# Truncated SVD of extended precision matrix

**URL:** <https://discourse.julialang.org/t/truncated-svd-of-extended-precision-matrix/78989>\
**Category:** Numerics\
**Tags:** extended-precision\
**Created:** [April 4, 2022, 8:03am UTC](https://discourse.julialang.org/t/truncated-svd-of-extended-precision-matrix/78989 "2022-04-04T08:03:22Z")\
**Posts on this page:** 1\
**Showing post:** 2

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**Author:** ![jd-foster](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/jd-foster/32/35824_2.png) [@jd-foster](https://discourse.julialang.org/u/jd-foster)\
**Post date:** [April 5, 2022, 10:58am UTC](https://discourse.julialang.org/t/truncated-svd-of-extended-precision-matrix/78989/2 "2022-04-05T10:58:06Z")

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I saw this earlier post:

> [@Truncated Singular Value Decomposition](https://discourse.julialang.org/t/truncated-singular-value-decomposition/42124):
>
> Hi. Given a matrix M I would like to compute its SVD truncated to rank k. I think this is possible without doing the full SVD. For example, Python has this: [sklearn.decomposition.TruncatedSVD — scikit-learn 1.1.2 documentation](https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.TruncatedSVD.html). The code I am currently using to do this is given below. The problem is that it computes SVD first, and then throws out the extra rows/columns, which can be quite costly if k is much smaller than rank of M. Is there a function in Julia to do this? using LinearAlgebra "re…

The recommendation is [TSVD.jl](https://github.com/JuliaLinearAlgebra/TSVD.jl).

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