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### Description
The Singular Value Decomposition (SVD) of a matrix is a factorization of that matrix into three matrices. It has some interesting algebraic properties and conveys important geometrica…
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#86 already mentions adding SVD, but I thought it deserved a separate issue to track progress and would be very convenient for calculating eigenvectors / eigenvalues.
I have a largely untested and un…
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# New Operator
### Describe the operator
The Singular Value Decomposition (SVD) is a popular linear algebra technique to decompose any matrix into 3 matrices. The SVD is popular in many fields and…
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```
1. Description of the new feature
wrap a library that does singular value decomposition, check if Lapack does it
2. Classes and or functions involved
M_Matrix
```
Original issue reported on c…
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I have a draft to compute the singular value decomposition with semi-orthogonal U and V, which shows rank-revealing property and which can reduce down a lot of redundant computations
I would want to …
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Dense SVD is probably the most asked for feature in ndarray, and it would be nice to have some module which _robustly_ and efficiently computes them for arbitrary data. Standard applications include …
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Try to reformulate everything in terms of SVD as opposed to eigen decomposition in an attempt to avoid complex results for borderline cases.
The code in in exampleCode/Benchmarking.R shows the imp…
JoFAM updated
6 years ago
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Currently sympy has function returning condensed form of singular value decomposition. I was thinking of also implementing the complete SVD algorithm, because for some applications you may require the…
ks147 updated
3 years ago
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For many applications (recommender systems, information retrieval, etc.), only the top _k_ singular values (and their corresponding vectors) are required from the singular value decomposition.
Would …
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_From @caisq on January 20, 2018 17:16_
This can mimic tensorflow's o`tf.linalg.svd`: https://www.tensorflow.org/api_docs/python/tf/svd
This is useful for, among other things, the generating of or…