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Gurobi.jl

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Gurobi.jl is a wrapper for the Gurobi Optimizer.

It has two components:

Affiliation

This wrapper is maintained by the JuMP community and is not officially supported by Gurobi. However, we thank Gurobi for providing us with a license to test Gurobi.jl on GitHub. If you are a commercial customer interested in official support for Gurobi in Julia, let them know.

License

Gurobi.jl is licensed under the MIT License.

The underlying solver is a closed-source commercial product for which you must obtain a license.

Free Gurobi licenses are available for academics and students.

Installation

First, obtain a license of Gurobi and install Gurobi solver.

Then, set the GUROBI_HOME environment variable as appropriate and run Pkg.add("Gurobi"):

# On Windows, this might be
ENV["GUROBI_HOME"] = "C:\\Program Files\\gurobi1000\\win64"
# ... or perhaps ...
ENV["GUROBI_HOME"] = "C:\\gurobi1000\\win64"
# On Mac, this might be
ENV["GUROBI_HOME"] = "/Library/gurobi1000/mac64"

import Pkg
Pkg.add("Gurobi")

Note: your path may differ. Check which folder you installed Gurobi in, and update the path accordingly.

By default, building Gurobi.jl will fail if the Gurobi library is not found. This may not be desirable in certain cases, for example when part of a package's test suite uses Gurobi as an optional test dependency, but Gurobi cannot be installed on a CI server running the test suite. To support this use case, the GUROBI_JL_SKIP_LIB_CHECK environment variable may be set (to any value) to make Gurobi.jl installable (but not usable).

Use with JuMP

To use Gurobi with JuMP, use Gurobi.Optimizer:

using JuMP, Gurobi
model = Model(Gurobi.Optimizer)
set_attribute(model, "TimeLimit", 100)
set_attribute(model, "Presolve", 0)

MathOptInterface API

The Gurobi optimizer supports the following constraints and attributes.

List of supported objective functions:

List of supported variable types:

List of supported constraint types:

List of supported model attributes:

Options

See the Gurobi Documentation for a list and description of allowable parameters.

C API

The C API can be accessed via Gurobi.GRBxx functions, where the names and arguments are identical to the C API.

See the Gurobi documentation for details.

Reusing the same Gurobi environment for multiple solves

When using this package via other packages such as JuMP.jl, the default behavior is to obtain a new Gurobi license token every time a model is created. If you are using Gurobi in a setting where the number of concurrent Gurobi uses is limited (for example, "Single-Use" or "Floating-Use" licenses), you might instead prefer to obtain a single license token that is shared by all models that your program solves.

You can do this by passing a Gurobi.Env() object as the first parameter to Gurobi.Optimizer. For example:

using JuMP, Gurobi
const GRB_ENV = Gurobi.Env()

model_1 = Model(() -> Gurobi.Optimizer(GRB_ENV))

# The solvers can have different options too
model_2 = direct_model(Gurobi.Optimizer(GRB_ENV))
set_attribute(model_2, "OutputFlag", 0)

Accessing Gurobi-specific attributes

Get and set Gurobi-specific variable, constraint, and model attributes as follows:

using JuMP, Gurobi
model = direct_model(Gurobi.Optimizer())
@variable(model, x >= 0)
@constraint(model, c, 2x >= 1)
@objective(model, Min, x)
MOI.set(model, Gurobi.ConstraintAttribute("Lazy"), c, 2)
optimize!(model)
MOI.get(model, Gurobi.VariableAttribute("LB"), x)  # Returns 0.0
MOI.get(model, Gurobi.ModelAttribute("NumConstrs")) # Returns 1

A complete list of supported Gurobi attributes can be found in their online documentation.

Callbacks

Here is an example using Gurobi's solver-specific callbacks.

using JuMP, Gurobi, Test

model = direct_model(Gurobi.Optimizer())
@variable(model, 0 <= x <= 2.5, Int)
@variable(model, 0 <= y <= 2.5, Int)
@objective(model, Max, y)
cb_calls = Cint[]
function my_callback_function(cb_data, cb_where::Cint)
    # You can reference variables outside the function as normal
    push!(cb_calls, cb_where)
    # You can select where the callback is run
    if cb_where != GRB_CB_MIPSOL && cb_where != GRB_CB_MIPNODE
        return
    end
    # You can query a callback attribute using GRBcbget
    if cb_where == GRB_CB_MIPNODE
        resultP = Ref{Cint}()
        GRBcbget(cb_data, cb_where, GRB_CB_MIPNODE_STATUS, resultP)
        if resultP[] != GRB_OPTIMAL
            return  # Solution is something other than optimal.
        end
    end
    # Before querying `callback_value`, you must call:
    Gurobi.load_callback_variable_primal(cb_data, cb_where)
    x_val = callback_value(cb_data, x)
    y_val = callback_value(cb_data, y)
    # You can submit solver-independent MathOptInterface attributes such as
    # lazy constraints, user-cuts, and heuristic solutions.
    if y_val - x_val > 1 + 1e-6
        con = @build_constraint(y - x <= 1)
        MOI.submit(model, MOI.LazyConstraint(cb_data), con)
    elseif y_val + x_val > 3 + 1e-6
        con = @build_constraint(y + x <= 3)
        MOI.submit(model, MOI.LazyConstraint(cb_data), con)
    end
    if rand() < 0.1
        # You can terminate the callback as follows:
        GRBterminate(backend(model))
    end
    return
end
# You _must_ set this parameter if using lazy constraints.
MOI.set(model, MOI.RawOptimizerAttribute("LazyConstraints"), 1)
MOI.set(model, Gurobi.CallbackFunction(), my_callback_function)
optimize!(model)
@test termination_status(model) == MOI.OPTIMAL
@test primal_status(model) == MOI.FEASIBLE_POINT
@test value(x) == 1
@test value(y) == 2

See the Gurobi documentation for other information that can be queried with GRBcbget.

Common Performance Pitfall with JuMP

Gurobi's API works differently than most solvers. Any changes to the model are not applied immediately, but instead go sit in a internal buffer (making any modifications appear to be instantaneous) waiting for a call to GRBupdatemodel (where the work is done).

This leads to a common performance pitfall that has the following message as its main symptom:

Warning: excessive time spent in model updates. Consider calling update less frequently.

This often means the JuMP program was structured in such a way that Gurobi.jl ends up calling GRBupdatemodel in each iteration of a loop.

Usually, it is possible (and easy) to restructure the JuMP program in a way it stays ssolver-agnostic and has a close-to-ideal performance with Gurobi.

To guide such restructuring it is good to keep in mind the following bits of information:

  1. GRBupdatemodel is only called if changes were done since last GRBupdatemodel (that is, if the internal buffer is not empty).
  2. GRBupdatemodel is called when JuMP.optimize! is called, but this often is not the source of the problem.
  3. GRBupdatemodel may be called when any model attribute is queried, even if that specific attribute was not changed. This often the source of the problem.

The worst-case scenario is, therefore, a loop of modify-query-modify-query, even if what is being modified and what is being queried are two completely distinct things.

As an example, instead of:

model = Model(Gurobi.Optimizer)
@variable(model, x[1:100] >= 0)
for i in 1:100
    set_upper_bound(x[i], i)
    # `GRBupdatemodel` called on each iteration of this loop.
    println(lower_bound(x[i]))
end

do

model = Model(Gurobi.Optimizer)
@variable(model, x[1:100] >= 0)
# All modifications are done before any queries.
for i in 1:100
    set_upper_bound(x[i], i)
end
for i in 1:100
    # Only the first `lower_bound` query may trigger an `GRBupdatemodel`.
    println(lower_bound(x[i]))
end

Common errors

Using Gurobi v9.0 and you got an error like Q not PSD?

You need to set the NonConvex parameter:

model = Model(Gurobi.Optimizer)
set_optimizer_attribute(model, "NonConvex", 2)

Gurobi Error 1009: Version number is XX.X, license is for version XX.X

Make sure that your license is correct for your Gurobi version. See the Gurobi documentation for details.

Once you are sure that the license and Gurobi versions match, re-install Gurobi.jl by running:

import Pkg
Pkg.build("Gurobi")