addMVar vs addVar

For MVar,

z = m.addMVar(shape = W, vtype=GRB.CONTINUOUS)

just use z[0] to indicate the first variable.

For Var,

x ={}

for i in range(1,3): x[i] = model.addVar(lb = 0, ub = GRB.INFINITY, vtype = GRB.CONTINUOUS, name = ‘x_’ + str(i))

Use x[1] to indicate the first variable.

addConstrs() vs addConstr()

model.addConstrs(x.sum(i, ‘*’) <= capacity[i] for i in range(5))

m.addConstrs((x[i,j] == 0 for i in range(4) for j in range(4) if i != j), name=’c’)

m.addConstr(x <= 1, name=’c0’)

for i in range(2): m.addConstr(x[i] + z[i] <= d[i])

Some mistakes you may make

1. Notice that

addMVar (shape, lb=0.0, ub=float(‘inf’), obj=0.0, vtype=GRB.CONTINUOUS, name=”” )

The lower bound is non-negative by default.

2. How to obtain the variables.

Bear in mind that the difference between global and local variables in the function.

m.getVarByName(‘varz[’ + str(k) + ‘]’)

k should be an integer.

ensure getvar is not None.

Two ways to change the type of variables

  1. v = m.getVarByName('pi[' + str(1) + ']')
v.vtype = GRB.BINARY

for var in m.getVars():
    var.vtype = GRB.BINARY

Ways to change the constraints:

1. Search model.chgCoeff(c0, x, 2.0)

  1. vx1 = m.getVarByName(‘x3’)

m.getConstrByName(‘c1’)

m.getCoeff(cc1,vx1)

Note that querying by name can be slow, so either try to avoid it, or do it once (and store the result in a dictionary).

  1. for cnstr in model.getConstrs():
    print(cnstr.sense, cnstr.rhs)

  2. for cnstr in pre.getConstrs():
for var in pre.getVars():
    print(pre.getCoeff(cnstr, var), end=" ")

  3. for cnstr in model.getConstrs():
print("Constraint %s: sense %s, RHS=%f" % (cnstr.ConstrName, cnstr.Sense, cnstr.RHS))
row = model.getRow(cnstr)
for k in range(row.size()):
    print("Variable %s, coefficient %f" % (row.getVar(k).VarName, row.getCoeff(k))

c1 = model.addConstr(x + y >= 1.0, “c1”) c1.rhs = 2.0

To change all rhs. Search Model.setAttr() model.setAttr(“rhs”, conss, values)

print(m.display())

It will print out the model.

Several ways to change model.

  1. If you want to adjust a model from the existing model, you can only get the var and constrs by name.

  2. You could write the adjustment in one function, then change the existing model.

  3. Use import from another file, you can also use the defined variables.

An example: how to use MVars to add 2D-constraints.

import gurobipy as gp
import numpy as np

m = 3
n = 4

model = gp.Model()
X = model.addMVar((m,n))
a = np.random.rand(m)
b = np.random.rand(n)

# Set objective to a @ X @ b
model.setObjective(sum(b[j] * a @ X[:, j] for j in range(n)))

Tips: use addLConstr to add constraints.

import sys sys.path.append(‘../CodeDeterministic’)

调用不同文件目录下的函数

linearization

https://blog.csdn.net/sanpi329/article/details/123603349

https://zhuanlan.zhihu.com/p/63204658