No. This algorithm can be one of algorithms to work with, but complexity is also affected by matrix structure ability to access random element and produce non-zero elements on an iteration. more detailed discussion is currently going on PR 44 https://github.com/apache/mahout/pull/44
On Thu, Sep 11, 2014 at 12:24 AM, Ankit Sharma <[email protected]> wrote: > Hi Dmitriy, > > Did you mean something like implementing *"Strassen algorithm"* for matrix > multiplication? > > thanks & best regards, > > Ankit > > On Wed, Sep 10, 2014 at 10:59 PM, Dmitriy Lyubimov <[email protected]> > wrote: > > > The biggest problem today (in my opinion) is mahout-math. > > > > (1) cost/type based optimization of matrix-matrix multiplication > > (2) cost/type based optimization of elementwise matrix-matrix operations > > > > There is already some work done there, especially in the realm of > > vector-vector opreations, so matrix-matrix operations that work with > > matrices backed by a set of vectors, should naturally benefit from that. > > > > Other two noble goals have been: > > > > (3) jBLAS backed matrices, including a part of (1) and (2) > > (4) JCuda backed matrices, including as a part of (1) and (2) > > > > Otherwise, if you are interested in writing yet-another quasi-algebraic > > solver methodology, it is a second priority but would be welcome provided > > you provide references to principled approach and its adaptation to > scaled > > operations strategy, for review, and as long as long as preferrably this > > method is not yet part of MLib in spark. > > > > -d > > > > > > > > On Wed, Sep 10, 2014 at 10:14 AM, Ankit Sharma < > > [email protected]> > > wrote: > > > > > Hello, > > > > > > I have been an user of Mahout for quite sometime now and got really > > exited > > > when I heard mahout is moving to Spark. Today I played around with > Linear > > > Regression example and browsed some of the spark Machine Learning(ML) > > code. > > > It was really interesting to see how intuitive the entire process is. > > > > > > I have background in data science model building and I would like to > > > contribute in the development process. So, I would like to get some > > advice > > > on what has already been completed on ML side and from where I can > start? > > > > > > I have couple of ideas like I can start with either some classification > > > algorithm like SVM or build(enhance) some simple building blocks. You > can > > > throw in your suggestions and I'll be see which one falls into my > domain, > > > and try to work on them. > > > > > > thanks & best regards, > > > > > > Ankit Sharma > > > Data Science Professional > > > _______________________ > > > Mobile: +91-9632383141 > > > Email: [email protected] > > > Skype: aksharma11588 > > > LinkedIn <http://in.linkedin.com/in/aks11588/> | Digg Data > > > <http://www.diggdata.in/> | about.me <http://about.me/ankitksharma> > > > > > >
