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From "Debasish Das (JIRA)" <>
Subject [jira] [Commented] (SPARK-2426) Quadratic Minimization for MLlib ALS
Date Sun, 19 Oct 2014 17:32:33 GMT


Debasish Das commented on SPARK-2426:

[~mengxr] I thought more on it and one of the reason we choose ADMM because QuadraticMinimizer
is not designed to be a local algorithm....

If it runs on Spark Master it will take a RDD...If it runs on Spark worker, it will take a
H and c from x'Hx + c'x along with proximal operators...

I will update the API and show some POCs that how this meta algorithm will add LBFGS/Truncated
Newton as a core solver for x-solve for scalable version of matrix factorization where we
don't want to create the H matrix explicitly ever...

Truncated Newton is a better choice for the constraints we want to support...I am working
on a variant of TRON and linear CG that's in breeze for the scalable version..Those are the
building blocks I need...

I am sure some of the code will move to Breeze. Proximal will definitely move to Breeze but
QuadraticMinimizer will be refactored. It will really help if you can open up a PR on the
new ALS design you have and we can work on it...

> Quadratic Minimization for MLlib ALS
> ------------------------------------
>                 Key: SPARK-2426
>                 URL:
>             Project: Spark
>          Issue Type: New Feature
>          Components: MLlib
>    Affects Versions: 1.0.0
>            Reporter: Debasish Das
>            Assignee: Debasish Das
>   Original Estimate: 504h
>  Remaining Estimate: 504h
> Current ALS supports least squares and nonnegative least squares.
> I presented ADMM and IPM based Quadratic Minimization solvers to be used for the following
ALS problems:
> 1. ALS with bounds
> 2. ALS with L1 regularization
> 3. ALS with Equality constraint and bounds
> Initial runtime comparisons are presented at Spark Summit. 
> Based on Xiangrui's feedback I am currently comparing the ADMM based Quadratic Minimization
solvers with IPM based QpSolvers and the default ALS/NNLS. I will keep updating the runtime
comparison results.
> For integration the detailed plan is as follows:
> 1. Add QuadraticMinimizer and Proximal algorithms in mllib.optimization
> 2. Integrate QuadraticMinimizer in mllib ALS

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