Python cvxpy.Variable方法代码示例

# 需要导入模块: import cvxpy [as 别名]
# 或者: from cvxpy import Variable [as 别名]
def __init__(self):
        """
        A large r_scale for a small scale problem will
        ead to numerical problems as parameters become excessively small
        and (it seems) precision is lost in the dynamics.
        """

        self.set_random_initial_state()

        self.x_init = np.concatenate(((self.m_wet,), self.r_I_init, self.v_I_init, self.q_B_I_init, self.w_B_init))
        self.x_final = np.concatenate(((self.m_dry,), self.r_I_final, self.v_I_final, self.q_B_I_final, self.w_B_final))

        self.r_scale = np.linalg.norm(self.r_I_init)
        self.m_scale = self.m_wet

        # slack variable for linear constraint relaxation
        self.s_prime = cvx.Variable((K, 1), nonneg=True)

        # slack variable for lossless convexification
        # self.gamma = cvx.Variable(K, nonneg=True) 

# 需要导入模块: import cvxpy [as 别名]
# 或者: from cvxpy import Variable [as 别名]
def test_simple_batch_socp(self):
        set_seed(243)
        n = 5
        m = 1
        batch_size = 4

        P_sqrt = cp.Parameter((n, n), name='P_sqrt')
        q = cp.Parameter((n, 1), name='q')
        A = cp.Parameter((m, n), name='A')
        b = cp.Parameter((m, 1), name='b')

        x = cp.Variable((n, 1), name='x')

        objective = 0.5 * cp.sum_squares(P_sqrt @ x) + q.T @ x
        constraints = [A@x == b, cp.norm(x) <= 1]
        prob = cp.Problem(cp.Minimize(objective), constraints)

        prob_tch = CvxpyLayer(prob, [P_sqrt, q, A, b], [x])

        P_sqrt_tch = torch.randn(batch_size, n, n, requires_grad=True)
        q_tch = torch.randn(batch_size, n, 1, requires_grad=True)
        A_tch = torch.randn(batch_size, m, n, requires_grad=True)
        b_tch = torch.randn(batch_size, m, 1, requires_grad=True)

        torch.autograd.gradcheck(prob_tch, (P_sqrt_tch, q_tch, A_tch, b_tch)) 

# 需要导入模块: import cvxpy [as 别名]
# 或者: from cvxpy import Variable [as 别名]
def get_variable(self, name):
        """
        :param name: Name of the variable.
        :return The value of the variable.

        The following variables can be accessed:
        X
        U
        sigma
        nu
        """

        if name in self.var:
            return self.var[name].value
        else:
            print(f'Variable \'{name}\' does not exist.')
            return None 

# 需要导入模块: import cvxpy [as 别名]
# 或者: from cvxpy import Variable [as 别名]
def test_diff_fn(self):
        """Test generic differentiable function operator.
        """
        # Least squares.
        tmp = Variable(10)
        fn = diff_fn(tmp, lambda x: np.square(x).sum(), lambda x: 2 * x)
        rho = 1
        v = np.arange(10) * 1.0 - 5.0
        x = fn.prox(rho, v.copy())
        self.assertItemsAlmostEqual(x, v / (2 + rho))

        # -log
        n = 5
        tmp = Variable(n)
        fn = diff_fn(tmp, lambda x: -np.log(x).sum(), lambda x: -1.0 / x)
        rho = 2
        v = np.arange(n) * 2.0 + 1
        x = fn.prox(rho, v.copy())
        val = (v + np.sqrt(v**2 + 4 / rho)) / 2
        self.assertItemsAlmostEqual(x, val) 

# 需要导入模块: import cvxpy [as 别名]
# 或者: from cvxpy import Variable [as 别名]
def get_sudoku_matrix(n):
    X = np.array([[cp.Variable(n**2) for i in range(n**2)] for j in range(n**2)])
    cons = ([x >= 0 for row in X for x in row] +
            [cp.sum(x) == 1 for row in X for x in row] +
            [sum(row) == np.ones(n**2) for row in X] +
            [sum([row[i] for row in X]) == np.ones(n**2) for i in range(n**2)] +
            [sum([sum(row[i:i+n]) for row in X[j:j+n]]) == np.ones(n**2) for i in range(0,n**2,n) for j in range(0, n**2, n)])
    f = sum([cp.sum(x) for row in X for x in row])
    prob = cp.Problem(cp.Minimize(f), cons)

    A = np.asarray(prob.get_problem_data(cp.ECOS)[0]["A"].todense())
    A0 = [A[0]]
    rank = 1
    for i in range(1,A.shape[0]):
        if np.linalg.matrix_rank(A0+[A[i]], tol=1e-12) > rank:
            A0.append(A[i])
            rank += 1

    return np.array(A0) 

# 需要导入模块: import cvxpy [as 别名]
# 或者: from cvxpy import Variable [as 别名]
def __init__(self, n, Qpenalty, qp_solver, trueInit=False):
        super().__init__()

        self.qp_solver = qp_solver

        nx = (n**2)**3
        self.Q = Variable(Qpenalty*torch.eye(nx).double().cuda())
        self.Q_idx = spa.csc_matrix(self.Q.detach().cpu().numpy()).nonzero()

        self.G = Variable(-torch.eye(nx).double().cuda())
        self.h = Variable(torch.zeros(nx).double().cuda())
        t = get_sudoku_matrix(n)

        if trueInit:
            self.A = Parameter(torch.DoubleTensor(t).cuda())
        else:
            self.A = Parameter(torch.rand(t.shape).double().cuda())
        self.log_z0 = Parameter(torch.zeros(nx).double().cuda())
        # self.b = Variable(torch.ones(self.A.size(0)).double().cuda())

        if self.qp_solver == 'osqpth':
            t = torch.cat((self.A, self.G), dim=0)
            self.AG_idx = spa.csc_matrix(t.detach().cpu().numpy()).nonzero()

    # @profile 

# 需要导入模块: import cvxpy [as 别名]
# 或者: from cvxpy import Variable [as 别名]
def forward(self, puzzles):
        nBatch = puzzles.size(0)

        x = puzzles.view(nBatch,-1)
        x = self.fc_in(x)

        e = Variable(torch.Tensor())

        h = self.G.mv(self.z)+self.s
        x = QPFunction(verbose=False)(
            self.Q, x, self.G, h, e, e,
        )

        x = self.fc_out(x)
        x = x.view_as(puzzles)
        return x


# if __name__=="__main__":
#     sudoku = SolveSudoku(2, 0.2)
#     puzzle = [[4, 0, 0, 0], [0,0,4,0], [0,2,0,0], [0,0,0,1]]
#     Y = Variable(torch.DoubleTensor(np.array([[np.array(np.eye(5,4,-1)[i,:]) for i in row] for row in puzzle])).cuda())
#     solution = sudoku(Y.unsqueeze(0))
#     print(solution.view(1,4,4,4)) 

# 需要导入模块: import cvxpy [as 别名]
# 或者: from cvxpy import Variable [as 别名]
def get_inpaint_func_tv():
    def inpaint_func(image, mask):
        """Total variation inpainting"""
        inpainted = np.zeros_like(image)
        for c in range(image.shape[2]):
            image_c = image[:, :, c]
            mask_c = mask[:, :, c]
            if np.min(mask_c) > 0:
                # if mask is all ones, no need to inpaint
                inpainted[:, :, c] = image_c
            else:
                h, w = image_c.shape
                inpainted_c_var = cvxpy.Variable(h, w)
                obj = cvxpy.Minimize(cvxpy.tv(inpainted_c_var))
                constraints = [cvxpy.mul_elemwise(mask_c, inpainted_c_var) == cvxpy.mul_elemwise(mask_c, image_c)]
                prob = cvxpy.Problem(obj, constraints)
                # prob.solve(solver=cvxpy.SCS, max_iters=100, eps=1e-2)  # scs solver
                prob.solve()  # default solver
                inpainted[:, :, c] = inpainted_c_var.value
        return inpainted
    return inpaint_func 

# 需要导入模块: import cvxpy [as 别名]
# 或者: from cvxpy import Variable [as 别名]
def ball_con():
    # print(f'--- {sys._getframe().f_code.co_name} ---')
    print('ball con')
    npr.seed(0)

    n = 2

    A = cp.Parameter((n, n))
    z = cp.Parameter(n)
    p = cp.Parameter(n)
    x = cp.Variable(n)
    t = cp.Variable(n)
    obj = cp.Minimize(0.5 * cp.sum_squares(x - p))
    # TODO automate introduction of variables.
    cons = [0.5 * cp.sum_squares(A * t) <= 1, t == (x - z)]
    prob = cp.Problem(obj, cons)

    L = npr.randn(n, n)
    A.value = L.T
    z.value = npr.randn(n)
    p.value = npr.randn(n)

    prob.solve(solver=cp.SCS)
    print(x.value) 

# 需要导入模块: import cvxpy [as 别名]
# 或者: from cvxpy import Variable [as 别名]
def relu():
    # print(f'--- {sys._getframe().f_code.co_name} ---')
    print('relu')
    npr.seed(0)

    n = 4
    _x = cp.Parameter(n)
    _y = cp.Variable(n)
    obj = cp.Minimize(cp.sum_squares(_y - _x))
    cons = [_y >= 0]
    prob = cp.Problem(obj, cons)

    _x.value = npr.randn(n)

    prob.solve(solver=cp.SCS)
    print(_y.value) 

# 需要导入模块: import cvxpy [as 别名]
# 或者: from cvxpy import Variable [as 别名]
def running_example():
    print("running example")
    m = 20
    n = 10
    x = cp.Variable((n, 1))
    F = cp.Parameter((m, n))
    g = cp.Parameter((m, 1))
    lambd = cp.Parameter((1, 1), nonneg=True)
    objective_fn = cp.norm(F @ x - g) + lambd * cp.norm(x)
    constraints = [x >= 0]
    problem = cp.Problem(cp.Minimize(objective_fn), constraints)
    assert problem.is_dcp()
    assert problem.is_dpp()
    print("is_dpp: ", problem.is_dpp())

    F_t = torch.randn(m, n, requires_grad=True)
    g_t = torch.randn(m, 1, requires_grad=True)
    lambd_t = torch.rand(1, 1, requires_grad=True)
    layer = CvxpyLayer(problem, parameters=[F, g, lambd], variables=[x])
    x_star, = layer(F_t, g_t, lambd_t)
    x_star.sum().backward()
    print("F_t grad: ", F_t.grad)
    print("g_t grad: ", g_t.grad) 

# 需要导入模块: import cvxpy [as 别名]
# 或者: from cvxpy import Variable [as 别名]
def __call__(self, *parameters, solver_args={}):
        """Solve problem (or a batch of problems) corresponding to `parameters`

        Args:
          parameters: a sequence of tf.Tensors; the n-th Tensor specifies
                      the value for the n-th CVXPY Parameter. These Tensors
                      can be batched: if a Tensor has 3 dimensions, then its
                      first dimension is interpreted as the batch size.
          solver_args: a dict of optional arguments, to send to `diffcp`. Keys
                       should be the names of keyword arguments.

        Returns:
          a list of optimal variable values, one for each CVXPY Variable
          supplied to the constructor.
        """
        if len(parameters) != len(self.params):
            raise ValueError('A tensor must be provided for each CVXPY '
                             'parameter; received %d tensors, expected %d' % (
                                 len(parameters), len(self.params)))
        compute = tf.custom_gradient(
            lambda *parameters: self._compute(parameters, solver_args))
        return compute(*parameters) 

# 需要导入模块: import cvxpy [as 别名]
# 或者: from cvxpy import Variable [as 别名]
def test_lml(self):
        tf.random.set_seed(0)
        k = 2
        x = cp.Parameter(4)
        y = cp.Variable(4)
        obj = -x * y - cp.sum(cp.entr(y)) - cp.sum(cp.entr(1. - y))
        cons = [cp.sum(y) == k]
        problem = cp.Problem(cp.Minimize(obj), cons)
        lml = CvxpyLayer(problem, [x], [y])
        x_tf = tf.Variable([1., -1., -1., -1.], dtype=tf.float64)

        with tf.GradientTape() as tape:
            y_opt = lml(x_tf, solver_args={'eps': 1e-10})[0]
            loss = -tf.math.log(y_opt[1])

        def f():
            problem.solve(solver=cp.SCS, eps=1e-10)
            return -np.log(y.value[1])

        grad = tape.gradient(loss, [x_tf])
        numgrad = numerical_grad(f, [x], [x_tf])
        np.testing.assert_almost_equal(grad, numgrad, decimal=3) 

# 需要导入模块: import cvxpy [as 别名]
# 或者: from cvxpy import Variable [as 别名]
def test_example(self):
        n, m = 2, 3
        x = cp.Variable(n)
        A = cp.Parameter((m, n))
        b = cp.Parameter(m)
        constraints = [x >= 0]
        objective = cp.Minimize(0.5 * cp.pnorm(A @ x - b, p=1))
        problem = cp.Problem(objective, constraints)
        assert problem.is_dpp()

        cvxpylayer = CvxpyLayer(problem, parameters=[A, b], variables=[x])
        A_tch = torch.randn(m, n, requires_grad=True)
        b_tch = torch.randn(m, requires_grad=True)

        # solve the problem
        solution, = cvxpylayer(A_tch, b_tch)

        # compute the gradient of the sum of the solution with respect to A, b
        solution.sum().backward() 

# 需要导入模块: import cvxpy [as 别名]
# 或者: from cvxpy import Variable [as 别名]
def run(self):
        ''' Run experiment design. Returns a list of configurations and their scores'''
        training_points = list(self._get_training_points())
        num_points = len(training_points)

        all_training_features = np.array([_get_features(point) for point in training_points])
        covariance_matrices = list(_get_covariance_matrices(all_training_features))

        lambdas = cvx.Variable(num_points)

        objective = cvx.Minimize(_construct_objective(covariance_matrices, lambdas))
        constraints = self._construct_constraints(lambdas, training_points)

        problem = cvx.Problem(objective, constraints)

        opt_val = problem.solve()
        # TODO: Add debug logging
        # print "solution status ", problem.status
        # print "opt value is ", opt_val

        filtered_lambda_idxs = []
        for i in range(0, num_points):
            if lambdas[i].value > self.MIN_WEIGHT_FOR_SELECTION:
                filtered_lambda_idxs.append((lambdas[i].value, i))

        sorted_by_lambda = sorted(filtered_lambda_idxs, key=lambda t: t[0], reverse=True)
        return [(self._frac2parts(training_points[idx][0]), training_points[idx][0],
                 training_points[idx][1], l) for (l, idx) in sorted_by_lambda]