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https://issues.apache.org/jira/browse/IGNITE-7438?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Anton Dmitriev updated IGNITE-7438:
-----------------------------------
Description:
This task consists of two parts:
* Implementation of the LSQR iterative solver for systems of linear equations.
* Implementation of the LSQR-based linear regression trainer.
Apache Ignite LSQR iterative solver is based on [SciPy reference
implementation|[https://github.com/scipy/scipy/blob/master/scipy/sparse/linalg/isolve/lsqr.py#L98]],
but it's distributed and can efficiently work in cases when a data is
distributed across a cluster. Distribution is achieved as result of changing
[Golub-Kahan-Lanczos Bidiagonalization
Procedure|http://www.netlib.org/utk/people/JackDongarra/etemplates/node198.html]
was:
This task consists of two parts:
* Implementation of the LSQR iterative solver for systems of linear equations.
* Implementation of the LSQR-based linear regression trainer.
Apache Ignite LSQR iterative solver is based on [SciPy reference
implementation|[https://github.com/scipy/scipy/blob/master/scipy/sparse/linalg/isolve/lsqr.py#L98]|https://github.com/scipy/scipy/blob/master/scipy/sparse/linalg/isolve/lsqr.py#L98].],
but it's distributed and can efficiently work in cases when a data is
distributed across a cluster. Distribution is achieved as result of changing bi
> LSQR: Sparse Equations and Least Squares for Lin Regression
> -----------------------------------------------------------
>
> Key: IGNITE-7438
> URL: https://issues.apache.org/jira/browse/IGNITE-7438
> Project: Ignite
> Issue Type: New Feature
> Components: ml
> Reporter: Yury Babak
> Assignee: Anton Dmitriev
> Priority: Major
>
> This task consists of two parts:
> * Implementation of the LSQR iterative solver for systems of linear
> equations.
> * Implementation of the LSQR-based linear regression trainer.
> Apache Ignite LSQR iterative solver is based on [SciPy reference
> implementation|[https://github.com/scipy/scipy/blob/master/scipy/sparse/linalg/isolve/lsqr.py#L98]],
> but it's distributed and can efficiently work in cases when a data is
> distributed across a cluster. Distribution is achieved as result of changing
> [Golub-Kahan-Lanczos Bidiagonalization
> Procedure|http://www.netlib.org/utk/people/JackDongarra/etemplates/node198.html]
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