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
