Hi Jose, Just to note that in the databricks blog they state that they compute the top-5 singular vectors, not all singular values/vectors. Computing all is much more computational intense.
Cheers, Anastasios Am 09.08.2017 15:19 schrieb "Jose Francisco Saray Villamizar" < jsa...@gmail.com>: Hi everyone, I am trying to invert a 5000 x 5000 Dense Matrix (99% non-zeros), by using SVD with an approach simmilar to : https://stackoverflow.com/questions/29969521/how-to- compute-the-inverse-of-a-rowmatrix-in-apache-spark The time Im getting with SVD is close to 10 minutes what is very long for me. A benchmark for SVD is already given here https://databricks.com/blog/2014/07/21/distributing-the- singular-value-decomposition-with-spark.html However, it seems they are using sparse matrices, thats why they get short times. Have anyone of you try to perform a SVD on a very dense big matrix . ? Is this time normal ? Thank you. -- -- Buen dia, alegria !! José Francisco Saray Villamizar cel +33 6 13710693 <+33%206%2013%2071%2006%2093> Lyon, France
