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Solving the model using the Gurobi command-line interface
The final step in solving our optimization problem is to pass the model to the Gurobi Optimizer. We'll use the Gurobi command-line interface here. As we've mentioned, our command-line interface is typically the simplest of our interfaces to use when solving a model stored in a file.
To use the command-line interface, you'll first need to bring up a
window that allows you to run command-line programs. On a Linux or
Mac system, you can use a Terminal window. On a Windows
system, you'll need to bring up a console
window (also
known as a cmd
window). To open a
console window, press the Start and R keys
on your keyboard
simultaneously, and then type cmd
into the Run window
that pops up. Alternatively, you can type cmd
into the
Search box that appears in the bottom-left after clicking
on the Windows Start button. (Note that the Gurobi Interactive
Shell, which was used earlier to test your license, does not
directly accept command-line program input, so it is not an
appropriate choice for this section).
The name of the Gurobi command-line tool is gurobi_cl
. To
invoke it, we simply need to type gurobi_cl
, followed by the
name of the model file.
For example, if our example is stored in file
c:\gurobi560\win64\examples\data\coins.lp
,
you would type the following command into your command-line window...
> gurobi_cl c:\gurobi560\win64\examples\data\coins.lpThis command should produce the following output...
Read LP format model from file c:\gurobi560\win64\examples\data\coins.lp Reading time = 0.00 seconds (null): 4 rows, 9 columns, 16 nonzeros Optimize a model with 4 rows, 9 columns and 16 nonzeros 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 Found heuristic solution: objective 113.3000000 Root relaxation: objective 1.134615e+02, 4 iterations, 0.00 seconds Nodes | Current Node | Objective Bounds | Work Expl Unexpl | Obj Depth IntInf | Incumbent BestBd Gap | It/Node Time 0 0 113.46154 0 1 113.30000 113.46154 0.14% - 0s 0 0 113.45952 0 1 113.30000 113.45952 0.14% - 0s H 0 0 113.4500000 113.45952 0.01% - 0s Explored 0 nodes (5 simplex iterations) in 0.00 seconds Thread count was 2 (of 2 available processors) Optimal solution found (tolerance 1.00e-04) Best objective 1.134500000000e+02, best bound 1.134500000000e+02, gap 0.0%Details on the format of the Gurobi log file can be found in the Gurobi Reference Manual. For now, you can simply note that the optimal objective value is 113.45. Recall that the objective is denoted in dollars. We can therefore conclude that by a proper choice of production plan, the Mint can produce $113.45 worth of coins using the available minerals. Moreover, because this value is optimal, we know that it is not possible to produce coins with value greater than $113.45!
It would clearly be useful to know the exact number of each coin
produced by this optimal plan. The gurobi_cl
command allows
you to set Gurobi parameters through command-line arguments. One
particularly useful parameter for the purposes of this example is
ResultFile
, which instructs the Gurobi Optimizer to write a file
once optimization is complete. The type of the file is encoded in the
suffix. If we request a .sol
file...
> gurobi_cl ResultFile=coins.sol coins.lp...then the command produces a file that contains solution values for the variables in the model...
# Objective value = 113.45 Pennies 0 Nickels 0 Dimes 2 Quarters 53 Dollars 100 Cu 999.8 Ni 46.9 Zi 50 Mn 30In the optimal solution, we'll produce 100 dollar coins, 53 quarters, and 2 dimes.
If we wanted to explore the parameters of the model (for example, to consider how the optimal solution changes with different quantities of available minerals), we could of course use a text editor to modify the input file. However, it is typically better to do such tests within a more powerful system. We'll now describe the Gurobi Interactive Shell, which provides an environment for creating, modifying, and experimenting with optimization models.