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From "Valeriy Avanesov (JIRA)" <>
Subject [jira] [Commented] (SPARK-2426) Quadratic Minimization for MLlib ALS
Date Fri, 12 Dec 2014 00:00:22 GMT


Valeriy Avanesov commented on SPARK-2426:

> what's the normalization constraint ? Each row of W should sum upto 1 and each column
of H should sum upto 1 with positivity ? 

> That is similar to PLSA right except that PLSA will have a bi-concave loss...
There's a completely different loss... BTW, we've used a factorisation with the loss you've
described as an initial approximation for PLSA. It gave a significant speed-up. 

> Quadratic Minimization for MLlib ALS
> ------------------------------------
>                 Key: SPARK-2426
>                 URL:
>             Project: Spark
>          Issue Type: New Feature
>          Components: MLlib
>    Affects Versions: 1.3.0
>            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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