This book is on conjugate gradient methods for unconstrained optimization. The concept of
conjugacy was introduced by Magnus Hestenes and Garrett Birkhoff in 1936 in the context of …

Time-varying nonlinear optimization problems with different noises often arise in the fields of
scientific and engineering research. Noises are unavoidable in the practical workspace, but …

For the purpose of solving a large-scale system of nonlinear equations, a hybrid conjugate
gradient algorithm is introduced in this paper, based on the convex combination of βFR kand …

En muchas ocasiones se presentan problemas de regresión donde se desea estimar de
manera simultánea más de un rasgo o variable real. En estos casos se pueden modelar …

Dai-Yuan (DY) conjugate gradient method is an effective method for solving large-scale
unconstrained optimization problems. In this paper, a new DY method, possessing a …

According to a minimization problem founded upon the Byrd–Nocedal measure function
coupled with a penalty term of the secant equation, a diagonal quasi–Newton updating …

In this paper, a new spectral scaling memoryless Broyden-Fletcher-Goldfarb-Shanno
(BFGS) algorithm is developed for solving large scale unconstrained optimization problems …

In this paper, we propose a new hybrid conjugate gradient method to solve unconstrained
optimization problems. This new method is defined as a convex combination of DY and DL …

Semi-supervised symmetric nonnegative matrix factorization (SNMF) has been extensively
utilized in both linear and nonlinear data clustering tasks. However, the current SNMF …

In our research, a new conjugate gradient (CG) method for solving nonlinear systems of
equations is introduced and studied. The proposed method defined as a convex …