Gurobi Optimizer Simple Tutorial

Gurobi is a mathematical optimizer which is designed to be the fastest and most powerful solver.

For:

LP(Linear Programming): Wiki link
QP(Quadratic Programming): Wiki link
QCQP(Quadratic Constrained Quadratic Program): Wiki link
ILP(Integer Linear Programming) or IP(Integer Programming): All of the unknown variables are required to be integers.
MIP(Mixed Integer Programming): Some of the unknown variables are integers.
MIQP, MIQCQP

Features

programming interface: C++, Java, .Net, Python
free license for student on the local machine.
Gurobi has client-server and Cloud computing capabilities. 提供云计算服务
Gurobi vs CPlex: Gurobi is easier to get the academic license and Gurobi has good support for python, which is easy to learn and code.

Quick Tutorial

After installed on your on computer, there are severak ways to use Gurobi:

Gurobi interactive shell

Start the IS: open the terminal, enter
```
exec gurobi.sh
```
Read a model from a file and return a Model object
```
 gurobi > m = read(‘model path’)
```
Invoke the optimize method on the Model object 如果计算过程被打算，再次启动时，会接着上次打断出继续计算
```
gurobi > m.optimize()
```
Reset the optimization and start from the begining
```
m.reset()
```

Inspect the value of the model variable in the solution

m.printAttr(‘X’)
v = m.getVars()
print (len(v))
print (v[0].varName, v[0].x)

Change the MIP strategy to find a good feasible solutions instead of the optimized one
```
m.setParam(‘MIPFocus’,1)
or
m.Param.MIPFocus=1
```
Show the full set of available parameter
```
paramHelp()
```
Show current loaded model
```
models()
```

Gurobi command line tool

Calculate the optimization model

gurobi_cl model_name

Output the result to a file:

gurobi_cl ResultFile = file_name model_name

Python script (Gurobi Python Interface)

in python environment:

from gurobipy import *

Python Script Example

A simple example

#!/usr/bin/python

# Copyright 2017, Gurobi Optimization, Inc.

# This example formulates and solves the following simple MIP model:
#  maximize
#        x +   y + 2 z
#  subject to
#        x + 2 y + 3 z <= 4
#        x +   y       >= 1
#  x, y, z binary

from gurobipy import *

try:

    # Create a new model
    m = Model("mip1")

    # Create variables
    x = m.addVar(vtype=GRB.BINARY, name="x")
    y = m.addVar(vtype=GRB.BINARY, name="y")
    z = m.addVar(vtype=GRB.BINARY, name="z")

    # Set objective
    m.setObjective(x + y + 2 * z, GRB.MAXIMIZE)
    # another way to build a objective
    # obj = LinExpr()
    # obj += x
    # obj += y
    # obj += 2*z
    # m.setObjective(obj, GRB.MAXIMIZE)


    # Add constraint: x + 2 y + 3 z <= 4
    m.addConstr(x + 2 * y + 3 * z <= 4, "c0")

    # Add constraint: x + y >= 1
    m.addConstr(x + y >= 1, "c1")

    m.optimize()

    for v in m.getVars():
        print('%s %g' % (v.varName, v.x))

    print('Obj: %g' % m.objVal)

except GurobiError as e:
    print('Error code ' + str(e.errno) + ": " + str(e))

except AttributeError:
    print('Encountered an attribute error')

Reference Document:

Here are the quick start guide, examples and reference manuals. Check Here

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