To access the Gurobi Interactive Shell, you can simply type gurobi.sh from the command prompt. If you've installed a Python IDE, the shell will also be available from that environment.
Once the optimizer has started, you are ready to load and optimize a
model. We'll consider model
Set parameter LogFile to value "gurobi.log"
Gurobi Interactive Shell (linux64), Version 10.0.0
Copyright (c) 2022, Gurobi Optimization, LLC
Type "help()" for help
gurobi> m = read("/opt/gurobi1000/linux64/examples/data/coins.lp")
Read LP format model from file /opt/gurobi1000/linux64/examples/data/coins.lp
Reading time = 0.00 seconds : 4 rows, 9 columns, 16 nonzeros
Gurobi Optimizer version 10.0.0 build v10.0.0rc0 (linux64)
CPU model: 11th Gen Intel(R) Core(TM) i7-1185G7 @ 3.00GHz, instruction set [SSE2] Thread count: 4 physical cores, 4 logical processors, using up to 4 threads Optimize a model with 4 rows, 9 columns and 16 nonzeros Model fingerprint: 0x06e334a4 Variable types: 4 continuous, 5 integer (0 binary) Coefficient statistics: Matrix range [6e-02, 7e+00] Objective range [1e-02, 1e+00] Bounds range [5e+01, 1e+03] RHS range [0e+00, 0e+00] Found heuristic solution: objective -0.0000000 Presolve removed 1 rows and 5 columns Presolve time: 0.00s Presolved: 3 rows, 4 columns, 9 nonzeros Variable types: 0 continuous, 4 integer (0 binary) Found heuristic solution: objective 26.1000000 Root relaxation: objective 1.134615e+02, 2 iterations, 0.00 seconds (0.00 work units) Nodes | Current Node | Objective Bounds | Work Expl Unexpl | Obj Depth IntInf | Incumbent BestBd Gap | It/Node Time 0 0 113.46153 0 1 26.10000 113.46153 335% - 0s H 0 0 113.3000000 113.46153 0.14% - 0s H 0 0 113.4500000 113.46153 0.01% - 0s 0 0 113.46153 0 1 113.45000 113.46153 0.01% - 0s Explored 1 nodes (2 simplex iterations) in 0.00 seconds (0.00 work units) Thread count was 4 (of 4 available processors) Solution count 4: 113.45 113.3 26.1 -0 Optimal solution found (tolerance 1.00e-04) Best objective 1.134500000000e+02, best bound 1.134500000000e+02, gap 0.0000%
The read() command reads a model from a file and returns a
Model object. In the example, that object is placed into
m. There is no need to declare variables in the
Python language; you simply assign a value to a variable.
Note that read() accepts wildcard characters, so you could also have said:
gurobi> m = read("/opt/gurobi1000/linux64/*/*/coin*")
Note also that Gurobi commands that read or write files will also
function correctly with compressed files. If
7zip are installed on your machine and
available in your default path, then you simply need to add the
appropriate suffix (
to the file name to read or write compressed versions.
The next statement in the example, m.optimize(), invokes the
optimize method on the
Model object (you can obtain a
list of all methods on
Model objects by typing
help(m)). The node log generated by the
optimize() call shows the progress of the optimization. The most
prominent measurements of progress are the Incumbent and BestBd values,
which track the best solution found so far, and the bound on the best
possible solution. For more details on the node log,
refer to the MIP Logging section in the Gurobi Reference
The Gurobi optimization engine
finds an optimal solution with objective 113.45.