Hi Deb, Why do you want to make those methods public? If you only need to replace the solver for subproblems. You can try to make the solver pluggable. Now it supports least squares and non-negative least squares. You can define an interface for the subproblem solvers and maintain the IPM solver at your own code base, if the only information you need is Y^T Y and Y^T b.
Btw, just curious, what is the use case for quadratic constraints? Best, Xiangrui On Thu, Jun 5, 2014 at 3:38 PM, Debasish Das <debasish.da...@gmail.com> wrote: > Hi, > > We are adding a constrained ALS solver in Spark to solve matrix > factorization use-cases which needs additional constraints (bounds, > equality, inequality, quadratic constraints) > > We are using a native version of a primal dual SOCP solver due to its small > memory footprint and sparse ccs matrix computation it uses...The solver > depends on AMD and LDL packages from Timothy Davis for sparse ccs matrix > algebra (released under lgpl)... > > Due to GPL dependencies, it won't be possible to release the code as Apache > license for now...If we get good results on our use-cases, we will plan to > write a version in breeze/modify joptimizer for sparse ccs operations... > > I derived ConstrainedALS from Spark mllib ALS and I am comparing the > performance with default ALS and non-negative ALS as baseline. Plan is to > release the code as GPL license for community review...I have kept the > package structure as org.apache.spark.mllib.recommendation > > There are some private functions defined in ALS, which I would like to > reuse....Is it possible to take the private out from the following > functions: > > 1. makeLinkRDDs > 2. makeInLinkBlock > 3. makeOutLinkBlock > 4. randomFactor > 5. unblockFactors > > I don't want to copy any code.... I can ask for a PR to make these > changes... > > Thanks. > Deb
