# Import packages.
import cvxpy as cp
import numpy as np
# Generate a random non-trivial quadratic program.
m = 15
n = 10
p = 5
np.random.seed(1)
P = np.random.randn(n, n)
P = P.T @ P
q = np.random.randn(n)
G = np.random.randn(m, n)
h = G @ np.random.randn(n)
A = np.random.randn(p, n)
b = np.random.randn(p)
# Define and solve the CVXPY problem.
x = cp.Variable(n)
prob = cp.Problem(cp.Minimize((1/2)*cp.quad_form(x, P) + q.T @ x),
[G @ x <= h,
A @ x == b])
prob.solve()
# Print result.
print("\nThe optimal value is", prob.value)
print("A solution x is")
print(x.value)
print("A dual solution corresponding to the inequality constraints is")
print(prob.constraints[0].dual_value)
The optimal value is 86.89141585569907 A solution x is [-1.68244521 0.29769913 -2.38772183 -2.79986015 1.18270433 -0.20911897 -4.50993526 3.76683701 -0.45770675 -3.78589638] A dual solution corresponding to the inequality constraints is [ 0. 0. 0. 0. 0. 10.45538054 0. 0. 0. 39.67365045 0. 0. 0. 20.79927156 6.54115873]