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: In this paper, a new block adaptive filtering algorithm, based on the Conjugate Gradient (CG) method of optimization, is proposed. A Toeplitz approximation of the autocorrelation matrix is ...

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: Fuzzy system of linear equations (FSLE) comes from many fields, including mathematics, physics, engineering and so on. It is an interesting work to study numerical methods solving the system ...

This study introduced an efficient method for solving non-linear equations. Our approach enhances the traditional spectral conjugate gradient parameter, resulting in significant improvements in the ...

This is a PyTorch based machine learning project that focuses on implementing the Trust Region Newton Conjugate Gradient (TRNCG) optimization algorithm to train a neural network. Since TRNCG is not ...

The primary objective of this paper is to develop a novel and efficient modified conjugate gradient algorithm for addressing nonconvex minimization problems. The study demonstrates that the proposed ...

MATLAB package of iterative regularization methods and large-scale test problems. This software is described in the paper "IR Tools: A MATLAB Package of Iterative Regularization Methods and ...

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 ...