The least mean squares (LMS) method for linear least squares problems differs from the steepest descent method in that it processes data blocks one-by-one, with intermediate adjustment of the parameter vector under optimization. This mode of operation often leads to faster convergence when far from the eventual limit and to slower (sublinear) convergence when close to the optimal solution. We e...

The LMS method for linear least squares problems differs from the steepest descent method in that it processes data blocks one-by-one, with intermediate adjustment of the parameter vector under optimization. This mode of operation often leads to faster convergence when far from the eventual limit, and to slower (sublinear) convergence when close to the optimal solution. We embed both LMS and st...

1Department of Mathematics, Shanghai Normal University, Shanghai; and Scientific Computing Key Laboratory of Shanghai Universities, Shanghai, China 2Department of Mathematics & Statistics, College of Science, King Fahd University of Petroleum & Minerals, Dhahran, Saudi Arabia; and Department of Mathematics, Aligarh Muslim University, Aligarh, India 3Department of Applied Mathematics, National S...

In this paper, a hybrid conjugate gradient algorithm with weighted preconditioner is proposed. The algorithm can efficiently solve the minimizing problem of general function deriving from finite element discretization of the p-Laplacian. The algorithm is efficient, and its convergence rate is meshindependent. Numerical experiments show that the hybrid conjugate gradient direction of the algorit...

the focus of studies in the field of passive walking has often been on straight walking, while less attention has been paid to the field of turning motions. in this paper, the passive motions of a finite width rimless wheel as the simplest 3d model of passive biped walkers was investigated with a focus on turning motions. for this purpose, the hybrid model of the system consisting of continuous...

A recently published idea is to use the A*-Algorithm to optimize the topology of Neural Networks. In this paper, optimization techniques are investigated that combine the A*-Algorithm with diierent parallel training algorithms, namely the backpropagation algorithm and several hybrid algorithms. The hybrid algorithms combine the backpropagation's steepest descent method with diierent sets of gen...