This task consists of two parts:
- Implementation of the of systems of linear equations.
- Implementation of the .
Apache Ignite LSQR iterative solver is based on SciPy reference implementation, but it's distributed and can:
- Efficiently work in cases when a data is distributed across a cluster.
- Utilize all CPU resources by processing different parts of data on different cores.
These advantages are achieved as result of changing Golub-Kahan-Lanczos Bidiagonalization Procedure procedure which is a core of LSQR algorithm and utilizing features of Partition Based Dataset implementation.
LSQR-based linear regression trainer is a trainer that uses the LSQR solver to solve a system of linear equations which represents a linear regression problem.