Quadratic program

A quadratic program is an optimization problem with a quadratic objective and affine equality and inequality constraints. A common standard form is the following:

\[\begin{split}\begin{array}{ll} \mbox{minimize} & (1/2)x^TPx + q^Tx\\ \mbox{subject to} & Gx \leq h \\ & Ax = b. \end{array}\end{split}\]

Here \(P \in \mathcal{S}^{n}_+\), \(q \in \mathcal{R}^n\), \(G \in \mathcal{R}^{m \times n}\), \(h \in \mathcal{R}^m\), \(A \in \mathcal{R}^{p \times n}\), and \(b \in \mathcal{R}^n\) are problem data and \(x \in \mathcal{R}^{n}\) is the optimization variable. The inequality constraint \(Gx \leq h\) is elementwise.

A simple example of a quadratic program arises in finance. Suppose we have \(n\) different stocks, an estimate \(r \in \mathcal{R}^n\) of the expected return on each stock, and an estimate \(\Sigma \in \mathcal{S}^{n}_+\) of the covariance of the returns. Then we solve the optimization problem

\[\begin{split}\begin{array}{ll} \mbox{minimize} & (1/2)x^T\Sigma x - r^Tx\\ \mbox{subject to} & x \geq 0 \\ & \mathbf{1}^Tx = 1, \end{array}\end{split}\]

to find a portfolio allocation \(x \in \mathcal{R}^n_+\) that optimally balances expected return and variance of return.

When we solve a quadratic program, in addition to a solution \(x^\star\), we obtain a dual solution \(\lambda^\star\) corresponding to the inequality constraints. A positive entry \(\lambda^\star_i\) indicates that the constraint \(g_i^Tx \leq h_i\) holds with equality for \(x^\star\) and suggests that changing \(h_i\) would change the optimal value.

Example

In the following code, we solve a quadratic program with CVXPY.

# 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 86.89141585569918
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]