Work

Some descent Dai-Liao-type conjugate gradient methods for vector optimization

Year: 2025

Type: article

Abstract: Conjugate gradient methods are considered vital for solving unconstrained single-objective optimization problems and have recently been extended to vector optimization problems (VOPs). In this paper, four conjugate gradient methods for VOPs, inspired by the Dai-Liao method, are introduced. Initially, two Dai-Liao-type conjugate gradient methods are proposed, which, although they do not guarantee a descent direction, are shown to converge under the assumption of descent direction. These methods are refined to create two modified versions that satisfy the sufficient descent condition. By employing the Wolfe line search, the sufficient descent condition is satisfied, and global convergence is achieved without requiring regular restarts or assumptions of convexity on the objective functions. Numerical experiments are conducted to demonstrate the effectiveness of the proposed methods with detailed implementation and results provided.

Source: Franklin Open

DOI: 10.1016/j.fraope.2025.100231

Authors: Jamilu Yahaya, Poom Kumam, Abdulmalik Usman Bello

Institutions: Ahmadu Bello University, Center of Excellence in Theoretical and Computational Science, King Mongkut's University of Technology Thonburi, African Institute of Science and Technology, Federal University Dutsin-Ma

Language: English

Topic: Advanced Optimization Algorithms Research

Subfield: Numerical Analysis

Field: Mathematics

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $1,900

Funder: King Mongkut's University of Technology Thonburi