Optimization techniques are among the most promising methods to deal with real-world
problems, consisting of several objective functions and constraints. Over the decades, many …

In this paper, a hybrid version of the Rivaie–Mustafa–Ismail–Leong, RMIL for short,
conjugate gradient method is proposed for unconstrained optimization problem. The search …

The conjugate gradient (CG) method is an optimization method, which, in its application, has
a fast convergence. Until now, many CG methods have been developed to improve …

Scalarization approaches transform vector optimization problems (VOPs) into single-
objective optimization but have trade-offs: information loss, subjective weight assignments …

In this paper, using the concept of inertial extrapolation, we introduce a globally convergent
inertial extrapolation method for solving nonlinear equations with convex constraints for …

Conjugate gradient methods have played a vital role in finding the minimizers of large-scale
unconstrained optimization problems due to the simplicity of their iteration, convergence …

Conjugate gradient method is one of the most effective methods for solving large-scale
optimization problems. Based on the CD conjugate parameter and an improved PRP …

In optimization theory, to speed up the convergence of iterative procedures, many
mathematicians often use the inertial extrapolation method. In this article, based on the three …

The conjugate gradient projection method (CGPM) has good theoretical properties and
numerical performance for solving large-scale nonlinear monotone equations with convex …

In this paper, we introduce a derivative‐free iterative method for finding the solutions of
convex constrained nonlinear equations (CCNE) using the projection strategy. The new …