The nonlinear conjugate gradient method is a very useful technique for solving large scale minimization problems and has wide applications in many fields. In this paper, we present a new algorithm of ...

Abstract: The conjugate gradient technique has been broadly used to solve various unconstrained problems. In this paper, we present an extension of a nonlinear conjugate gradient algorithm for the ...

Conjugate gradient methods form a class of iterative algorithms that are highly effective for solving large‐scale unconstrained optimisation problems. They achieve efficiency by constructing search ...

Abstract: This thesis further study descent conjugate gradient methods based on the modified FR method and the modified PRP method give the class of conjugate gradient methods formed by the convex ...

This is a preview. Log in through your library . Abstract In this paper, we focus on the stochastic inverse eigenvalue problem of reconstructing a stochastic matrix from the prescribed spectrum. We ...

In this paper we test different conjugate gradient (CG) methods for solving largescale unconstrained optimization problems. The methods are divided in two groups: the first group includes five basic ...

The Fletcher-Reeves conjugate gradient ( FR ) method is the earliest nonlinear conjugate gradient method. It was obtained by Fletcher and Reeves in 1964 by extending the conjugate gradient method for ...

There are several optimization techniques available in PROC NLMIXED. You can choose a particular optimizer with the TECH=name option in the PROC NLMIXED statement. No algorithm for optimizing general ...

Compute $\boldsymbol{r}^{\left( 0 \right)}=\boldsymbol{b}-\boldsymbol{Ax}^{\left( 0 \right)}$, and set $\boldsymbol{p}^{\left( 0 \right)}=\boldsymbol{r}^{\left( 0 ...