Gurobi#

Gurobi Optimizer#

Gurobi Optimizer is a Mixed Integer Programming (MIP) solver provided by Gurobi that can handle quadratic problems in integer and real variables, including QUBO.

Note

To use Gurobi, you need to install the Gurobi Optimizer and license on the machine running the Amplify SDK.

Solver specification:

Binary variable

Ising variable

Integer variable

Real variable

Objective function

2nd

-

2nd

2nd

Equality constraint

2nd

-

2nd

2nd

Inequality constraint

2nd

-

2nd

2nd

Client class:

In addition to the common interface, the client class has the following method.

Method

Return type

Details

tune()

None

Perform execution parameter search

Note

GurobiClient file output works as follows.

  • write_request_data:

    Output the model to a file with the specified file path extension.
    Possible file extensions are .mps, .rew, .lp, .rlp, .dua/.dlp (as a model of LP dual problem).

  • write_response_data:

    Output the solution in a file with the specified file path extension.
    Possible file extensions are .sol or .json.

See also

See Gurobi Parameters for details on the attributes of the Parameters class that can be specified in parameters. Note that the attribute names of the Parameters class are standardized to snake_case.

Note

All attributes of the Parameters class are initialized to None (unset) by default, but some attributes are explicitly passed to Gurobi when None is specified for convenience, as follows.

  • log_to_console:

    Contrary to Gurobi’s default (1), 0 (no output) is treated as default.

  • seed:

    Contrary to Gurobi’s default (0), this is initialized by generating a hardware random number.

Configuration example:
from amplify import GurobiClient
from datetime import timedelta

client = GurobiClient()

# Set installation path
# (necessary if the installation path is not detected automatically)
# client.library_path = "/opt/gurobi950/linux64/lib/libgurobi.so.9.5.0"

# Set the execution time to 100 seconds
client.parameters.time_limit = timedelta(seconds=100)
Execution example:
from amplify import VariableGenerator, Model

# Create decision variables and the objective function
g = VariableGenerator()
q = g.array("Binary", 2)
f = q[0] * q[1] + q[0] - q[1] + 1

# Create a model
model = Model(f)

# Run the solver
result = solve(model, client)

To obtain the solver version:

>>> client.version
'9.5.0'