Hey,
We're actually working on similar ideas in the AMPlab with spark - for example
we've got some image classification pipelines built on this idea -
http://www.eecs.berkeley.edu/~brecht/papers/07.rah.rec.nips.pdf
Approximating kernel methods via random projections hit with nonlinearity.
Add
Thanks Tom for the pointers...
I have a IPM running on the JVM which uses SOCP formulation for the
quadratic program I wrote above
We are going to show the details of it at the SummitIPM runtimes and
accuracy give a baseline for the problem that we are solving...
Now we are trying to see how
What is your general solver? IPM or simplex or something else? I have
seen a lot of attempts to apply iterative solvers for the subproblems on
those without much luck because the conditioning of the linear systems gets
worse and worse near the optimum. IPOPT (interior point method) has an
LBFGS
Hi,
I am coming up with an iterative solver for Equality and bound constrained
quadratic minimization...
I have the cholesky versions running but cholesky does not scale for large
dimensions but works fine for matrix factorization use-cases where ranks
are low..
Minimize 0.5x'Px + q'x
s.t Aeq x
What flavor of SVM are you trying to support? LSSVM doesn't need a bound
constraint, but most other formulations do. There have been ideas for
bound-constrained CG, though bounded LBFGS is more common. I think code
for Nystrom approximations or kernel mappings would be more useful.
On Fri, Jun
Thanks David...Let me try it...I am keen to see the results first and later
will look into runtime optimizations...
Deb
On Fri, Jun 27, 2014 at 3:12 PM, David Hall wrote:
> I have no ideas on benchmarks, but breeze has a CG solver:
>
> https://github.com/scalanlp/breeze/tree/master/math/src
I have no ideas on benchmarks, but breeze has a CG solver:
https://github.com/scalanlp/breeze/tree/master/math/src/main/scala/breeze/optimize/linear/ConjugateGradient.scala
https://github.com/scalanlp/breeze/blob/e2adad3b885736baf890b306806a56abc77a3ed3/math/src/test/scala/breeze/optimize/linear/C
Hi,
I am looking for an efficient linear CG to be put inside the Quadratic
Minimization algorithms we added for Spark mllib.
With a good linear CG, we should be able to solve kernel SVMs with this
solver in mllib...
I use direct solves right now using cholesky decomposition which has higher
comp