[jira] [Updated] (SPARK-8514) LU factorization on BlockMatrix
[ https://issues.apache.org/jira/browse/SPARK-8514?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Hyukjin Kwon updated SPARK-8514: Labels: advanced bulk-closed (was: advanced) > LU factorization on BlockMatrix > --- > > Key: SPARK-8514 > URL: https://issues.apache.org/jira/browse/SPARK-8514 > Project: Spark > Issue Type: New Feature > Components: MLlib >Reporter: Xiangrui Meng >Priority: Critical > Labels: advanced, bulk-closed > Attachments: BlockMatrixSolver.pdf, BlockPartitionMethods.py, > BlockPartitionMethods.scala, LUBlockDecompositionBasic.pdf, Matrix > Factorization - M...ark 1.5.0 Documentation.pdf, testImplementation.scala, > testScript.scala > > > LU is the most common method to solve a general linear system or inverse a > general matrix. A distributed version could in implemented block-wise with > pipelining. A reference implementation is provided in ScaLAPACK: > http://netlib.org/scalapack/slug/node178.html -- This message was sent by Atlassian JIRA (v7.6.3#76005) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Updated] (SPARK-8514) LU factorization on BlockMatrix
[ https://issues.apache.org/jira/browse/SPARK-8514?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Joseph K. Bradley updated SPARK-8514: - Target Version/s: (was: 2.0.0) > LU factorization on BlockMatrix > --- > > Key: SPARK-8514 > URL: https://issues.apache.org/jira/browse/SPARK-8514 > Project: Spark > Issue Type: New Feature > Components: MLlib >Reporter: Xiangrui Meng >Priority: Critical > Labels: advanced > Attachments: BlockMatrixSolver.pdf, BlockPartitionMethods.py, > BlockPartitionMethods.scala, LUBlockDecompositionBasic.pdf, Matrix > Factorization - M...ark 1.5.0 Documentation.pdf, testImplementation.scala, > testScript.scala > > > LU is the most common method to solve a general linear system or inverse a > general matrix. A distributed version could in implemented block-wise with > pipelining. A reference implementation is provided in ScaLAPACK: > http://netlib.org/scalapack/slug/node178.html -- This message was sent by Atlassian JIRA (v6.3.4#6332) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Updated] (SPARK-8514) LU factorization on BlockMatrix
[ https://issues.apache.org/jira/browse/SPARK-8514?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Xiangrui Meng updated SPARK-8514: - Target Version/s: 2.0.0 (was: ) > LU factorization on BlockMatrix > --- > > Key: SPARK-8514 > URL: https://issues.apache.org/jira/browse/SPARK-8514 > Project: Spark > Issue Type: New Feature > Components: MLlib >Reporter: Xiangrui Meng >Priority: Critical > Labels: advanced > Attachments: BlockMatrixSolver.pdf, BlockPartitionMethods.py, > BlockPartitionMethods.scala, LUBlockDecompositionBasic.pdf, Matrix > Factorization - M...ark 1.5.0 Documentation.pdf, testImplementation.scala, > testScript.scala > > > LU is the most common method to solve a general linear system or inverse a > general matrix. A distributed version could in implemented block-wise with > pipelining. A reference implementation is provided in ScaLAPACK: > http://netlib.org/scalapack/slug/node178.html -- This message was sent by Atlassian JIRA (v6.3.4#6332) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Updated] (SPARK-8514) LU factorization on BlockMatrix
[ https://issues.apache.org/jira/browse/SPARK-8514?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Xiangrui Meng updated SPARK-8514: - Priority: Critical (was: Major) > LU factorization on BlockMatrix > --- > > Key: SPARK-8514 > URL: https://issues.apache.org/jira/browse/SPARK-8514 > Project: Spark > Issue Type: New Feature > Components: MLlib >Reporter: Xiangrui Meng >Priority: Critical > Labels: advanced > Attachments: BlockMatrixSolver.pdf, BlockPartitionMethods.py, > BlockPartitionMethods.scala, LUBlockDecompositionBasic.pdf, Matrix > Factorization - M...ark 1.5.0 Documentation.pdf, testImplementation.scala, > testScript.scala > > > LU is the most common method to solve a general linear system or inverse a > general matrix. A distributed version could in implemented block-wise with > pipelining. A reference implementation is provided in ScaLAPACK: > http://netlib.org/scalapack/slug/node178.html -- This message was sent by Atlassian JIRA (v6.3.4#6332) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Updated] (SPARK-8514) LU factorization on BlockMatrix
[ https://issues.apache.org/jira/browse/SPARK-8514?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Xiangrui Meng updated SPARK-8514: - Target Version/s: 1.7.0 (was: 1.6.0) > LU factorization on BlockMatrix > --- > > Key: SPARK-8514 > URL: https://issues.apache.org/jira/browse/SPARK-8514 > Project: Spark > Issue Type: New Feature > Components: MLlib >Reporter: Xiangrui Meng > Labels: advanced > Attachments: BlockMatrixSolver.pdf, BlockPartitionMethods.py, > BlockPartitionMethods.scala, LUBlockDecompositionBasic.pdf, Matrix > Factorization - M...ark 1.5.0 Documentation.pdf, testImplementation.scala, > testScript.scala > > > LU is the most common method to solve a general linear system or inverse a > general matrix. A distributed version could in implemented block-wise with > pipelining. A reference implementation is provided in ScaLAPACK: > http://netlib.org/scalapack/slug/node178.html -- This message was sent by Atlassian JIRA (v6.3.4#6332) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Updated] (SPARK-8514) LU factorization on BlockMatrix
[ https://issues.apache.org/jira/browse/SPARK-8514?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Jerome updated SPARK-8514: -- Attachment: testImplementation.scala another test script with a more efficient random matrix generation routine. This will likely be used for more timings. > LU factorization on BlockMatrix > --- > > Key: SPARK-8514 > URL: https://issues.apache.org/jira/browse/SPARK-8514 > Project: Spark > Issue Type: New Feature > Components: MLlib >Reporter: Xiangrui Meng > Labels: advanced > Attachments: BlockMatrixSolver.pdf, BlockPartitionMethods.py, > BlockPartitionMethods.scala, LUBlockDecompositionBasic.pdf, Matrix > Factorization - M...ark 1.5.0 Documentation.pdf, testImplementation.scala, > testScript.scala > > > LU is the most common method to solve a general linear system or inverse a > general matrix. A distributed version could in implemented block-wise with > pipelining. A reference implementation is provided in ScaLAPACK: > http://netlib.org/scalapack/slug/node178.html -- This message was sent by Atlassian JIRA (v6.3.4#6332) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Updated] (SPARK-8514) LU factorization on BlockMatrix
[ https://issues.apache.org/jira/browse/SPARK-8514?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Jerome updated SPARK-8514: -- Attachment: Matrix Factorization - M...ark 1.5.0 Documentation.pdf I added a version of the Documentation that contains some of the design documentation for the LU algorithm. Some of the descriptions may not be necessary for Spark users, but could be useful for reviewers. Cheers, Jerome > LU factorization on BlockMatrix > --- > > Key: SPARK-8514 > URL: https://issues.apache.org/jira/browse/SPARK-8514 > Project: Spark > Issue Type: New Feature > Components: MLlib >Reporter: Xiangrui Meng > Labels: advanced > Attachments: BlockMatrixSolver.pdf, BlockPartitionMethods.py, > BlockPartitionMethods.scala, LUBlockDecompositionBasic.pdf, Matrix > Factorization - M...ark 1.5.0 Documentation.pdf, testScript.scala > > > LU is the most common method to solve a general linear system or inverse a > general matrix. A distributed version could in implemented block-wise with > pipelining. A reference implementation is provided in ScaLAPACK: > http://netlib.org/scalapack/slug/node178.html -- This message was sent by Atlassian JIRA (v6.3.4#6332) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Updated] (SPARK-8514) LU factorization on BlockMatrix
[ https://issues.apache.org/jira/browse/SPARK-8514?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Jerome updated SPARK-8514: -- Attachment: BlockPartitionMethods.py BlockPartitionMethods.scala testScript.scala LUBlockDecompositionBasic.pdf BlockMatrixSolver.pdf To run LUDecomposition example in spark-shell... :load BlockPartitionMethods.scala :load testScript.scala It will perform a simple LU Decomposition on a 6x6 Matrix with 2x2 blocks and report residual = LU-PA. LU factorization on BlockMatrix --- Key: SPARK-8514 URL: https://issues.apache.org/jira/browse/SPARK-8514 Project: Spark Issue Type: New Feature Components: MLlib Reporter: Xiangrui Meng Labels: advanced Attachments: BlockMatrixSolver.pdf, BlockPartitionMethods.py, BlockPartitionMethods.scala, LUBlockDecompositionBasic.pdf, testScript.scala LU is the most common method to solve a general linear system or inverse a general matrix. A distributed version could in implemented block-wise with pipelining. A reference implementation is provided in ScaLAPACK: http://netlib.org/scalapack/slug/node178.html -- This message was sent by Atlassian JIRA (v6.3.4#6332) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Updated] (SPARK-8514) LU factorization on BlockMatrix
[ https://issues.apache.org/jira/browse/SPARK-8514?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Joseph K. Bradley updated SPARK-8514: - Target Version/s: 1.6.0 (was: 1.5.0) LU factorization on BlockMatrix --- Key: SPARK-8514 URL: https://issues.apache.org/jira/browse/SPARK-8514 Project: Spark Issue Type: New Feature Components: MLlib Reporter: Xiangrui Meng Labels: advanced LU is the most common method to solve a general linear system or inverse a general matrix. A distributed version could in implemented block-wise with pipelining. A reference implementation is provided in ScaLAPACK: http://netlib.org/scalapack/slug/node178.html -- This message was sent by Atlassian JIRA (v6.3.4#6332) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Updated] (SPARK-8514) LU factorization on BlockMatrix
[ https://issues.apache.org/jira/browse/SPARK-8514?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Xiangrui Meng updated SPARK-8514: - Labels: advanced (was: ) LU factorization on BlockMatrix --- Key: SPARK-8514 URL: https://issues.apache.org/jira/browse/SPARK-8514 Project: Spark Issue Type: New Feature Components: MLlib Reporter: Xiangrui Meng Labels: advanced LU is the most common method to solve a general linear system or inverse a general matrix. A distributed version could in implemented block-wise with pipelining. A reference implementation is provided in ScaLAPACK: http://netlib.org/scalapack/slug/node178.html -- This message was sent by Atlassian JIRA (v6.3.4#6332) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
