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ABSTRACT: We consider the problem of finding the optimal coefficient vector that maximizes the computation rate at a relay in the compute-and-forward scheme.
Also to find out what the optimal size of block is in column block strip cyclic layout, the decomposition of execution time was performed.
Cholesky-factorization is a well know method for solving a linear system of equations.
In this paper, various parallel algorithms for Cholesky-factorization using MPI are designed, analyzed, and implemented.
We propose a new class of hyperbolic Gram-Schmidt methods to simultaneously update and downdate the Cholesky factor of a sample covariance matrix efficiently with applications to sliding window recursive least squares (RLS) filtering problems.
Several vectorized versions of this Gram-Schmidt approach are introduced, which include conventional column-updating, modified row/column- updating, and square-root-free methods.