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The Utilities submodule

The Utilities submodule provides a variety of functions and datastructures for managing MOI.ModelLike objects.

Utilities.Model

Utilities.Model provides an implementation of a ModelLike that efficiently supports all functions and sets defined within MOI. However, given the extensibility of MOI, this might not cover all use cases.

Create a model as follows:

julia> model = MOI.Utilities.Model{Float64}()
MOIU.Model{Float64}
├ ObjectiveSense: FEASIBILITY_SENSE
├ ObjectiveFunctionType: MOI.ScalarAffineFunction{Float64}
├ NumberOfVariables: 0
└ NumberOfConstraints: 0

Utilities.UniversalFallback

Utilities.UniversalFallback is a layer that sits on top of any ModelLike and provides non-specialized (slower) fallbacks for constraints and attributes that the underlying ModelLike does not support.

For example, Utilities.Model doesn't support some variable attributes like VariablePrimalStart, so JuMP uses a combination of Universal fallback and Utilities.Model as a generic problem cache:

julia> model = MOI.Utilities.UniversalFallback(MOI.Utilities.Model{Float64}())
MOIU.UniversalFallback{MOIU.Model{Float64}}
├ ObjectiveSense: FEASIBILITY_SENSE
├ ObjectiveFunctionType: MOI.ScalarAffineFunction{Float64}
├ NumberOfVariables: 0
└ NumberOfConstraints: 0
Warning

Adding a UniversalFallback means that your model will now support all constraints, even if the inner-model does not. This can lead to unexpected behavior.

Utilities.@model

For advanced use cases that need efficient support for functions and sets defined outside of MOI (but still known at compile time), we provide the Utilities.@model macro.

The @model macro takes a name (for a new type, which must not exist yet), eight tuples specifying the types of constraints that are supported, and then a Bool indicating the type is a subtype of MOI.AbstractOptimizer (if true) or MOI.ModelLike (if false).

The eight tuples are in the following order:

  1. Un-typed scalar sets, for example, Integer
  2. Typed scalar sets, for example, LessThan
  3. Un-typed vector sets, for example, Nonnegatives
  4. Typed vector sets, for example, PowerCone
  5. Un-typed scalar functions, for example, VariableIndex
  6. Typed scalar functions, for example, ScalarAffineFunction
  7. Un-typed vector functions, for example, VectorOfVariables
  8. Typed vector functions, for example, VectorAffineFunction

The tuples can contain more than one element. Typed-sets must be specified without their type parameter, for example, MOI.LessThan, not MOI.LessThan{Float64}.

Here is an example:

julia> MOI.Utilities.@model(
           MyNewModel,
           (MOI.Integer,),                  # Un-typed scalar sets
           (MOI.GreaterThan,),              # Typed scalar sets
           (MOI.Nonnegatives,),             # Un-typed vector sets
           (MOI.PowerCone,),                # Typed vector sets
           (MOI.VariableIndex,),            # Un-typed scalar functions
           (MOI.ScalarAffineFunction,),     # Typed scalar functions
           (MOI.VectorOfVariables,),        # Un-typed vector functions
           (MOI.VectorAffineFunction,),     # Typed vector functions
           true,                            # <:MOI.AbstractOptimizer?
       )
MathOptInterface.Utilities.GenericOptimizer{T, MathOptInterface.Utilities.ObjectiveContainer{T}, MathOptInterface.Utilities.VariablesContainer{T}, MyNewModelFunctionConstraints{T}} where T

julia> model = MyNewModel{Float64}()
MOIU.GenericOptimizer{Float64, MOIU.ObjectiveContainer{Float64}, MOIU.VariablesContainer{Float64}, MyNewModelFunctionConstraints{Float64}}
├ ObjectiveSense: FEASIBILITY_SENSE
├ ObjectiveFunctionType: MOI.ScalarAffineFunction{Float64}
├ NumberOfVariables: 0
└ NumberOfConstraints: 0
Warning

MyNewModel supports every VariableIndex-in-Set constraint, as well as VariableIndex, ScalarAffineFunction, and ScalarQuadraticFunction objective functions. Implement MOI.supports as needed to forbid constraint and objective function combinations.

As another example, PATHSolver, which only supports VectorAffineFunction-in-Complements defines its optimizer as:

julia> MOI.Utilities.@model(
           PathOptimizer,
           (),  # Scalar sets
           (),  # Typed scalar sets
           (MOI.Complements,),  # Vector sets
           (),  # Typed vector sets
           (),  # Scalar functions
           (),  # Typed scalar functions
           (),  # Vector functions
           (MOI.VectorAffineFunction,),  # Typed vector functions
           true,  # is_optimizer
       )
MathOptInterface.Utilities.GenericOptimizer{T, MathOptInterface.Utilities.ObjectiveContainer{T}, MathOptInterface.Utilities.VariablesContainer{T}, MathOptInterface.Utilities.VectorOfConstraints{MathOptInterface.VectorAffineFunction{T}, MathOptInterface.Complements}} where T

However, PathOptimizer does not support some VariableIndex-in-Set constraints, so we must explicitly define:

julia> function MOI.supports_constraint(
           ::PathOptimizer,
           ::Type{MOI.VariableIndex},
           ::Type{Union{<:MOI.Semiinteger,MOI.Semicontinuous,MOI.ZeroOne,MOI.Integer}}
       )
           return false
       end

Finally, PATH doesn't support an objective function, so we need to add:

julia> MOI.supports(::PathOptimizer, ::MOI.ObjectiveFunction) = false
Warning

This macro creates a new type, so it must be called from the top-level of a module, for example, it cannot be called from inside a function.

Utilities.CachingOptimizer

A [Utilities.CachingOptimizer] is an MOI layer that abstracts the difference between solvers that support incremental modification (for example, they support adding variables one-by-one), and solvers that require the entire problem in a single API call (for example, they only accept the A, b and c matrices of a linear program).

It has two parts:

  1. A cache, where the model can be built and modified incrementally
  2. An optimizer, which is used to solve the problem
julia> model = MOI.Utilities.CachingOptimizer(
           MOI.Utilities.Model{Float64}(),
           PathOptimizer{Float64}(),
       )
MOIU.CachingOptimizer
├ state: EMPTY_OPTIMIZER
├ mode: AUTOMATIC
├ model_cache: MOIU.Model{Float64}
│ ├ ObjectiveSense: FEASIBILITY_SENSE
│ ├ ObjectiveFunctionType: MOI.ScalarAffineFunction{Float64}
│ ├ NumberOfVariables: 0
│ └ NumberOfConstraints: 0
└ optimizer: MOIU.GenericOptimizer{Float64, MOIU.ObjectiveContainer{Float64}, MOIU.VariablesContainer{Float64}, MOIU.VectorOfConstraints{MOI.VectorAffineFunction{Float64}, MOI.Complements}}
  ├ ObjectiveSense: FEASIBILITY_SENSE
  ├ ObjectiveFunctionType: MOI.ScalarAffineFunction{Float64}
  ├ NumberOfVariables: 0
  └ NumberOfConstraints: 0

A Utilities.CachingOptimizer may be in one of three possible states:

  • NO_OPTIMIZER: The CachingOptimizer does not have any optimizer.
  • EMPTY_OPTIMIZER: The CachingOptimizer has an empty optimizer, and it is not synchronized with the cached model. Modifications are forwarded to the cache, but not to the optimizer.
  • ATTACHED_OPTIMIZER: The CachingOptimizer has an optimizer, and it is synchronized with the cached model. Modifications are forwarded to the optimizer. If the optimizer does not support modifications, and error will be thrown.

Use Utilities.attach_optimizer to go from EMPTY_OPTIMIZER to ATTACHED_OPTIMIZER:

julia> MOI.Utilities.attach_optimizer(model)

julia> MOI.Utilities.state(model)
ATTACHED_OPTIMIZER::CachingOptimizerState = 2
Info

You must be in ATTACHED_OPTIMIZER to use optimize!.

Use Utilities.reset_optimizer to go from ATTACHED_OPTIMIZER to EMPTY_OPTIMIZER:

julia> MOI.Utilities.reset_optimizer(model)

julia> MOI.Utilities.state(model)
EMPTY_OPTIMIZER::CachingOptimizerState = 1
Info

Calling MOI.empty!(model) also resets the state to EMPTY_OPTIMIZER. So after emptying a model, the modification will only be applied to the cache.

Use Utilities.drop_optimizer to go from any state to NO_OPTIMIZER:

julia> MOI.Utilities.drop_optimizer(model)

julia> MOI.Utilities.state(model)
NO_OPTIMIZER::CachingOptimizerState = 0

julia> model
MOIU.CachingOptimizer
├ state: NO_OPTIMIZER
├ mode: AUTOMATIC
├ model_cache: MOIU.Model{Float64}
│ ├ ObjectiveSense: FEASIBILITY_SENSE
│ ├ ObjectiveFunctionType: MOI.ScalarAffineFunction{Float64}
│ ├ NumberOfVariables: 0
│ └ NumberOfConstraints: 0
└ optimizer: nothing

Pass an empty optimizer to Utilities.reset_optimizer to go from NO_OPTIMIZER to EMPTY_OPTIMIZER:

julia> MOI.Utilities.reset_optimizer(model, PathOptimizer{Float64}())

julia> MOI.Utilities.state(model)
EMPTY_OPTIMIZER::CachingOptimizerState = 1

julia> model
MOIU.CachingOptimizer
├ state: EMPTY_OPTIMIZER
├ mode: AUTOMATIC
├ model_cache: MOIU.Model{Float64}
│ ├ ObjectiveSense: FEASIBILITY_SENSE
│ ├ ObjectiveFunctionType: MOI.ScalarAffineFunction{Float64}
│ ├ NumberOfVariables: 0
│ └ NumberOfConstraints: 0
└ optimizer: MOIU.GenericOptimizer{Float64, MOIU.ObjectiveContainer{Float64}, MOIU.VariablesContainer{Float64}, MOIU.VectorOfConstraints{MOI.VectorAffineFunction{Float64}, MOI.Complements}}
  ├ ObjectiveSense: FEASIBILITY_SENSE
  ├ ObjectiveFunctionType: MOI.ScalarAffineFunction{Float64}
  ├ NumberOfVariables: 0
  └ NumberOfConstraints: 0

Deciding when to attach and reset the optimizer is tedious, and you will often write code like this:

try
    # modification
catch
    MOI.Utilities.reset_optimizer(model)
    # Re-try modification
end

To make this easier, Utilities.CachingOptimizer has two modes of operation:

  • AUTOMATIC: The CachingOptimizer changes its state when necessary. Attempting to add a constraint or perform a modification not supported by the optimizer results in a drop to EMPTY_OPTIMIZER mode.
  • MANUAL: The user must change the state of the CachingOptimizer. Attempting to perform an operation in the incorrect state results in an error.

By default, AUTOMATIC mode is chosen. However, you can create a CachingOptimizer in MANUAL mode as follows:

julia> model = MOI.Utilities.CachingOptimizer(
           MOI.Utilities.Model{Float64}(),
           MOI.Utilities.MANUAL,
       )
MOIU.CachingOptimizer
├ state: NO_OPTIMIZER
├ mode: MANUAL
├ model_cache: MOIU.Model{Float64}
│ ├ ObjectiveSense: FEASIBILITY_SENSE
│ ├ ObjectiveFunctionType: MOI.ScalarAffineFunction{Float64}
│ ├ NumberOfVariables: 0
│ └ NumberOfConstraints: 0
└ optimizer: nothing

julia> MOI.Utilities.reset_optimizer(model, PathOptimizer{Float64}())

julia> model
MOIU.CachingOptimizer
├ state: EMPTY_OPTIMIZER
├ mode: MANUAL
├ model_cache: MOIU.Model{Float64}
│ ├ ObjectiveSense: FEASIBILITY_SENSE
│ ├ ObjectiveFunctionType: MOI.ScalarAffineFunction{Float64}
│ ├ NumberOfVariables: 0
│ └ NumberOfConstraints: 0
└ optimizer: MOIU.GenericOptimizer{Float64, MOIU.ObjectiveContainer{Float64}, MOIU.VariablesContainer{Float64}, MOIU.VectorOfConstraints{MOI.VectorAffineFunction{Float64}, MOI.Complements}}
  ├ ObjectiveSense: FEASIBILITY_SENSE
  ├ ObjectiveFunctionType: MOI.ScalarAffineFunction{Float64}
  ├ NumberOfVariables: 0
  └ NumberOfConstraints: 0

Printing

Use print to print the formulation of the model.

julia> model = MOI.Utilities.Model{Float64}();

julia> x = MOI.add_variable(model)
MOI.VariableIndex(1)

julia> MOI.set(model, MOI.VariableName(), x, "x_var")

julia> MOI.add_constraint(model, x, MOI.ZeroOne())
MathOptInterface.ConstraintIndex{MathOptInterface.VariableIndex, MathOptInterface.ZeroOne}(1)

julia> MOI.set(model, MOI.ObjectiveFunction{typeof(x)}(), x)

julia> MOI.set(model, MOI.ObjectiveSense(), MOI.MAX_SENSE)

julia> print(model)
Maximize VariableIndex:
 x_var

Subject to:

VariableIndex-in-ZeroOne
 x_var ∈ {0, 1}

Use Utilities.latex_formulation to display the model in LaTeX form:

julia> MOI.Utilities.latex_formulation(model)
$$ \begin{aligned}
\max\quad & x\_var \\
\text{Subject to}\\
 & \text{VariableIndex-in-ZeroOne} \\
 & x\_var \in \{0, 1\} \\
\end{aligned} $$
Tip

In IJulia, calling print or ending a cell with Utilities.latex_formulation will render the model in LaTeX.

Utilities.PenaltyRelaxation

Pass Utilities.PenaltyRelaxation to modify to relax the problem by adding penalized slack variables to the constraints. This is helpful when debugging sources of infeasible models.

julia> model = MOI.Utilities.Model{Float64}();

julia> x = MOI.add_variable(model);

julia> MOI.set(model, MOI.VariableName(), x, "x")

julia> c = MOI.add_constraint(model, 1.0 * x, MOI.LessThan(2.0));

julia> map = MOI.modify(model, MOI.Utilities.PenaltyRelaxation(Dict(c => 2.0)));

julia> print(model)
Minimize ScalarAffineFunction{Float64}:
 0.0 + 2.0 v[2]

Subject to:

ScalarAffineFunction{Float64}-in-LessThan{Float64}
 0.0 + 1.0 x - 1.0 v[2] <= 2.0

VariableIndex-in-GreaterThan{Float64}
 v[2] >= 0.0

julia> map[c]
0.0 + 1.0 MOI.VariableIndex(2)

You can also modify a single constraint using Utilities.ScalarPenaltyRelaxation:

julia> model = MOI.Utilities.Model{Float64}();

julia> x = MOI.add_variable(model);

julia> MOI.set(model, MOI.VariableName(), x, "x")

julia> c = MOI.add_constraint(model, 1.0 * x, MOI.LessThan(2.0));

julia> f = MOI.modify(model, c, MOI.Utilities.ScalarPenaltyRelaxation(2.0));

julia> print(model)
Minimize ScalarAffineFunction{Float64}:
 0.0 + 2.0 v[2]

Subject to:

ScalarAffineFunction{Float64}-in-LessThan{Float64}
 0.0 + 1.0 x - 1.0 v[2] <= 2.0

VariableIndex-in-GreaterThan{Float64}
 v[2] >= 0.0

julia> f
0.0 + 1.0 MOI.VariableIndex(2)

Utilities.MatrixOfConstraints

The constraints of Utilities.Model are stored as a vector of tuples of function and set in a Utilities.VectorOfConstraints.

Other representations can be used by parameterizing the type Utilities.GenericModel (resp. Utilities.GenericOptimizer).

For example, if all non-VariableIndex constraints are affine, the coefficients of all the constraints can be stored in a single sparse matrix using Utilities.MatrixOfConstraints.

The constraints storage can even be customized up to a point where it exactly matches the storage of the solver of interest, in which case copy_to can be implemented for the solver by calling copy_to to this custom model.

For example, Clp.jl defines the following model:

julia> MOI.Utilities.@product_of_sets(
           SupportedSets,
           MOI.EqualTo{T},
           MOI.LessThan{T},
           MOI.GreaterThan{T},
           MOI.Interval{T},
       );

julia> const OptimizerCache = MOI.Utilities.GenericModel{
           Float64,
           MOI.Utilities.ObjectiveContainer{Float64},
           MOI.Utilities.VariablesContainer{Float64},
           MOI.Utilities.MatrixOfConstraints{
               Float64,
               MOI.Utilities.MutableSparseMatrixCSC{
                   # The data type of the coefficients
                   Float64,
                   # The data type of the variable indices
                   Cint,
                   # Can also be MOI.Utilities.OneBasedIndexing
                   MOI.Utilities.ZeroBasedIndexing,
               },
               MOI.Utilities.Hyperrectangle{Float64},
               SupportedSets{Float64},
           },
       };

Given the input model:

julia> src = MOI.Utilities.Model{Float64}();

julia> MOI.Utilities.loadfromstring!(
           src,
           """
           variables: x, y, z
           maxobjective: x + 2.0 * y + -3.1 * z
           x + y <= 1.0
           2.0 * y >= 3.0
           -4.0 * x + z == 5.0
           x in Interval(0.0, 1.0)
           y <= 10.0
           z == 5.0
           """,
       )

We can construct a new cached model and copy src to it:

julia> dest = OptimizerCache();

julia> index_map = MOI.copy_to(dest, src);

From dest, we can access the A matrix in sparse matrix form:

julia> A = dest.constraints.coefficients;

julia> A.n
3

julia> A.m
3

julia> A.colptr
4-element Vector{Int32}:
 0
 2
 4
 5

julia> A.rowval
5-element Vector{Int32}:
 0
 1
 1
 2
 0

julia> A.nzval
5-element Vector{Float64}:
 -4.0
  1.0
  1.0
  2.0
  1.0

The lower and upper row bounds:

julia> row_bounds = dest.constraints.constants;

julia> row_bounds.lower
3-element Vector{Float64}:
   5.0
 -Inf
   3.0

julia> row_bounds.upper
3-element Vector{Float64}:
  5.0
  1.0
 Inf

The lower and upper variable bounds:

julia> dest.variables.lower
3-element Vector{Float64}:
   0.0
 -Inf
   5.0

julia> dest.variables.upper
3-element Vector{Float64}:
  1.0
 10.0
  5.0

Because of larger variations between solvers, the objective can be queried using the standard MOI methods:

julia> MOI.get(dest, MOI.ObjectiveSense())
MAX_SENSE::OptimizationSense = 1

julia> F = MOI.get(dest, MOI.ObjectiveFunctionType())
MathOptInterface.ScalarAffineFunction{Float64}

julia> F = MOI.get(dest, MOI.ObjectiveFunction{F}())
0.0 + 1.0 MOI.VariableIndex(1) + 2.0 MOI.VariableIndex(2) - 3.1 MOI.VariableIndex(3)

Thus, Clp.jl implements copy_to methods similar to the following:

# This method copies from the cache to the `Clp.Optimizer` object.
function MOI.copy_to(dest::Optimizer, src::OptimizerCache)
    @assert MOI.is_empty(dest)
    A = src.constraints.coefficients
    row_bounds = src.constraints.constants
    Clp_loadProblem(
        dest,
        A.n,
        A.m,
        A.colptr,
        A.rowval,
        A.nzval,
        src.lower_bound,
        src.upper_bound,
        # (...) objective vector (omitted),
        row_bounds.lower,
        row_bounds.upper,
    )
    return MOI.Utilities.identity_index_map(src)
end

# This method copies from an arbitrary model to the optimizer, by the
# intermediate `OptimizerCache` representation.
function MOI.copy_to(dest::Optimizer, src::MOI.ModelLike)
    cache = OptimizerCache()
    index_map = MOI.copy_to(cache, src)
    MOI.copy_to(dest, cache)
    return index_map
end

# This is a special method that gets called in some cases when `OptimizerCache`
# is used as the backing data structure in a `MOI.Utilities.CachingOptimizer`.
# It is needed for performance, but not correctness.
function MOI.copy_to(
    dest::Optimizer,
    src::MOI.Utilities.UniversalFallback{OptimizerCache},
)
    MOI.Utilities.throw_unsupported(src)
    return MOI.copy_to(dest, src.model)
end

ModelFilter

Utilities provides Utilities.ModelFilter as a useful tool to copy a subset of a model. For example, given an infeasible model, we can copy the irreducible infeasible subsystem (for models implementing ConstraintConflictStatus) as follows:

my_filter(::Any) = true
function my_filter(ci::MOI.ConstraintIndex)
    status = MOI.get(dest, MOI.ConstraintConflictStatus(), ci)
    return status != MOI.NOT_IN_CONFLICT
end
filtered_src = MOI.Utilities.ModelFilter(my_filter, src)
index_map = MOI.copy_to(dest, filtered_src)

Fallbacks

The value of some attributes can be inferred from the value of other attributes.

For example, the value of ObjectiveValue can be computed using ObjectiveFunction and VariablePrimal.

When a solver gives direct access to an attribute, it is better to return this value. However, if this is not the case, Utilities.get_fallback can be used instead. For example:

function MOI.get(model::Optimizer, attr::MOI.ObjectiveFunction)
    return MOI.Utilities.get_fallback(model, attr)
end

DoubleDicts

When writing MOI interfaces, we often need to handle situations in which we map ConstraintIndexs to different values. For example, to a string for ConstraintName.

One option is to use a dictionary like Dict{MOI.ConstraintIndex,String}. However, this incurs a performance cost because the key is not a concrete type.

The DoubleDicts submodule helps this situation by providing two types main types Utilities.DoubleDicts.DoubleDict and Utilities.DoubleDicts.IndexDoubleDict. These types act like normal dictionaries, but internally they use more efficient dictionaries specialized to the type of the function-set pair.

The most common usage of a DoubleDict is in the index_map returned by copy_to. Performance can be improved, by using a function barrier. That is, instead of code like:

index_map = MOI.copy_to(dest, src)
for (F, S) in MOI.get(src, MOI.ListOfConstraintTypesPresent())
    for ci in MOI.get(src, MOI.ListOfConstraintIndices{F,S}())
        dest_ci = index_map[ci]
        # ...
    end
end

use instead:

function function_barrier(
    dest,
    src,
    index_map::MOI.Utilities.DoubleDicts.IndexDoubleDictInner{F,S},
) where {F,S}
    for ci in MOI.get(src, MOI.ListOfConstraintIndices{F,S}())
        dest_ci = index_map[ci]
        # ...
    end
    return
end

index_map = MOI.copy_to(dest, src)
for (F, S) in MOI.get(src, MOI.ListOfConstraintTypesPresent())
    function_barrier(dest, src, index_map[F, S])
end