Question

我试图在设置约束时最小化目标函数，使得x中的所有元素都相同。我的挑战是x中的元素数量不固定。现在，我有约束1：x [0] = x [1]，约束2：x [1] = x [2]等。但是，元素的数量可以增长，我不能预设所有的限制。我想知道我是否可以使用循环来更动态地设置这些约束？

'Minimize risks'
def objective(x):
    x = np.matrix(x)     
    return x.dot(covariance_matrix).dot(x.transpose())

'Make sure weight_AC equals weight_BB'
def constraint1(x):
    x = np.matrix(x)
    return np.float(x[0,0]) - np.float(x[0,1])


'Make sure weight_BB equals weight_GS'
def constraint2(x):
    x = np.matrix(x)    
    return np.float(x[0,1]) - np.float(x[0,2])

'Make sure weight_GS equals weight_AAPL'
def constraint3(x):
    x = np.matrix(x)   
    return np.float(x[0,2]) - np.float(x[0,3])

'Total weight equals 1'
def constraint4(x):
    x = np.matrix(x)   
    result = 1
    for i in range(0,x.size):        
        result = result - x[0,i]
    return result

con1 = {'type': 'eq', 'fun': constraint1}
con2 = {'type': 'eq', 'fun': constraint2}
con3 = {'type': 'eq', 'fun': constraint3}
con4 = {'type': 'eq', 'fun': constraint4}

cons = ([con1, con2, con3, con4])


solution = minimize(objective,x0,method='SLSQP',constraints = cons)

如何在Scipy优化中更动态地设置约束？

0 个答案: