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Cholesky should always be faster. otherwise we've done something seriously wrong!
@tsgut @ioannisPApapadopoulos @DanielVandH
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开发计划可参考以下节点:
1. 方案撰写,xx.xx~xx.xx
2. 开发自测,xx.xx~xx.xx
3. 提出 PR/MR,xx.xx~xx.xx
4. review( 3个赞),xx.xx~xx.xx
6. maintainer 合入
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there seems to be some limit between the n_components and n_features. If I try and create a model with
```
n_components=1
n_features=99
```
it will fail with `_LinAlgError: linalg.cholesky: The…
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### Description
After building jaxlib as per the instructions and installing jax-metal, upon testing with an existing model which works fine using CPU (and GPU on linux), I get the following error.…
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I have created a cholesky decomposition of my covariance matrix as follows: `L = T.slinalg.cholesky(self.cov)`, a 210x210 matrix. However, when I try to use `Linv_delta = T.slinalg.solve(L,T.transpose…
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This might be a good solution for what to do when the Cholesky fails due to almost-singular matrices.
http://www.cs.umd.edu/~oleary/tr/tr4807.pdf
Comments and thoughts welcome. With thanks to S…
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### Is your feature request related to a problem? Please describe.
I am working on an algorithm that makes multiple calculations of the form `cho_solve(cho_factor(A), B)`, with the same (and rather l…
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**Feature functionality**
computes the cholesky decomposition
Intended scope:
- real/complex matrices
- `split` $\in${ `0,1,None`}
- of course, appropriate scalability of the routine is desired…
mtar updated
8 months ago
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#25782
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