We propose a novel variant of conjugate gradient based on the Reproducing Kernel Hilbert Space (RKHS) inner product. An analysis of the algorithm suggests it enjoys better performance properties than standard iterative methods when applied to learning kernel machines. Experimental results for both classification and regression bear out the theoretical implications. We further address the domina...

The Conjugate Gradient Squared (CGS) is a well-known and widely used iterative method for solving non-symmetric linear systems of equations. In practice the method converges fast, often twice as fast as the Bi-Conjugate Gradient (Bi-CG) method. However, during the iteration large residual norms may appear, which may lead to inaccurate approximate solutions or may even deteriorate the convergenc...

Bundle adjustment for multi-view reconstruction is traditionally done using the Levenberg-Marquardt algorithm with a direct linear solver, which is computationally very expensive. An alternative to this approach is to apply the conjugate gradients algorithm in the inner loop. This is appealing since the main computational step of the CG algorithm involves only a simple matrix-vector multiplicat...

The conjugate gradient method is a powerful algorithm to solve well-structured sparse linear systems that arise from partial diierential equations. We consider here three diierent conjugate gradient schemes for solving elliptic partial diierential equations that arise from 5-point diierence schemes: the classical CG, CG with a block diagonal-block incomplete Cholesky preconditioner and the redu...

In this paper, we present new variants of global bi-conjugate gradient (Gl-BiCG) and global bi-conjugate residual (Gl-BiCR) methods for solving nonsymmetric linear systems with multiple right-hand sides. These methods are based on global oblique projections of the initial residual onto a matrix Krylov subspace. It is shown that these new algorithms converge faster and more smoothly than the Gl-...

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,...