[ 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 for 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][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]], 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] > 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|http://example.com][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] -- This message was sent by Atlassian JIRA (v7.6.3#76005)