Question

Gurobi在python中计算l1范数

我试图通过在python中使用Gurobi来计算l1范数。由于我是python和Gurobi的新手，我在这里寻求帮助。

模型是：

minimize 1^T(r+ + r-)
s.t. y - X beta = r+ - r-
r+ >= 0 and r- >= 0

其中y是n向量，X是n-by-p矩阵。 r +，r-是n向量，β是p向量这是我的代码，我不知道什么是错的，有人可以帮助我吗？

row col = X.shape

# import Gurobi
from gurobipy import *

# model
m = Model("l1-norm")

# create decision variables
r_plus = []
for i in range(row):
    r_plus = m.addVar(name="r_plus%d" % i)
r_minus = []
for i in range(row):
    r_minus = m.addVar(name = "r_minu%d" % i)
beta = []
for j in range(col):
    beta = m.addVar(name = "beta%d" % j)

# Update model to integrate new variables
m.update()

# set objective
m.setObjective(sum(r_plus) + sum(r_minus), GRB.MINIMIZE)

# add model constraint
for i in range(row):
m.addConstr(y[i] - quicksum(X[[i], j] * beta[j] for j in range(col)) == r_plus[i] - r_minus[i])

# solve
m.optimize()

Answer 1

您正在创建错误的变量数组，它应该是

r_plus = []
for i in range(row):
    r_plus.append(m.addVar(name="r_plus%d" % i))
r_minus = []
for i in range(row):
    r_minus.append(m.addVar(name = "r_minu%d" % i))
beta = []
for j in range(col):
    beta.append(m.addVar(name = "beta%d" % j))

或更简单

r_plus  = [m.addVar(name="r_plus%d" % i) for i in range(row)]
r_minus = [m.addVar(name="r_minu%d" % i) for i in range(row)]
beta = [m.addVar(name = "beta%d" % j) for j in range(col)]

Gurobi在python中计算lp范数

1 个答案: