To solve a quadratic program, simply build the matrices that define it and call the solve_qp() function:

from numpy import array, dot
from qpsolvers import solve_qp

M = array([[1., 2., 0.], [-8., 3., 2.], [0., 1., 1.]])
P = dot(M.T, M)  # quick way to build a symmetric matrix
q = dot(array([3., 2., 3.]), M).reshape((3,))
G = array([[1., 2., 1.], [2., 0., 1.], [-1., 2., -1.]])
h = array([3., 2., -2.]).reshape((3,))
A = array([1., 1., 1.])
b = array([1.])

x = solve_qp(P, q, G, h, A, b)
print(f"QP solution: x = {x}")

This example outputs the solution [0.30769231, -0.69230769,  1.38461538]. The solve_qp() function accepts a solver keyword argument to select the backend solver:

qpsolvers.solve_qp(P, q, G=None, h=None, A=None, b=None, lb=None, ub=None, solver='quadprog', initvals=None, sym_proj=False, verbose=False, **kwargs)

Solve a Quadratic Program defined as:

$\begin{split}\begin{split}\begin{array}{ll} \mbox{minimize} & \frac{1}{2} x^T P x + q^T x \\ \mbox{subject to} & G x \leq h \\ & A x = b \\ & lb \leq x \leq ub \end{array}\end{split}\end{split}$

using one of the available QP solvers.

Parameters
• P (Union[ndarray, csc_matrix, spmatrix]) – Symmetric quadratic-cost matrix (most solvers require it to be definite as well).

• q (Union[ndarray, csc_matrix, spmatrix]) – Quadratic-cost vector.

• G (Union[ndarray, csc_matrix, spmatrix, None]) – Linear inequality matrix.

• h (Union[ndarray, csc_matrix, spmatrix, None]) – Linear inequality vector.

• A (Union[ndarray, csc_matrix, spmatrix, None]) – Linear equality matrix.

• b (Union[ndarray, csc_matrix, spmatrix, None]) – Linear equality vector.

• lb (Union[ndarray, csc_matrix, spmatrix, None]) – Lower bound constraint vector.

• ub (Union[ndarray, csc_matrix, spmatrix, None]) – Upper bound constraint vector.

• solver (str) – Name of the QP solver, to choose in qpsolvers.available_solvers.

• initvals (Union[ndarray, csc_matrix, spmatrix, None]) – Vector of initial $$x$$ values used to warm-start the solver.

• sym_proj (bool) – Set to True when the $$P$$ matrix provided is not symmetric.

• verbose (bool) – Set to True to print out extra information.

Return type

Optional[ndarray]

Returns

Optimal solution if found, otherwise None.

Raises

ValueError – If the problem is not correctly defined.

Note

In quadratic programming, the matrix $$P$$ should be symmetric. Many solvers (including CVXOPT, OSQP and quadprog) leverage this property and may return unintended results when it is not the case. You can set sym_proj=True to project $$P$$ on its symmetric part, at the cost of some computation time.

Notes

Extra keyword arguments given to this function are forwarded to the underlying solvers. For example, OSQP has a setting eps_abs which we can provide by solve_qp(P, q, G, h, solver='osqp', eps_abs=1e-4).

Installed solvers are listed in:

qpsolvers.available_solvers = ['cvxopt', 'cvxpy', 'ecos', 'gurobi', 'mosek', 'osqp', 'qpoases', 'quadprog', 'scs']

Built-in mutable sequence.

If no argument is given, the constructor creates a new empty list. The argument must be an iterable if specified.

See the examples/ folder in the repository for other use cases. For a more general introduction you can also check out this post on quadratic programming in Python.