ignite-issues mailing list archives

Site index · List index
Message view « Date » · « Thread »
Top « Date » · « Thread »
From "Anton Dmitriev (JIRA)" <j...@apache.org>
Subject [jira] [Updated] (IGNITE-7438) LSQR: Sparse Equations and Least Squares for Lin Regression
Date Fri, 09 Feb 2018 10:57:02 GMT

     [ https://issues.apache.org/jira/browse/IGNITE-7438?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]

Anton Dmitriev updated IGNITE-7438:
-----------------------------------
    Description: 
This task consists of two parts:
 * Implementation of the +LSQR iterative solver+ of 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. 
 * Utilize all CPU resources by processing different parts of data on different cores.  

These advantages are achieved as result of changing [Golub-Kahan-Lanczos Bidiagonalization
Procedure|http://www.netlib.org/utk/people/JackDongarra/etemplates/node198.html] procedure
which is a core of LSQR algorithm and utilizing features of Partition Based Dataset implementation.

 

LSQR-based linear regression trainer is a trainer that uses the LSQR solver to solve a system
of linear equations which represents a linear regression problem.

  was:
This task consists of two parts:
 * Implementation of the +LSQR iterative solver+ of systems of linear equations.
 * Implementation of the +LSQR-based linear regression trainer+.

 

Apache Ignite LSQR iterative solver is based on [SciPy reference implementation|http://example.com/],
but it's distributed and can:
 * Efficiently work in cases when a data is distributed across a cluster. 
 * Utilize all CPU resources by processing different parts of data on different cores.  

These advantages are achieved as result of changing [Golub-Kahan-Lanczos Bidiagonalization
Procedure|http://www.netlib.org/utk/people/JackDongarra/etemplates/node198.html] procedure
which is a core of LSQR algorithm and utilizing features of Partition Based Dataset implementation.

 

LSQR-based linear regression trainer is a trainer that uses the LSQR solver to solve a system
of linear equations which represents a linear regression problem.


> 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
>             Fix For: 2.5
>
>
> This task consists of two parts:
>  * Implementation of the +LSQR iterative solver+ of 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. 
>  * Utilize all CPU resources by processing different parts of data on different cores.  
> These advantages are achieved as result of changing [Golub-Kahan-Lanczos Bidiagonalization
Procedure|http://www.netlib.org/utk/people/JackDongarra/etemplates/node198.html] procedure
which is a core of LSQR algorithm and utilizing features of Partition Based Dataset implementation.
>  
> LSQR-based linear regression trainer is a trainer that uses the LSQR solver to solve a
system of linear equations which represents a linear regression problem.



--
This message was sent by Atlassian JIRA
(v7.6.3#76005)

Mime
View raw message