This Paper focused on adaptive conjugate gradient method (CGM) algorithm and the same is compared with one of the fastest adaptive algorithm named recursive least squares (RLS) algorithm. This algorithms has improved the computation complexity and better convergence. MATLAB simulation showed that conjugate gradient algorithm has better output signal resolution.

New hybrid conjugate gradient algorithms are proposed and analyzed. In these hybrid algorithms the famous parameter k β is computed as a convex combination of the Polak-Ribière-Polyak and Dai-Yuan conjugate gradient algorithms. In one hybrid algorithm the parameter in convex combination is computed in such a way that the conjugacy condition is satisfied, independent of the line search. In the o...

In this paper, we propose a new hybrid coefficient of conjugate gradient method (CG) for solving unconstrained optimization model. The is combination part the MMSIS (Malik et.al, 2020) and PRP (Polak, Ribi'ere \& Polyak, 1969) coefficients. Under exact line search, search direction satisfies sufficient descent condition based on certain assumption, establish global convergence properties. U...

A meta-analysis was performed on the immunogenicity of Haemophilus influenzae type b (Hib) conjugate vaccines after 2 (2 and 4 months) and 3 doses (2, 4, and 6 months) in Korean infants. A database search of MEDLINE, KoreaMed, and Korean Medical Database was done. The primary outcome measure was the proportion of infants with anti-polyribosylribitol phosphate (PRP) concentrations > or =1.0 micr...

According to conjugate gradient algorithm, a new consensus protocol algorithm of discrete-time multi-agent systems is presented, which can achieve finite-time consensus. Finally, a numerical example is given to illustrate our theoretical result. Keywords—Consensus protocols; Graph theory; Multi-agent systems; Conjugate gradient algorithm; Finite-time.

Based on the secant condition often satisfied by quasi-Newton methods, two new versions of the Hestenes-Stiefel (HS) nonlinear conjugate gradient method are proposed, which are descent methods even with inexact line searches. The search directions of the proposed methods have the form dk = −θk gk + β H S k dk−1, or dk = −gk + β H S k dk−1 + θk yk−1. When exact line searches are used, the propos...

It is shown that the four vector extrapolation methods, minimal polynomial extrapolation, reduced rank extrapolation, modified minimal polynomial extrapolation, and topological epsilon algorithm, when applied to linearly generated vector sequences, are Krylov subspace methods, and are equivalent to some well known conjugate gradient type methods. A unified recursive method that includes the con...

The conjugate gradient optimization algorithm usually used for nonlinear least squares is presented and is combined with the modified back propagation algorithm yielding a new fast training multilayer perceptron (MLP) algorithm (CGFR/AG). The approaches presented in the paper consist of three steps: (1) Modification on standard back propagation algorithm by introducing gain variation term of th...

A new framework based on the curved Riemannian manifold is proposed to calculate the numerical solution of the Lyapunov matrix equation by using a natural gradient descent algorithm and taking the geodesic distance as the objective function. Moreover, a gradient descent algorithm based on the classical Euclidean distance is provided to compare with this natural gradient descent algorithm. Furth...