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From Debasish Das <debasish.da...@gmail.com>
Subject Re: Constraint Solver for Spark
Date Fri, 06 Jun 2014 17:42:11 GMT
Hi Xiangrui,

It's not the linear constraint, It is quadratic inequality with slack,
first order taylor approximation of off diagonal cross terms and a cyclic
coordinate descent, which we think will yield orthogonality....It's still
under works...

Also we want to put a L1 constraint as set of linear equations when solving
for ALS...

I will create the JIRA...as I see it, this will evolve to a generic
constraint solver for machine learning problems that has a QP
structure....ALS is one example....another example is kernel SVMs...

I did not know that lgpl solver can be added to the classpath....if it can
be then definitely we should add these in ALS.scala...

Thanks.
Deb



On Thu, Jun 5, 2014 at 11:31 PM, Xiangrui Meng <mengxr@gmail.com> wrote:

> I don't quite understand why putting linear constraints can promote
> orthogonality. For the interfaces, if the subproblem is determined by
> Y^T Y and Y^T b for each iteration, then the least squares solver, the
> non-negative least squares solver, or your convex solver is simply a
> function
>
> (A, b) -> x.
>
> You can define it as an interface, and make the solver pluggable by
> adding a setter to ALS. If you want to use your lgpl solver, just
> include it in the classpath. Creating two separate files still seems
> unnecessary to me. Could you create a JIRA and we can move our
> discussion there? Thanks!
>
> Best,
> Xiangrui
>
> On Thu, Jun 5, 2014 at 7:20 PM, Debasish Das <debasish.das83@gmail.com>
> wrote:
> > Hi Xiangrui,
> >
> > For orthogonality properties in the factors we need a constraint solver
> > other than the usuals (l1, upper and lower bounds, l2 etc)
> >
> > The interface of constraint solver is standard and I can add it in mllib
> > optimization....
> >
> > But I am not sure how will I call the gpl licensed ipm solver from
> > mllib....assume the solver interface is as follows:
> >
> > Qpsolver (densematrix h, array [double] f, int linearEquality, int
> > linearInequality, bool lb, bool ub)
> >
> > And then I have functions to update equalities, inequalities, bounds etc
> > followed by the run which generates the solution....
> >
> > For l1 constraints I have to use epigraph formulation which needs a
> > variable transformation before the solve....
> >
> > I was thinking that for the problems that does not need constraints
> people
> > will use ALS.scala and ConstrainedALS.scala will have the constrained
> > formulations....
> >
> > I can point you to the code once it is ready and then you can guide me
> how
> > to refactor it to mllib als ?
> >
> > Thanks.
> > Deb
> > 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.das83@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
>

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