Modified conjugate gradient method for diagonalizing large matrices

Global conjugate gradient method for solving large general Sylvester matrix equation

In this paper, an iterative method is proposed for solving large general Sylvester matrix equation $AXB+CXD = E$, where $A in R^{ntimes n}$ , $C in R^{ntimes n}$ , $B in R^{stimes s}$ and  $D in R^{stimes s}$ are given matrices and $X in R^{stimes s}$  is the unknown matrix. We present a global conjugate gradient (GL-CG) algo- rithm for solving linear system of equations with multiple right-han...

Globally convergent modified Perry's conjugate gradient method

Conjugate gradient methods are probably the most famous iterative methods for solving large scale optimization problems in scientific and engineering computation, characterized by the simplicity of their iteration and their low memory requirements. In this paper, we propose a new conjugate gradient method which is based on the MBFGS secant condition by modifying Perry’s method. Our proposed met...

A conjugate gradient based method for Decision Neural Network training

Decision Neural Network is a new approach for solving multi-objective decision-making problems based on artificial neural networks. Using inaccurate evaluation data, network training has improved and the number of educational data sets has decreased. The available training method is based on the gradient decent method (BP). One of its limitations is related to its convergence speed. Therefore,...

The Conjugate Gradient Method for Large Sparse Matrices on the Intel Ipsc/860 Hypercube

For large sparse unstructured matrices, the critical parts of the Conjugate Gradient method on the iPSC/860 are the inter-processor communications needed for the matrix-vector multiplication and the vector-updates. In this work several implementations are tested and discussed in search for an optimal algorithm. They di er in distribution of the matrix and the various vectors over the processorg...

Diagonalizing Matrices