linalg.svd 和 JacobiSVD<MatrixXf> svd ，结果不一样

Question

我正在将 Python 代码翻译成 C++ 版本，但是我发现这两个函数（linalg.svd 和 JacobiSVD svd ）产生不同的结果。我该怎么办？

A = np.array([[1, 2, 3, 4],
              [5, 6, 7, 8],
              [9, 10, 11, 12],
              [13, 14, 15, 16]])
U, S, V = svd(A,0)
print("U =\n", U)
print("S =\n", S)
print("V =\n", V)

 MatrixXf m = MatrixXf::Zero(4,4);
    m << 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16;
    cout << "Here is the matrix m:" << endl << m << endl;
    JacobiSVD<MatrixXf> svd(m, ComputeFullU | ComputeFullV);
    cout << "Its singular values are:" << endl << svd.singularValues() << endl;
    cout << "Its left singular vectors are the columns of the thin U matrix:" << endl << endl << svd.matrixU() << endl;
    cout << "Its right singular vectors are the columns of the thin V matrix:" << endl << endl << svd.matrixV() << endl;

虽然结果有些小偏差，但会影响我的工作吗？