使用numpy在python中进行二次编程?

CVXOPT

def quadprog_solve_qp(P, q, G=None, h=None, A=None, b=None):
    qp_G = .5 * (P + P.T)   # make sure P is symmetric
    qp_a = -q
    if A is not None:
        qp_C = -numpy.vstack([A, G]).T
        qp_b = -numpy.hstack([b, h])
        meq = A.shape[0]
    else:  # no equality constraint
        qp_C = -G.T
        qp_b = -h
        meq = 0
    return quadprog.solve_qp(qp_G, qp_a, qp_C, qp_b, meq)[0] 

OSQP OSQP documentation demo qpsolvers repository

H G CSC format

import numpy as np
import scipy.sparse as spa
import osqp


def quadprog(P, q, G=None, h=None, A=None, b=None,
             initvals=None, verbose=True):
    l = -np.inf * np.ones(len(h))
    if A is not None:
        qp_A = spa.vstack([G, A]).tocsc()
        qp_l = np.hstack([l, b])
        qp_u = np.hstack([h, b])
    else:  # no equality constraint
        qp_A = G
        qp_l = l
        qp_u = h
    model = osqp.OSQP()
    model.setup(P=P, q=q,
                A=qp_A, l=qp_l, u=qp_u, verbose=verbose)
    if initvals is not None:
        model.warm_start(x=initvals)
    results = model.solve()
    return results.x, results.info.status


# Generate problem data
n = 2   # Variables
H = spa.csc_matrix([[4, 1], [1, 2]])
f = np.array([1, 1])
G = spa.csc_matrix([[1, 0], [0, 1]])
h = np.array([0.7, 0.7])
A = spa.csc_matrix([[1, 1]])
b = np.array([1.])

# Initial point
q0 = np.ones(n)

x, status = quadprog(H, f, G, h, A, b, initvals=q0, verbose=True)