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