MISDP/MISOCP在cvxpy

我试图解决以下问题在CVXPY.

问题是一个混合整数的SDP是由于私营部门司的矩阵，我们的解决。 然而，根据 该列表， 它看起来好像没有解决可以处理这样的问题。

我们可以使用的事实， A 是2x2矩阵以某种方式将这一混合整数SOCP问题？

import cvxpy as cp
import matplotlib.pyplot as plt
import numpy as np

np.random.seed(271828) 
m = 2; n = 50
x = np.random.randn(m,n)

off = cp.Variable(boolean=True)

A = cp.Variable((2,2), PSD=True)
b = cp.Variable(2)
obj = cp.Maximize(cp.log_det(A))
constraints = [ cp.norm(A@x[:,i] + b) <= 1 + 20*off for i in range(n) ]
constraints += [cp.sum(off) <= 20]

prob = cp.Problem(obj, constraints)
optval = prob.solve(solver='XPRESS', verbose=False) # seems to work, although it's not super accurate

print(f"Optimum value: {optval}")

# plot the ellipse and data
angles = np.linspace(0, 2*np.pi, 200)
rhs = np.row_stack((np.cos(angles) - b.value[0], np.sin(angles) - b.value[1]))
ellipse = np.linalg.solve(A.value, rhs)

plt.scatter(x[0,:], x[1,:])
plt.plot(ellipse[0,:].T, ellipse[1,:].T)
plt.xlabel('Dimension 1'); plt.ylabel('Dimension 2')
plt.title('Minimum Volume Ellipsoid')
plt.show()