You can try LinearRegression with sparse input. It converges the least
squares solution if the linear system is over-determined, while the
convergence rate depends on the condition number. Applying standard
scaling is popular heuristic to reduce the condition number.
If you are interested in
Durin,
I have integrated ecos with spark which uses suitesparse under the hood for
linear equation solvesI have exposed only the qp solver api in spark
since I was comparing ip with proximal algorithms but we can expose
suitesparse api as well...jni is used to load up ldl amd and ecos
Xiangrui,
Should I open up a JIRA for this ?
Distributed lp/socp solver through ecos/ldl/amd ?
I can open source it with gpl license in spark code as that's what our
legal cleared (apache + gpl becomes gpl) and figure out the right way to
call it...ecos is gpl but we can definitely use the jni
I asked Tim whether he would change the license of SuiteSparse to an
Apache-friendly license couple months ago, but the answer was no. So I
don't think we can use SuiteSparse in MLlib through JNI. Please feel
free to create JIRAs for distributed linear programming and SOCP
solvers and run the
Yup...this can be a spark community project...I saw a PR for
that...interested users fine with lgpl/gpl code can make use of it...
On Mon, Sep 8, 2014 at 12:37 PM, Xiangrui Meng men...@gmail.com wrote:
I asked Tim whether he would change the license of SuiteSparse to an
Apache-friendly license
such an
algorithm for Spark?
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