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
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/raja...
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
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-equat...
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
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