Conjugate gradient methods are among the most efficient for solving optimization models. In this paper, a newly proposed conjugate method is problems as convex combination of Harger-Zhan and Dai-Yaun nonlinear methods, which capable producing sufficient descent condition with global convergence properties under strong Wolfe conditions. The numerical results demonstrate efficiency some benchmark...

Power spectrum estimation and evaluation of associated errors in the presence of incomplete sky coverage; non-homogeneous, correlated instrumental noise; and foreground emission is a problem of central importance for the extraction of cos-mological information from the cosmic microwave background. We develop a Monte Carlo approach for the maximum likelihood estimatation of the power spectrum. T...

In this paper, performance of three classifiers for classification of five mental tasks were investigated. Wavelet Packet Transform (WPT) was used for feature extraction of the relevant frequency bands from raw Electroencephalograph (EEG) signal. The three classifiers namely used were Multilayer Back propagation Neural Network, Support Vector Machine and Radial Basis Function Neural Network. In...

Conjugate gradient methods are an important class of methods for unconstrained optimization, especially for large-scale problems. Recently, they have been much studied. This paper proposes a three-parameter family of hybrid conjugate gradient methods. Two important features of the family are that (i) it can avoid the propensity of small steps, namely, if a small step is generated away from the ...

In this paper, by minimizing the distance between CG direction and of improved Perry conjugate gradient method [Yao et al., Numer. Algorithms 78 (2018) 1255–1269], we propose a descent modified HS method. A remarkable property is that it can produce sufficient property, which independent line search used. Under suitable conditions, prove with standard Armijo globally convergent for uniformly co...

Based on an eigenvalue analysis, a new proof for the sufficient descent property of the modified Polak-Ribière-Polyak conjugate gradient method proposed by Yu et al. is presented.

Low-rank matrix completion aims to recover an unknown from a subset of observed entries. In this paper, we solve the problem via optimization manifold. Specially, apply QR factorization retraction during optimization. We devise two fast algorithms based on steepest gradient descent and conjugate descent, demonstrate their superiority over promising baseline with ratio at least 24%.