问当通过lambda调用函数时，尺寸是错误的。

EN

from scipy import optimize as sco
import numpy as np

def risk_measure(covMatrix, weights):
    risk = np.dot(weights, np.dot(covMatrix, weights))
    return risk

prescribed_esg = 6 # esg score between 0 and 10 used as threshold in the esg_constraint


# Covariance and return matrix
V = np.matrix([[84.76695659,  20.8854772,   20.62182415,  74.73652696,  14.35995947], 
              [20.8854772,   35.22429277,  12.95439707,  32.22912903,  12.96449085],
              [20.62182415,  12.95439707,  44.02079739,  38.73627316,   9.46608475],
              [74.73652696,  32.22912903,  38.73627316, 178.86640813,  33.40281336],
              [14.35995947,  12.96449085,   9.46608475,  33.40281336,  32.38514103]])
R = np.matrix([[-0.32264539, -0.08469428, 1.27628749, -0.23207085, 0.21012106]]).T
# Mean ESG score of each company
esgarr = np.matrix([[8.24336898, 4.6373262,  8.30657754, 4.65406417, 3.43620321]]).T

# Bounds and constraints
N = len(R) # number of instruments
bounds = ((-10,10),)*N # allow shorting, bounds of stocks
constraints = {'type': 'eq', 'fun': lambda weights: weights.sum() - 1}
esg_constraint = {'type': 'eq', 'fun': lambda weights: np.dot(weights, esgarr) - prescribed_esg}

esgmvp = sco.minimize(lambda x: risk_measure(V, x), # function to be minimized
                      N * [1 / N], # initial guess
                      bounds=bounds, # boundary conditions
                      constraints =[constraints, esg_constraint], # equality constraints)
                     ) 

esgmvp_weights = list(esgmvp['x'])
esgmvp_risk = esgmvp['fun']
esgmvp_esg = np.dot(esgmvp_weights, esgarr)

<ipython-input-252-0d6bf5d30ccf> in risk_measure(covMatrix, weights)
      3 
      4 def risk_measure(covMatrix, weights):
----> 5     risk = np.dot(weights, np.dot(covMatrix, weights))
      6     return risk
      7 

<__array_function__ internals> in dot(*args, **kwargs)

ValueError: shapes (5,) and (1,5) not aligned: 5 (dim 0) != 1 (dim 0)

weights = np.matrix([[1, 1, 1, 1, 1]])
risk = np.dot(weights, np.dot(V, weights.T))