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I decided to actually look at the numpy SVD decomposition in preparation for my class today, and quickly found that it is wrong.
```
sage: import numpy
sage: numpy.linalg.svd?
---
Definition: nump…
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I would like to use `Harmony` to normalize my data, but i need the original shape to use in other part of my analytical pipeline.
`Harmony` takes as input principal components ($PC$), and outputs c…
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1. #### Coordinating Institute: IIT Bombay
2. #### Appr…
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Hi,
This is somewhat of an open issue. An eigenvalue decomposition is necessary for line mixing ECS computations to be effective. This is done. We also need partial derivatives w.r.t. user input…
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I want to implement singular value decomposition on a sparse matrix using the Scipy implementation. The [documentation](https://docs.scipy.org/doc/scipy/reference/generated/scipy.sparse.linalg.svds.ht…
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In OrdinaryKriging.train(), when lup.det() is infinity that the LUPDecomposition will get a wrong X. However, I can get a right X when using singular value decomposition. So, I think it should be uesd…
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When using `decomposition.takagi` there is a problem when the matrix is complex and has singular value close to zero. The funcion `takagi` has an optional argument called `rounding` which uses to deci…
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You mentioned in the README that the R implementation is slow due to the use of the `prcomp` function.
Have you taken a look at the [irlba](https://cran.r-project.org/web/packages/irlba/index.html) p…
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Currently (see [1]) if a state space system has eigenvalues at 0, it will return a scalar NaN for DC gain:
```python
except LinAlgError:
# zero eigenvalue: singular matrix
…
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Currently, there are no guarantees about ordering of eigenvalues and singular vectors returned by nalgebra's decompositions. A common convention throughout mathematical literature is to assume that ei…