qcqp.py 文件源码

def improve_ipopt(x0, prob, *args, **kwargs):
    try:
        import pyipopt
    except ImportError:
        raise Exception("PyIpopt package is not installed.")

    lb = pyipopt.NLP_LOWER_BOUND_INF
    ub = pyipopt.NLP_UPPER_BOUND_INF
    g_L = np.zeros(prob.m)
    for i in range(prob.m):
        if prob.fs[i].relop == '<=':
            g_L[i] = lb
    g_U = np.zeros(prob.m)

    def eval_grad_f(x, user_data = None):
        return 2*prob.f0.P.dot(x) + prob.f0.qarray
    def eval_g(x, user_data = None):
        return np.array([f.eval(x) for f in prob.fs])

    jac_grid = np.indices((prob.m, prob.n))
    jac_r = jac_grid[0].ravel()
    jac_c = jac_grid[1].ravel()
    def eval_jac_g(x, flag, user_data = None):
        if flag:
            return (jac_r, jac_c)
        else:
            return np.vstack([2*f.P.dot(x)+f.qarray for f in prob.fs])

    nlp = pyipopt.create(
        prob.n, lb*np.ones(prob.n), ub*np.ones(prob.n),
        prob.m, g_L, g_U, prob.m*prob.n, 0,
        prob.f0.eval, eval_grad_f,
        eval_g, eval_jac_g
    )
    try:
        x, zl, zu, constraint_multipliers, obj, status = nlp.solve(x0)
    except:
        pass

    return x