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https://issues.apache.org/jira/browse/SPARK-2426?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14191551#comment-14191551
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Debasish Das commented on SPARK-2426:
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[~mengxr] The matlab comparison scripts are open sourced over here:
https://github.com/debasish83/ecos/blob/master/matlab/admm/qprandom.m
https://github.com/debasish83/ecos/blob/master/matlab/pdco4/code/pdcotestQP.m
The detailed comparisons are on the REAME.md. Please look at the section on
Matlab comparisons.
In a nutshell, for bounds MOSEK and ADMM are similar, for elastic net Proximal
is 10X faster compared to MOSEK, for equality MOSEK is 2-3X faster than
Proximal but both PDCO and ECOS produces much worse result as compared to ADMM.
Accelerated ADMM also did not work as good as default ADMM. Increasing the
over-relaxation parameter helped accelerated ADMM but I have not explored it
yet.
ADMM and PDCO are in Matlab but ECOS and MOSEK are both using mex files so they
are expected to be more efficient.
Next I will add the performance results of running positivity, box, sparse
coding / regularized lsi and robust-plsa on MovieLens dataset and validate
product recommendation using the MAP measure...In terms of RMSE, default <
positive < sparse coding...
What's the largest datasets LDA PRs are running? I would like to try that on
sparse coding as well...From these papers sparse coding/RLSI should give
results at par with LDA:
https://www.cs.cmu.edu/~xichen/images/SLSA-sdm11-final.pdf
http://web.stanford.edu/group/mmds/slides2012/s-hli.pdf
The same randomized matrices can be generated and run in the PR as follows:
./bin/spark-class org.apache.spark.mllib.optimization.QuadraticMinimizer 1000 1
1.0 0.99
rank=1000, equality=1.0 lambda=1.0 beta=0.99
L1regularization = lambda*beta L2regularization = lambda*(1-beta)
Generating randomized QPs with rank 1000 equalities 1
sparseQp 88.423 ms iterations 45 converged true
posQp 181.369 ms iterations 121 converged true
boundsQp 175.733 ms iterations 121 converged true
Qp Equality 2805.564 ms iterations 2230 converged true
> Quadratic Minimization for MLlib ALS
> ------------------------------------
>
> Key: SPARK-2426
> URL: https://issues.apache.org/jira/browse/SPARK-2426
> 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.
> http://spark-summit.org/2014/talk/quadratic-programing-solver-for-non-negative-matrix-factorization-with-spark
> 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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