python - 用 numpy 减少循环

Question

我们正在尝试实现给定的 Modified Gram Schmidt 算法:

我们首先尝试以下面的方式实现第 5-7 行:

for j in range(i+1, N):
    R[i, j] = np.matmul(Q[:, i].transpose(), U[:, j])
    u = U[:, j] - R[i, j] * Q[:, i]
    U[:, j] = u

为了减少运行时间，我们尝试用矩阵运算替换循环，如下所示:

# we changed the inner loop to matrix operations in order to improve running time
R[i, i + 1:] = np.matmul(Q[:, i] , U[:, i + 1:])
U[:, i + 1:] = U[:, i + 1:] - R[i, i + 1:] * np.transpose(np.tile(Q[:, i], (N - i - 1, 1)))

结果并不相同，但非常相似。我们的二审有问题吗？
谢谢!
编辑:
完整的功能是:

def gram_schmidt2(A):
    """
    decomposes a matrix A ∈ R into a product A = QR of an
    orthogonal matrix Q (i.e. QTQ = I) and an upper triangular matrix R (i.e. entries below
    the main diagonal are zero)

    :return: Q,R
    """
    N = np.shape(A)[0]
    U = A.copy()
    Q = np.zeros((N, N), dtype=np.float64)
    R = np.zeros((N, N), dtype=np.float64)
    for i in range(N):
        R[i, i] = np.linalg.norm(U[:, i])
        # Handling devision by zero by exiting the program as was advised in the forum
        if R[i, i] == 0:
            zero_devision_error(gram_schmidt._name_)
        Q[:, i] = np.divide(U[:, i], R[i, i])
        # we changed the inner loop to matrix operatins in oreder to improve running time
        for j in range(i+1, N):
            R[i, j] = np.matmul(Q[:, i].transpose(), U[:, j])
            u = U[:, j] - R[i, j] * Q[:, i]
            U[:, j] = u
    return Q, R

和:

def gram_schmidt1(A):
    """
    decomposes a matrix A ∈ R into a product A = QR of an
    orthogonal matrix Q (i.e. QTQ = I) and an upper triangular matrix R (i.e. entries below
    the main diagonal are zero)

    :return: Q,R
    """
    N = np.shape(A)[0]
    U = A.copy()
    Q = np.zeros((N, N), dtype=np.float64)
    R = np.zeros((N, N), dtype=np.float64)
    for i in range(N):
        R[i, i] = np.linalg.norm(U[:, i])
        # Handling devision by zero by exiting the program as was advised in the forum
        if R[i, i] == 0:
            zero_devision_error(gram_schmidt._name_)
        Q[:, i] = np.divide(U[:, i], R[i, i])
        # we changed the inner loop to matrix operatins in oreder to improve running time
        R[i, i + 1:] = np.matmul(Q[:, i] , U[:, i + 1:])
        U[:, i + 1:] = U[:, i + 1:] - R[i, i + 1:] * np.transpose(np.tile(Q[:, i], (N - i - 1, 1)))
    return Q, R

当我们在矩阵上运行函数时:

[[ 1.00000000e+00 -1.98592571e-02 -1.00365698e-04 -1.45204974e-03
  -9.95711793e-01 -1.77405377e-04 -7.68526195e-03]
 [-1.98592571e-02  1.00000000e+00 -1.77809186e-02 -1.55937174e-01
  -9.80881385e-03 -2.05317715e-02 -2.01456899e-01]
 [-1.00365698e-04 -1.77809186e-02  1.00000000e+00 -1.87979660e-01
  -5.12368040e-05 -8.35323206e-01 -4.59007949e-05]
 [-1.45204974e-03 -1.55937174e-01 -1.87979660e-01  1.00000000e+00
  -8.69848133e-04 -3.64095785e-01 -5.55408776e-04]
 [-9.95711793e-01 -9.80881385e-03 -5.12368040e-05 -8.69848133e-04
   1.00000000e+00 -9.54867422e-05 -5.92716161e-03]
 [-1.77405377e-04 -2.05317715e-02 -8.35323206e-01 -3.64095785e-01
  -9.54867422e-05  1.00000000e+00 -5.55505343e-05]
 [-7.68526195e-03 -2.01456899e-01 -4.59007949e-05 -5.55408776e-04
  -5.92716161e-03 -5.55505343e-05  1.00000000e+00]]

我们得到不同的这些输出:
对于克 shmidt 1:
问:

[[ 7.34036501e-01 -8.55006295e-04 -8.15634583e-03 -9.24967764e-02
  -4.91879501e-02 -4.90769704e-01  1.58268518e-01]
 [-2.78569770e-04  7.14001661e-01 -2.70586659e-03 -2.70735367e-02
   5.78840577e-01  2.37376069e-01  1.97835647e-02]
 [-2.48309244e-03 -2.34709092e-03  7.38351181e-01  2.63187853e-01
  -3.35473487e-01  3.38823696e-01  3.36320600e-01]
 [-4.27658449e-03 -2.12584453e-03 -6.70730760e-01  3.82666405e-01
  -3.44451231e-01  3.46085878e-01 -7.71559024e-01]
 [-6.53970073e-04 -7.00117873e-01 -2.68125144e-03 -2.31536583e-02
   5.94568750e-01  2.38329853e-01 -2.76969906e-01]
 [-9.26674350e-02 -5.07961588e-03 -6.97972068e-02 -8.79879575e-01
  -2.78679804e-01  2.78781202e-01  0.00000000e+00]
 [-6.72739327e-01  1.73894101e-04  2.25707383e-03  1.69052581e-02
  -1.26723666e-02 -5.77668322e-01 -4.35238424e-01]]

回复:

[[ 1.36233007e+00  1.11436069e-03  1.04418015e-02  1.27072186e-02
   1.10993692e-03 -7.82681536e-02 -1.33081669e+00]
 [ 0.00000000e+00  1.40055740e+00  5.29057231e-04  1.44628716e-03
  -1.40014587e+00  3.57535802e-04  2.25417515e-03]
 [ 0.00000000e+00  0.00000000e+00  1.35440586e+00 -1.33059602e+00
   6.67148806e-04 -3.51561140e-02  2.23809829e-02]
 [ 0.00000000e+00  0.00000000e+00  0.00000000e+00  2.81147599e-01
   1.33951520e-02 -9.55057795e-01  2.36910667e-01]
 [ 0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00
   3.37143743e-02 -1.97436093e-01  7.90539705e-02]
 [ 0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00
   0.00000000e+00  3.40545951e-01 -1.75971454e-01]
 [ 0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00
   0.00000000e+00  0.00000000e+00  3.50740324e-16]]

对于克什密特 2:
问:

    [[ 7.34036501e-01 -8.55006295e-04 -8.15634583e-03 -9.24967764e-02
  -4.91879501e-02 -4.90769704e-01  4.55677949e-01]
 [-2.78569770e-04  7.14001661e-01 -2.70586659e-03 -2.70735367e-02
   5.78840577e-01  2.37376069e-01 -1.89865812e-01]
 [-2.48309244e-03 -2.34709092e-03  7.38351181e-01  2.63187853e-01
  -3.35473487e-01  3.38823696e-01  9.49329061e-02]
 [-4.27658449e-03 -2.12584453e-03 -6.70730760e-01  3.82666405e-01
  -3.44451231e-01  3.46085878e-01 -4.36691368e-01]
 [-6.53970073e-04 -7.00117873e-01 -2.68125144e-03 -2.31536583e-02
   5.94568750e-01  2.38329853e-01 -1.13919487e-01]
 [-9.26674350e-02 -5.07961588e-03 -6.97972068e-02 -8.79879575e-01
  -2.78679804e-01  2.78781202e-01 -1.51892650e-01]
 [-6.72739327e-01  1.73894101e-04  2.25707383e-03  1.69052581e-02
  -1.26723666e-02 -5.77668322e-01 -7.21490087e-01]]

回复:

[[ 1.36233007e+00  1.11436069e-03  1.04418015e-02  1.27072186e-02
   1.10993692e-03 -7.82681536e-02 -1.33081669e+00]
 [ 0.00000000e+00  1.40055740e+00  5.29057231e-04  1.44628716e-03
  -1.40014587e+00  3.57535802e-04  2.25417515e-03]
 [ 0.00000000e+00  0.00000000e+00  1.35440586e+00 -1.33059602e+00
   6.67148806e-04 -3.51561140e-02  2.23809829e-02]
 [ 0.00000000e+00  0.00000000e+00  0.00000000e+00  2.81147599e-01
   1.33951520e-02 -9.55057795e-01  2.36910667e-01]
 [ 0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00
   3.37143743e-02 -1.97436093e-01  7.90539705e-02]
 [ 0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00
   0.00000000e+00  3.40545951e-01 -1.75971454e-01]
 [ 0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00
   0.00000000e+00  0.00000000e+00  3.65463051e-16]]

Answer 1

以下代码以更有效的方式执行您想要的操作:

        Q_i = Q[:, i].reshape(1,-1)
        R[i,i+1:] = np.matmul(Q_i , U[:,i+1:])
        U[:,i+1:] -=  np.multiply(R[i,i+1:] , Q_i.T)

第一行只是为了方便，使代码更具可读性。
除了最后一行之外，一切都与您的原始提案相同。最后一行执行逐元素乘法，这最终是您在内循环的最后一行中所做的。
关于结果的差异:
你的代码没问题，两者都是一样的。当您处理浮点数时，您不应该测试为 A == B .相反，我建议您检查两个数组的不同之处。
特别是，运行

Q1,R1 = gram_schmidt2(A)
Q2,R2 = gram_schmidt1(A)

(Q1 - Q2).mean()
(R1 - R2).mean()

分别给出: -5.4997372770547595e-09 and -5.2465803662044656e-18已经非常接近 0 了。 1e-18 低于 dtype np.float64 的错误，所以你很好。
如果您运行差异 3*0.1 - 0.3，您可以检查这一点(关于1e-17)
矩阵 Q 的误差较大，因为它来自浮点数之间的除法，如果矩阵元素的大小很小(有时就是这种情况)，则会增加误差。
关于运行时:
运行您的代码的两个版本时，我得到了相似的运行时间:( 243 µs ± 25.5 µs 使用循环， 241 µs ± 6.82 µs 使用您的第二个版本)；而此处提供的代码实现了 152 µs ± 1.49 µs .