[jira] [Updated] (IGNITE-7438) LSQR: Sparse Equations and Least Squares for Lin Regression
[ 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)
[jira] [Updated] (IGNITE-7438) LSQR: Sparse Equations and Least Squares for Lin Regression
[ 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 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|https://issues.apache.org/jira/browse/IGNITE-7437]. 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 > 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|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. -- This message was sent by Atlassian JIRA (v7.6.3#76005)
[jira] [Updated] (IGNITE-7438) LSQR: Sparse Equations and Least Squares for Lin Regression
[ 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|https://issues.apache.org/jira/browse/IGNITE-7437]. 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. * 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. > 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|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|https://issues.apache.org/jira/browse/IGNITE-7437]. > > 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)
[jira] [Updated] (IGNITE-7438) LSQR: Sparse Equations and Least Squares for Lin Regression
[ https://issues.apache.org/jira/browse/IGNITE-7438?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Anton Dmitriev updated IGNITE-7438: --- Fix Version/s: 2.5 > 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|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)
[jira] [Updated] (IGNITE-7438) LSQR: Sparse Equations and Least Squares for Lin Regression
[ 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)
[jira] [Updated] (IGNITE-7438) LSQR: Sparse Equations and Least Squares for Lin Regression
[ 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. * 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. 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. Distribution is 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: 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. > * 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. -- This message was sent by Atlassian JIRA (v7.6.3#76005)
[jira] [Updated] (IGNITE-7438) LSQR: Sparse Equations and Least Squares for Lin Regression
[ 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. * Utilizes all CPU resources by processing different parts of data on different cores. Distribution is 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. 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|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] > 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. > * Utilizes all CPU resources by processing different parts of data on > different cores. > Distribution is 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. -- This message was sent by Atlassian JIRA (v7.6.3#76005)
[jira] [Updated] (IGNITE-7438) LSQR: Sparse Equations and Least Squares for Lin Regression
[ 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)
[jira] [Updated] (IGNITE-7438) LSQR: Sparse Equations and Least Squares for Lin Regression
[ 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|[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] -- This message was sent by Atlassian JIRA (v7.6.3#76005)
[jira] [Updated] (IGNITE-7438) LSQR: Sparse Equations and Least Squares for Lin Regression
[ 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|[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 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. LSQR iterative solver is implemented using SciPy reference implementation. > 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]|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 -- This message was sent by Atlassian JIRA (v7.6.3#76005)
[jira] [Updated] (IGNITE-7438) LSQR: Sparse Equations and Least Squares for Lin Regression
[ 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. LSQR iterative solver is implemented using SciPy reference implementation. was: Implementation of the LSQR trainer for linear regression. > 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. > LSQR iterative solver is implemented using SciPy reference implementation. -- This message was sent by Atlassian JIRA (v7.6.3#76005)
[jira] [Updated] (IGNITE-7438) LSQR: Sparse Equations and Least Squares for Lin Regression
[ https://issues.apache.org/jira/browse/IGNITE-7438?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Anton Dmitriev updated IGNITE-7438: --- Description: Implementation of the LSQR trainer for linear regression. was: We to implemet LSQR trainer for lin regresstion. > 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 > > Implementation of the LSQR trainer for linear regression. > -- This message was sent by Atlassian JIRA (v7.6.3#76005)
[jira] [Updated] (IGNITE-7438) LSQR: Sparse Equations and Least Squares for Lin Regression
[ https://issues.apache.org/jira/browse/IGNITE-7438?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Anton Dmitriev updated IGNITE-7438: --- Attachment: bidiagonalization.png > 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 > > We to implemet LSQR trainer for lin regresstion. -- This message was sent by Atlassian JIRA (v7.6.3#76005)
[jira] [Updated] (IGNITE-7438) LSQR: Sparse Equations and Least Squares for Lin Regression
[ https://issues.apache.org/jira/browse/IGNITE-7438?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Anton Dmitriev updated IGNITE-7438: --- Description: We to implemet LSQR trainer for lin regresstion. was:We to implemet LSQR trainer for lin regresstion. > 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 > > We to implemet LSQR trainer for lin regresstion. > -- This message was sent by Atlassian JIRA (v7.6.3#76005)
[jira] [Updated] (IGNITE-7438) LSQR: Sparse Equations and Least Squares for Lin Regression
[ https://issues.apache.org/jira/browse/IGNITE-7438?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Anton Dmitriev updated IGNITE-7438: --- Attachment: (was: bidiagonalization.png) > 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 > > We to implemet LSQR trainer for lin regresstion. > -- This message was sent by Atlassian JIRA (v7.6.3#76005)
