syntax for constructing a SparseAxisArray of variables.

julia> model = Model();

julia> @variable(model, x);

julia> @constraint(model, con[i = 1:2, j = 1:2; i != j], i * x <= j + 1)
JuMP.Containers.SparseAxisArray{ConstraintRef{Model, MathOptInterface.ConstraintIndex{MathOptInterface.ScalarAffineFunction{Float64}, MathOptInterface.LessThan{Float64}}, ScalarShape}, 2, Tuple{Int64, Int64}} with 2 entries:
  [1, 2]  =  con[1,2] : x ≤ 3
  [2, 1]  =  con[2,1] : 2 x ≤ 2
Warning

If you have many index dimensions and a large amount of sparsity, read Performance considerations.

Forcing the container type

When creating a container of constraints, JuMP will attempt to choose the tightest container type that can store the constraints. However, because this happens at parse time, it does not always make the best choice. Just like in @variable, you can force the type of container using the container keyword. For syntax and the reason behind this, take a look at the variable docs.

Constraints with similar indices

Containers are often used to create constraints over a set of indices. However, you'll often have cases in which you are repeating the indices:

julia> model = Model();

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

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

julia> @constraints(model, begin
           [i=1:2, j=1:2, k=1:2], i * x[j] <= k
           [i=1:2, j=1:2, k=1:2], i * y[j] <= k
       end);

This is hard to read and leads to a lot of copy-paste. A more readable way is to use a for-loop:

julia> for i=1:2, j=1:2, k=1:2
           @constraints(model, begin
               i * x[j] <= k
               i * y[j] <= k
           end)
       end

Accessing constraints from a model

Query the types of function-in-set constraints in a model using list_of_constraint_types:

julia> model = Model();

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

julia> @constraint(model, x[1] + x[2] <= 1);

julia> list_of_constraint_types(model)
3-element Vector{Tuple{Type, Type}}:
 (AffExpr, MathOptInterface.LessThan{Float64})
 (VariableRef, MathOptInterface.GreaterThan{Float64})
 (VariableRef, MathOptInterface.Integer)

For a given combination of function and set type, use num_constraints to access the number of constraints and all_constraints to access a list of their references:

julia> num_constraints(model, VariableRef, MOI.Integer)
2

julia> cons = all_constraints(model, VariableRef, MOI.Integer)
2-element Vector{ConstraintRef{Model, MathOptInterface.ConstraintIndex{MathOptInterface.VariableIndex, MathOptInterface.Integer}, ScalarShape}}:
 x[1] integer
 x[2] integer

You can also count the total number of constraints in the model, but you must explicitly choose whether to count VariableRef constraints such as bound and integrality constraints:

julia> num_constraints(model; count_variable_in_set_constraints = true)
5

julia> num_constraints(model; count_variable_in_set_constraints = false)
1

The same also applies for all_constraints:

julia> all_constraints(model; include_variable_in_set_constraints = true)
5-element Vector{ConstraintRef}:
 x[1] + x[2] ≤ 1
 x[1] ≥ 1
 x[2] ≥ 2
 x[1] integer
 x[2] integer

julia> all_constraints(model; include_variable_in_set_constraints = false)
1-element Vector{ConstraintRef}:
 x[1] + x[2] ≤ 1

If you need finer-grained control on which constraints to include, use a variant of:

julia> sum(
           num_constraints(model, F, S) for
           (F, S) in list_of_constraint_types(model) if F != VariableRef
       )
1

Use constraint_object to get an instance of an AbstractConstraint object that stores the constraint data:

julia> con = constraint_object(cons[1])
ScalarConstraint{VariableRef, MathOptInterface.Integer}(x[1], MathOptInterface.Integer())

julia> con.func
x[1]

julia> con.set
MathOptInterface.Integer()

MathOptInterface constraints

Because JuMP is based on MathOptInterface, you can add any constraints supported by MathOptInterface using the function-in-set syntax. For a list of supported functions and sets, read Standard form problem.

Note

We use MOI as an alias for the MathOptInterface module. This alias is defined by using JuMP. You may also define it in your code as follows:

import MathOptInterface as MOI

For example, the following two constraints are equivalent:

julia> model = Model();

julia> @variable(model, x[1:3]);

julia> @constraint(model, 2 * x[1] <= 1)
2 x[1] ≤ 1

julia> @constraint(model, 2 * x[1] in MOI.LessThan(1.0))
2 x[1] ≤ 1

You can also use any set defined by MathOptInterface:

julia> @constraint(model, x - [1; 2; 3] in MOI.Nonnegatives(3))
[x[1] - 1, x[2] - 2, x[3] - 3] ∈ MathOptInterface.Nonnegatives(3)

julia> @constraint(model, x in MOI.ExponentialCone())
[x[1], x[2], x[3]] ∈ MathOptInterface.ExponentialCone()
Info

Similar to how JuMP defines the <= and >= syntax as a convenience way to specify MOI.LessThan and MOI.GreaterThan constraints, the remaining sections in this page describe functions and syntax that have been added for the convenience of common modeling situations.

Set inequality syntax

For modeling convenience, the syntax @constraint(model, x >= y, Set()) is short-hand for @constraint(model, x - y in Set()). Therefore, the following calls are equivalent:

julia> model = Model();

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

julia> y = [0.5, 0.75];

julia> @constraint(model, x >= y, MOI.Nonnegatives(2))
[x[1] - 0.5, x[2] - 0.75] ∈ MathOptInterface.Nonnegatives(2)

julia> @constraint(model, y <= x, MOI.Nonnegatives(2))
[x[1] - 0.5, x[2] - 0.75] ∈ MathOptInterface.Nonnegatives(2)

julia> @constraint(model, x - y in MOI.Nonnegatives(2))
[x[1] - 0.5, x[2] - 0.75] ∈ MathOptInterface.Nonnegatives(2)

Non-zero constants are not supported in this syntax:

julia> @constraint(model, x >= 1, MOI.Nonnegatives(2))
ERROR: Operation `sub_mul` between `Vector{VariableRef}` and `Int64` is not allowed. This most often happens when you write a constraint like `x >= y` where `x` is an array and `y` is a constant. Use the broadcast syntax `x .- y >= 0` instead.
Stacktrace:
[...]

Use instead:

julia> @constraint(model, x .- 1 >= 0, MOI.Nonnegatives(2))
[x[1] - 1, x[2] - 1] ∈ MathOptInterface.Nonnegatives(2)

Second-order cone constraints

A SecondOrderCone constrains the variables t and x to the set:

\[||x||_2 \le t,\]

and $t \ge 0$. It can be added as follows:

julia> model = Model();

julia> @variable(model, t)
t

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

julia> @constraint(model, [t; x] in SecondOrderCone())
[t, x[1], x[2]] ∈ MathOptInterface.SecondOrderCone(3)

Rotated second-order cone constraints

A RotatedSecondOrderCone constrains the variables t, u, and x to the set:

\[||x||_2^2 \le 2 t \cdot u\]

and $t, u \ge 0$. It can be added as follows:

julia> model = Model();

julia> @variable(model, t)
t

julia> @variable(model, u)
u

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

julia> @constraint(model, [t; u; x] in RotatedSecondOrderCone())
[t, u, x[1], x[2]] ∈ MathOptInterface.RotatedSecondOrderCone(4)

Special Ordered Sets of Type 1

In a Special Ordered Set of Type 1 (often denoted SOS-I or SOS1), at most one element can take a non-zero value.

Construct SOS-I constraints using the SOS1 set:

julia> model = Model();

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

julia> @constraint(model, x in SOS1())
[x[1], x[2], x[3]] in MathOptInterface.SOS1{Float64}([1.0, 2.0, 3.0])

Although not required for feasibility, solvers can benefit from an ordering of the variables (for example, the variables represent different factories to build, at most one factory can be built, and the factories can be ordered according to cost). To induce an ordering, a vector of weights can be provided, and the variables are ordered according to their corresponding weight.

For example, in the constraint:

julia> @constraint(model, x in SOS1([3.1, 1.2, 2.3]))
[x[1], x[2], x[3]] in MathOptInterface.SOS1{Float64}([3.1, 1.2, 2.3])

the variables x have precedence x[2], x[3], x[1].

Special Ordered Sets of Type 2

In a Special Ordered Set of Type 2 (SOS-II), at most two elements can be non-zero, and if there are two non-zeros, they must be consecutive according to the ordering induced by a weight vector.

Construct SOS-II constraints using the SOS2 set:

julia> @constraint(model, x in SOS2([3.0, 1.0, 2.0]))
[x[1], x[2], x[3]] in MathOptInterface.SOS2{Float64}([3.0, 1.0, 2.0])

The possible non-zero pairs are (x[1], x[3]) and (x[2], x[3]):

If the weight vector is omitted, JuMP induces an ordering from 1:length(x):

julia> @constraint(model, x in SOS2())
[x[1], x[2], x[3]] in MathOptInterface.SOS2{Float64}([1.0, 2.0, 3.0])

Indicator constraints

Indicator constraints consist of a binary variable and a linear constraint. The constraint holds when the binary variable takes the value 1. The constraint may or may not hold when the binary variable takes the value 0.

To enforce the constraint x + y <= 1 when the binary variable a is 1, use:

julia> model = Model();

julia> @variable(model, x)
x

julia> @variable(model, y)
y

julia> @variable(model, a, Bin)
a

julia> @constraint(model, a --> {x + y <= 1})
a --> {x + y ≤ 1}

If the constraint must hold when a is zero, add ! or ¬ before the binary variable;

julia> @constraint(model, !a --> {x + y <= 1})
!a --> {x + y ≤ 1}
Warning

You cannot use an expression for the left-hand side of an indicator constraint.

Semidefinite constraints

To constrain a matrix to be positive semidefinite (PSD), use PSDCone:

julia> model = Model();

julia> @variable(model, X[1:2, 1:2])
2×2 Matrix{VariableRef}:
 X[1,1]  X[1,2]
 X[2,1]  X[2,2]

julia> @constraint(model, X >= 0, PSDCone())
[X[1,1]  X[1,2];
 X[2,1]  X[2,2]] ∈ PSDCone()
Tip

Where possible, prefer constructing a matrix of Semidefinite variables using the @variable macro, rather than adding a constraint like @constraint(model, X >= 0, PSDCone()). In some solvers, adding the constraint via @constraint is less efficient, and can result in additional intermediate variables and constraints being added to the model.

The inequality X >= Y between two square matrices X and Y is understood as constraining X - Y to be positive semidefinite.

julia> Y = [1 2; 2 1]
2×2 Matrix{Int64}:
 1  2
 2  1

julia> @constraint(model, X >= Y, PSDCone())
[X[1,1] - 1  X[1,2] - 2;
 X[2,1] - 2  X[2,2] - 1] ∈ PSDCone()

Symmetry

Solvers supporting PSD constraints usually expect to be given a matrix that is symbolically symmetric, that is, for which the expression in corresponding off-diagonal entries are the same. In our example, the expressions of entries (1, 2) and (2, 1) are respectively X[1,2] - 2 and X[2,1] - 2 which are different.

To bridge the gap between the constraint modeled and what the solver expects, solvers may add an equality constraint X[1,2] - 2 == X[2,1] - 2 to force symmetry. Use LinearAlgebra.Symmetric to explicitly tell the solver that the matrix is symmetric:

julia> import LinearAlgebra

julia> Z = [X[1, 1] X[1, 2]; X[1, 2] X[2, 2]]
2×2 Matrix{VariableRef}:
 X[1,1]  X[1,2]
 X[1,2]  X[2,2]

julia> @constraint(model, LinearAlgebra.Symmetric(Z) >= 0, PSDCone())
[X[1,1]  X[1,2];
 X[1,2]  X[2,2]] ∈ PSDCone()

Note that the lower triangular entries are ignored even if they are different so use it with caution:

julia> @constraint(model, LinearAlgebra.Symmetric(X) >= 0, PSDCone())
[X[1,1]  X[1,2];
 X[1,2]  X[2,2]] ∈ PSDCone()

(Note the (2, 1) element of the constraint is X[1,2], not X[2,1].)

Complementarity constraints

A mixed complementarity constraint F(x) ⟂ x consists of finding x in the interval [lb, ub], such that the following holds:

  • F(x) == 0 if lb < x < ub
  • F(x) >= 0 if lb == x
  • F(x) <= 0 if x == ub

JuMP supports mixed complementarity constraints via complements(F(x), x) or F(x) ⟂ x in the @constraint macro. The interval set [lb, ub] is obtained from the variable bounds on x.

For example, to define the problem 2x - 1 ⟂ x with x ∈ [0, ∞), do:

julia> model = Model();

julia> @variable(model, x >= 0)
x

julia> @constraint(model, 2x - 1 ⟂ x)
[2 x - 1, x] ∈ MathOptInterface.Complements(2)

This problem has a unique solution at x = 0.5.

The perp operator can be entered in most editors (and the Julia REPL) by typing \perp<tab>.

An alternative approach that does not require the symbol uses the complements function as follows:

julia> @constraint(model, complements(2x - 1, x))
[2 x - 1, x] ∈ MathOptInterface.Complements(2)

In both cases, the mapping F(x) is supplied as the first argument, and the matching variable x is supplied as the second.

Vector-valued complementarity constraints are also supported:

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

julia> M = [1 2; 3 4]
2×2 Matrix{Int64}:
 1  2
 3  4

julia> q = [5, 6]
2-element Vector{Int64}:
 5
 6

julia> @constraint(model, M * y + q ⟂ y)
[y[1] + 2 y[2] + 5, 3 y[1] + 4 y[2] + 6, y[1], y[2]] ∈ MathOptInterface.Complements(4)

Boolean constraints

Add a Boolean constraint (a MOI.EqualTo{Bool} set) using the := operator with a Bool right-hand side term:

julia> model = GenericModel{Bool}();

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

julia> @constraint(model, x[1] || x[2] := true)
x[1] || x[2] = true

julia> @constraint(model, x[1] && x[2] := false)
x[1] && x[2] = false

julia> model
A JuMP Model
Feasibility problem with:
Variables: 2
`GenericNonlinearExpr{GenericVariableRef{Bool}}`-in-`MathOptInterface.EqualTo{Bool}`: 2 constraints
Model mode: AUTOMATIC
CachingOptimizer state: NO_OPTIMIZER
Solver name: No optimizer attached.
Names registered in the model: x

Boolean constraints should not be added using the == operator because JuMP will rewrite the constraint as lhs - rhs = 0, and because constraints like a == b == c require parentheses to disambiguate between (a == b) == c and a == (b == c). In contrast, a == b := c is equivalent to (a == b) := c:

julia> model = Model();

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

julia> rhs = false
false

julia> @constraint(model, (x[1] == x[2]) == rhs)
(x[1] == x[2]) - 0.0 = 0

julia> @constraint(model, x[1] == x[2] := rhs)
x[1] == x[2] = false