[ 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|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 LSQR solver to solve system of linear equations which represents 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. * Utilizes 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 LSQR solver to solve system of linear equations which represents 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 > > 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 LSQR solver to > solve system of linear equations which represents linear regression problem. -- This message was sent by Atlassian JIRA (v7.6.3#76005)