Solvers

Interacting with solvers

A JuMP model keeps a MathOptInterface (MOI) backend of type MOI.ModelLike that stores the optimization problem and acts as the optimization solver. We call it an MOI backend and not optimizer as it can also be a wrapper around an optimization file format such as MPS that writes the JuMP model in a file. From JuMP, the MOI backend can be accessed using the backend function. JuMP can be viewed as a lightweight, user-friendly layer on top of the MOI backend, in the sense that:

While this allows JuMP to be a thin wrapper on top of the solver API, as mentioned in the last point above, this seems rather demanding on the solver. Indeed, while some solvers support incremental building of the model and modifications before and after solve, other solvers only support the model being copied at once before solve. Moreover, it seems to require all solvers to implement all possible reformulations independently which seems both very ambitious and might generate a lot of duplicated code.

These apparent limitations are addressed at level of MOI in a manner that is completely transparent to JuMP. While the MOI API may seem very demanding, it allows MOI models to be a succession of lightweight MOI layers that fill the gap between JuMP requirements and the solver capabilities. The remainder of this section describes how JuMP interacts with the MOI backend.

JuMP models can be created in three different modes: AUTOMATIC, MANUAL and DIRECT.

Automatic and Manual modes

In AUTOMATIC and MANUAL modes, two MOI layers are automatically applied to the optimizer:

See the MOI documentation for more details on these two MOI layers.

To attach an optimizer to a JuMP model, JuMP needs to create a new empty optimizer instance. New optimizer instances can be obtained using an OptimizerFactory that can be created using the with_optimizer function:

JuMP.with_optimizerFunction.
with_optimizer(constructor, args...; kwargs...)

Return an OptimizerFactory that creates optimizers using the constructor constructor with positional arguments args and keyword arguments kwargs.

Examples

The following returns an optimizer factory that creates Ipopt.Optimizers using the constructor call Ipopt.Optimizer(print_level=0):

with_optimizer(Ipopt.Optimizer, print_level=0)

The factory can be provided either at model construction time or at optimize! time:

struct NoOptimizer <: Exception end

No optimizer is set. The optimizer can be provided at the Model constructor or at the optimize! call with with_optimizer.

JuMP.optimize!Function.
optimize!(model::Model,
          optimizer_factory::Union{Nothing, OptimizerFactory}=nothing;
          bridge_constraints::Bool=true,
          ignore_optimize_hook=(model.optimize_hook === nothing),
          kwargs...)

Optimize the model. If optimizer_factory is not nothing, it first sets the optimizer to a new one created using the optimizer factory. The factory can be created using the with_optimizer function. If optimizer_factory is nothing and no optimizer was set to model before calling this function, a NoOptimizer error is thrown.

Keyword arguments kwargs are passed to the optimize_hook. An error is thrown if optimize_hook is nothing and keyword arguments are provided.

Examples

The optimizer factory can either be given in the Model constructor as follows:

model = Model(with_optimizer(GLPK.Optimizer))
# ...fill model with variables, constraints and objectives...
# Solve the model with GLPK
optimize!(model)

or in the optimize! call as follows:

model = Model()
# ...fill model with variables, constraints and objectives...
# Solve the model with GLPK
optimize!(model, with_optimizer(GLPK.Optimizer))

New JuMP models are created using the Model constructor:

JuMP.ModelMethod.
Model(; caching_mode::MOIU.CachingOptimizerMode=MOIU.AUTOMATIC,
        bridge_constraints::Bool=true)

Return a new JuMP model without any optimizer; the model is stored the model in a cache. The mode of the CachingOptimizer storing this cache is caching_mode. The optimizer can be set later in the optimize! call. If bridge_constraints is true, constraints that are not supported by the optimizer are automatically bridged to equivalent supported constraints when an appropriate transformation is defined in the MathOptInterface.Bridges module or is defined in another module and is explicitely added.

JuMP.ModelMethod.
Model(optimizer_factory::OptimizerFactory;
      caching_mode::MOIU.CachingOptimizerMode=MOIU.AUTOMATIC,
      bridge_constraints::Bool=true)

Return a new JuMP model using the optimizer factory optimizer_factory to create the optimizer. The optimizer factory can be created by the with_optimizer function.

Examples

The following creates a model using the optimizer Ipopt.Optimizer(print_level=0):

model = Model(with_optimizer(Ipopt.Optimizer, print_level=0))

Direct mode

JuMP models can be created in DIRECT mode using the JuMP.direct_model function.

JuMP.direct_modelFunction.
direct_model(backend::MOI.ModelLike)

Return a new JuMP model using backend to store the model and solve it. As opposed to the Model constructor, no cache of the model is stored outside of backend and no bridges are automatically applied to backend. The absence of cache reduces the memory footprint but it is important to bear in mind the following implications of creating models using this direct mode:

  • When backend does not support an operation, such as modifying constraints or adding variables/constraints after solving, an error is thrown. For models created using the Model constructor, such situations can be dealt with by storing the modifications in a cache and loading them into the optimizer when optimize! is called.
  • No constraint bridging is supported by default.
  • The optimizer used cannot be changed the model is constructed.
  • The model created cannot be copied.
JuMP.backendFunction.
backend(model::Model)

Return the lower-level MathOptInterface model that sits underneath JuMP. This model depends on which operating mode JuMP is in (manual, automatic, or direct), and whether there are any bridges in the model.

If JuMP is in direct mode (i.e., the model was created using direct_model), the backend with be the optimizer passed to direct_model. If JuMP is in manual or automatic mode, the backend is a MOI.Utilities.CachingOptimizer.

This function should only be used by advanced users looking to access low-level MathOptInterface or solver-specific functionality.

Solver attributes

Some solver attributes can be queried and set through JuMP models.

JuMP.solver_nameFunction.
solver_name(model::Model)

If available, returns the SolverName property of the underlying optimizer. Returns "No optimizer attached" in AUTOMATIC or MANUAL modes when no optimizer is attached. Returns "SolverName() attribute not implemented by the optimizer." if the attribute is not implemented.

bridge_constraints(model::Model)

When in direct mode, return false. When in manual or automatic mode, return a Bool indicating whether the optimizer is set and unsupported constraints are automatically bridged to equivalent supported constraints when an appropriate transformation is available.

JuMP.set_silentFunction.
set_silent(model::Model)

Takes precedence over any other attribute controlling verbosity and requires the solver to produce no output.

JuMP.unset_silentFunction.
unset_silent(model::Model)

Neutralize the effect of the set_silent function and let the solver attributes control the verbosity.

JuMP.set_parameterFunction.
set_parameter(model::Model, name, value)

Sets solver-specific parameter identified by name to value.

set_time_limit_sec(model::Model, limit)

Sets the time limit (in seconds) of the solver. Can be unset using unset_time_limit_sec or with limit set to nothing.

unset_time_limit_sec(model::Model)

Unsets the time limit of the solver. Can be set using set_time_limit_sec.

JuMP.time_limit_secFunction.
time_limit_sec(model::Model)

Gets the time limit (in seconds) of the model (nothing if unset). Can be set using set_time_limit_sec.