> > Matrix factorization is a non-convex problem and ALS solves it using 2 > convex problems, DSGD solves the problem by finding a local minima. > > ALS and SGD solve the same non-convex objective function, and thus both yield local minima. The following reference provides a nice overview (in particular see equation 2 of this paper):
http://www2.research.att.com/~volinsky/papers/ieeecomputer.pdf > > > > On Thu, Jan 2, 2014 at 4:06 PM, Ameet Talwalkar > <am...@eecs.berkeley.edu>wrote: > >> Hi Deb, >> >> Thanks for your email. We currently do not have a DSGD implementation in >> MLlib. Also, just to clarify, DSGD is not a variant of ALS, but rather a >> different algorithm for solving the same the same bi-convex objective >> function. >> >> It would be a good thing to do add, but to the best of my knowledge, no >> one is actively working on this right now. >> >> Also, as you mentioned, the ALS implementation in mllib is more >> robust/scalable than the one in spark.examples. >> >> -Ameet >> >> >> On Thu, Jan 2, 2014 at 3:16 PM, Debasish Das <debasish.da...@gmail.com>wrote: >> >>> Hi, >>> >>> I am not noticing any DSGD implementation of ALS in Spark. >>> >>> There are two ALS implementations. >>> >>> org.apache.spark.examples.SparkALS does not run on large matrices and >>> seems more like a demo code. >>> >>> org.apache.spark.mllib.recommendation.ALS looks feels more robust >>> version and I am experimenting with it. >>> >>> References here are Jellyfish, Twitter's implementation of Jellyfish >>> called Scalafish, Google paper called Sparkler and similar idea put forward >>> by IBM paper by Gemulla et al. (large-scale matrix factorization with >>> distributed stochastic gradient descent) >>> >>> https://github.com/azymnis/scalafish >>> >>> Are there any plans of adding DSGD in Spark or there are any existing >>> JIRA ? >>> >>> Thanks. >>> Deb >>> >>> >> >
