[GSoC 2019] Matrix pseudoinverse and Least squares solver

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[GSoC 2019] Matrix pseudoinverse and Least squares solver

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Hi all,

I was investigating some methods to perform least squares algorithms, and I noticed we currently do not have methods of doing so in the development branch. 

To the extent of my knowledge, we also do not have a method of determining matrix inverses or pseudoinverse. All of the existing implementations I found (e.g. rTensor) of CP decomposition have a method of determining a matrix's pseudoinverse. 

For GSoC, I initially based my proposal around implementing tensor decomposition algorithms. However, would it be a better choice to focus more on least squares methods like QR-factorization” And SVD? Furthermore, was this already attempted in a past GSoC project?

Thanks,

Thomas  

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Re: [GSoC 2019] Matrix pseudoinverse and Least squares solver

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Hi,

we have a solver algorithms done in GSOC which is not yet integrated into the main branch. I think we should start from there. The GSOC was very good and the algorithm stable. So if we have it integrated into the main branch, then implementing least-square solvers and other algorithm will be very easy.


On Tue, Apr 2, 2019 at 4:10 AM Thomas Yang via ublas <[hidden email]> wrote:
Hi all,

I was investigating some methods to perform least squares algorithms, and I noticed we currently do not have methods of doing so in the development branch. 

To the extent of my knowledge, we also do not have a method of determining matrix inverses or pseudoinverse. All of the existing implementations I found (e.g. rTensor) of CP decomposition have a method of determining a matrix's pseudoinverse. 

For GSoC, I initially based my proposal around implementing tensor decomposition algorithms. However, would it be a better choice to focus more on least squares methods like QR-factorization” And SVD? Furthermore, was this already attempted in a past GSoC project?

Thanks,

Thomas  
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