In this work, we propose nonlinear conjugate gradient methods for finding critical points of
vector-valued functions with respect to the partial order induced by a closed, convex, and …

In this work, we present an extension of the spectral conjugate gradient (SCG) methods for
solving unconstrained vector optimization problems, with respect to the partial order induced …

This paper attempts to propose Dai–Liao (DL)-type nonlinear conjugate gradient (CG)
methods for solving vector optimization problems. Four variants of the DL method are …

This paper presents an accelerated proximal gradient method for multiobjective
optimization, in which each objective function is the sum of a continuously differentiable …

We present a rigorous and comprehensive survey on extensions to the multicriteria setting of
three well-known scalar optimization algorithms. Multiobjective versions of the steepest …

This study analyzes the conditional gradient method for constrained multiobjective
optimization problems, also known as the Frank–Wolfe method. We assume that the …

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

We propose a BFGS method with Wolfe line searches for unconstrained multiobjective
optimization problems. The algorithm is well defined even for general nonconvex problems …

The extension of the Hager–Zhang (HZ) nonlinear conjugate gradient method for vector
optimization is discussed in the present research. In the scalar minimization case, this …

Recently, Gonçalves and Prudente proposed an extension of the Hager–Zhang nonlinear
conjugate gradient method for vector optimization (Comput Optim Appl 76: 889–916, 2020) …