例句與造句

1. The 3-D organization of the genome can also be analyzed via eigendecomposition of the contact matrix.
2. So I don't really see what using the eigendecomposition method in the positive-semidefinite case gets you.
3. The spectral theorem also provides a eigendecomposition "', of the underlying vector space on which the operator acts.
4. When eigendecomposition is used on a matrix of measured, real data, the inverse may be less valid when all eigenvalues are used unmodified in the form above.
5. Another approach uses the Nystr鰉 method to approximate the eigendecomposition of the Gram matrix " K ", using only a random sample of the training set.
6. It's difficult to find eigendecomposition in a sentence. 用eigendecomposition造句挺難的
7. Things like singular value decomposition, various types of spectral theorems, eigendecomposition, etc . can be taught at the intersection of advanced linear algebra and numeric methods.
8. However, whereas the spectral method is based on the eigendecomposition of the particular boundary value problem, the spectral element method does not use that information and works for arbitrary elliptic boundary value problems.
9. In this form, DMD is a type of eigendecomposition is performed on S rather than A is because S is much smaller than A, so the computational cost of DMD is determined by the number of snapshots rather than the size of a snapshot.
10. Giving us the solutions of the eigenvalues for the matrix \ mathbf { A } as \ lambda = 1 or \ lambda = 3, and the resulting diagonal matrix from the eigendecomposition of \ mathbf { A } is thus \ begin { bmatrix } 1 & 0 \ \ 0 & 3 \ end { bmatrix }.
11. Thinking about it, the eigendecomposition should always work ( covariance matrices are always symmetric and therefore diagonalizable, and always have nonnegative eigenvalues ), so the procedure in the article probably does make sense ( though I suppose, to decide whether your matrix is positive-definite, you need to try the Cholesky decomposition anyway-it probably makes sense to go straight to the eigendecomposition if a large proportion of your matrices aren't positive-definite ) . talk ) 16 : 54, 18 April 2011 ( UTC)
12. Thinking about it, the eigendecomposition should always work ( covariance matrices are always symmetric and therefore diagonalizable, and always have nonnegative eigenvalues ), so the procedure in the article probably does make sense ( though I suppose, to decide whether your matrix is positive-definite, you need to try the Cholesky decomposition anyway-it probably makes sense to go straight to the eigendecomposition if a large proportion of your matrices aren't positive-definite ) . talk ) 16 : 54, 18 April 2011 ( UTC)