Hi Mike, Apart from the operations I was proposing, the idea of solving polynomials via QR reduction seems a great idea to me. I can add this module in the list of operations to be added to Boost.ublas in my proposal. I am looking for someone to mentor me on this project. Regards, Shikhar Srivastava On Sat, Jan 20, 2018 at 10:34 AM, SHIKHAR SRIVASTAVA <shikharsri1996@gmail.com <mailto:shikharsri1996@gmail.com>> wrote: Hi Mike, It looks like some of the operations that I suggested were already implemented as a part of GSOC15, though never got merged into the main branch. https://www.google-melange.com/archive/gsoc/2015/orgs/boostcpp/projects/raja... <https://www.google-melange.com/archive/gsoc/2015/orgs/boostcpp/projects/rajaditya.html> https://github.com/BoostGSoC15/ublas/ <https://github.com/BoostGSoC15/ublas/> My Idea was to add generalised modules for the operations (Now) - 1. LU and Cholesky 2. QR and QL 3. SVD On Fri, Jan 19, 2018 at 9:14 PM, Mike Gresens <mike.gresens@googlemail.com <mailto:mike.gresens@googlemail.com>> wrote: Hi Shikhar, does this include something for solving polynomials (via balanced-QR reduction of the companion matrix)? See https://www.gnu.org/software/gsl/doc/html/poly.html#general-polynomial-equat... <https://www.gnu.org/software/gsl/doc/html/poly.html#general-polynomial-equations> Thanks! Best regards, Mike... Am 19.01.2018 um 07:37 schrieb SHIKHAR SRIVASTAVA via Boost: Hi everyone, I am a 4th year undergraduate student pursuing a degree in Computer Science and Engineering. I have strong programming experience in C++ through internships, self projects and programming events. I wish to be a part of gsoc18 under boost and am particularly interested in the linear algebra library Boost.ublas. The ublas library can be made more useful for Machine Learning applications like recommendation systems, clustering and classification, pattern recognition by adding some operations required in those. I propose to add advanced matrix operations to ublas including -     1. Triangular Factorisation (LU and Cholesky)     2. Orthogonal Factorisation (QR and QL)     3. Operations to find Singular Value lists     4. Eigenvalue algorithms     5. Singular Value Decomposition (SVD)     6. Jordan Decomposition     7. Schur Decomposition     8. Hessenberg Decomposition This is a very brief description of what I would like to propose. Any suggestions that help in refining the requirements before making a draft proposal  would be great. Regards Shikhar Srivastava Github: https://github.com/shikharsrivastava <https://github.com/shikharsrivastava> _______________________________________________ Unsubscribe & other changes: http://lists.boost.org/mailman/listinfo.cgi/boost <http://lists.boost.org/mailman/listinfo.cgi/boost>