A New Algorithm for Fuzzy Linear Regression with Crisp Inputs and Fuzzy Output

Abstract: In this work, the parameters of fuzzy linear regression based on the least squares approach is computed by ST-decomposition method. This method is not an iterative technique, however, it is a powerful method for nonsingular coefficient matrices. Numerical examples are at the end of this paper to illustrate the performance of the new method

GMRES implementations and residual smoothing techniques for solving ill-posed linear systems

AbstractThere are verities of useful Krylov subspace methods to solve nonsymmetric linear system of equations. GMRES is one of the best Krylov solvers with several different variants to solve large sparse linear systems. Any GMRES implementation has some advantages. As the solution of ill-posed problems are important. In this paper, some GMRES variants are discussed and applied to solve these kinds of problems. Residual smoothing techniques are efficient ways to accelerate the convergence speed of some iterative methods like CG variants. At the end of this paper, some residual smoothing techniques are applied for different GMRES methods to test the influence of these techniques on GMRES implementations