可以利用cvxopt模块完成：
from cvxopt  import solvers, matrix
P = matrix([[1.0,0.0],[0.0,0.0]])   # matrix里区分int和double，所以数字后面都需要加小数点
q = matrix([3.0,4.0])
G = matrix([[-1.0,0.0,-1.0,2.0,3.0],[0.0,-1.0,-3.0,5.0,4.0]])
h = matrix([0.0,0.0,-15.0,100.0,80.0])
sol = solvers.qp(P,q,G,h)   # 调用优化函数solvers.qp求解
print sol['x']  # 打印结果，sol里面还有很多其他属性，读者可以自行了解

可以用python的cvxopt：
from cvxopt  import solvers, matrix
P = matrix([[1.0,0.0],[0.0,0.0]])   # matrix里区分int和double，所以数字后面都需要加小数点
q = matrix([3.0,4.0])
G = matrix([[-1.0,0.0,-1.0,2.0,3.0],[0.0,-1.0,-3.0,5.0,4.0]])
h = matrix([0.0,0.0,-15.0,100.0,80.0])
sol = solvers.qp(P,q,G,h)   # 调用优化函数solvers.qp求解
print sol['x']  # 打印结果，sol里面还有很多其他属性，读者可以自行了解

可以用Python的CVXOPT模块实现