[ https://issues.apache.org/jira/browse/SPARK-23266?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Sean Owen updated SPARK-23266: ------------------------------ Priority: Minor (was: Critical) When do you need to invert a matrix vs just solve a linear system? the latter is typically faster and more stable. I know you give examples here but can you be more specific about applications and define kriging? > Matrix Inversion on BlockMatrix > ------------------------------- > > Key: SPARK-23266 > URL: https://issues.apache.org/jira/browse/SPARK-23266 > Project: Spark > Issue Type: New Feature > Components: MLlib > Affects Versions: 2.2.1 > Reporter: Chandan Misra > Priority: Minor > > Matrix inversion is the basic building block for many other algorithms like > regression, classification, geostatistical analysis using ordinary kriging > etc. A simple Spark BlockMatrix based efficient distributed > divide-and-conquer algorithm can be implemented using only *6* > multiplications in each recursion level of the algorithm. The reference paper > can be found in > [https://arxiv.org/abs/1801.04723] -- 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