scipy.optimize注意constraints

最新推荐文章于 2025-04-12 01:31:52 发布

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最新推荐文章于 2025-04-12 01:31:52 发布

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求解带有等式约束的最优化问题，遇到了

Optimization terminated successfully. (Exit mode 0)

参考了问题https://stackoverflow.com/questions/37791680/scipy-optimize-minimize-slsqp-with-linear-constraints-fails，找到了原因是constraints写得有误。例子如下

import numpy as np
from scipy.optimize import minimize

# problem dimensions:
n = 10 # arbitrary integer set by user
m = 2 * n

# generate parameters A, b:
np.random.seed(123) # for reproducibility of results
A = np.random.randn(m,n)
b = np.random.randn(m)
z0 = np.random.randn(n+m)
# objective function:
def obj(z):
    vy = z[n:]
    return 0.5 * vy.dot(vy)

def cons_i(z, i):
    vx = z[:n]
    vy = z[n:]
    return A[i].dot(vx) - b[i] - vy[i]

# listable of scalar-output constraints input for SLSQP:
cons_per_i_one = [{'type':'eq', 'fun': lambda z: cons_i(z, i)} for i in np.arange(m)]  #这种就不行
cons_per_i = [{'type':'eq', 'fun': cons_i, 'args': (i,)} for i in np.arange(m)]  # 下面这种就可以

sol2 = minimize(obj, x0 = z0, constraints = cons_per_i, method = 'SLSQP', options={'disp': True})

还没具体看为什么会有这个问题，但把这里bug记录下