Python二次规划和线性规划使用实例

conda install -c conda-forge cvxopt

def cvxopt_solve_qp(P, q, G=None, h=None, A=None, b=None):
  P = .5 * (P + P.T) # make sure P is symmetric
  args = [cvxopt.matrix(P), cvxopt.matrix(q)]
  if G is not None:
    args.extend([cvxopt.matrix(G), cvxopt.matrix(h)])
    if A is not None:
      args.extend([cvxopt.matrix(A), cvxopt.matrix(b)])
  sol = cvxopt.solvers.qp(*args)
  if 'optimal' not in sol['status']:
    return None
  return np.array(sol['x']).reshape((P.shape[1],))

def cvxopt_solve_lp(f, A, b):
  #args = [cvxopt.matrix(f), cvxopt.matrix(A), cvxopt.matrix(b)]
  #cvxopt.solvers.lp(*args)
  sol = cvxopt.solvers.lp(cvxopt.matrix(f), cvxopt.matrix(A), cvxopt.matrix(b))
  return np.array(sol['x']).reshape((f.shape[0],))