Global Convergence Condition for a New Spectral Conjugate Gradient Method for Large-Scale Optimization

Sheekoo, Ahmed Hussien; Al-Naemi, Ghada M.

doi:10.1088/1742-6596/1879/3/032001

Cited by 2 publications

(1 citation statement)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Recently, global convergence results for CG methods under various inexact line searches have been established in Refs. [23][24][25]. To demonstrate the convergence of CG techniques, the steplength φ n typically needs to meet the strong Wolfe (SW) conditions put forth by Wolfe [26] and provided by:…”

Section: Introductionmentioning

confidence: 99%

Global convergence properties of a Dai-Liao-type CGM for unconstrained optimization

Onuoha

2024

Recent Adv. Nat. Sci

View full text Add to dashboard Cite

A popular optimization technique called the conjugate gradient method (CGM) is renowned for its effectiveness in addressing problems involving unconstrained optimization. Several conjugate gradient (CG) techniques have been proven to possess global convergence properties both theoretically and numerically. The Dai-Liao-type CGM is a variant that incorporates certain modifications to enhance its convergence properties. This paper examines the global convergence properties of a Dai-Liao-type CGM for unconstrained optimization problems. Theoretically, this study investigates the conditions under which the method ensures convergence to the global minimum of the objective function, focusing on the algorithm’s descent directions, the necessary reduction in objective function values, and termination criteria. A numerical experiment is conducted on a set of unconstrained optimization problems to validate the theoretical results obtained in this work. The numerical findings of this study demonstrate the robustness and reliability of the Dai-Liao-type CGM, showing its ability to find the global optimal solution in a wide range of unconstrained optimization problems.

show abstract

Section: Introductionmentioning

confidence: 99%

Global convergence properties of a Dai-Liao-type CGM for unconstrained optimization

Onuoha

2024

Recent Adv. Nat. Sci

View full text Add to dashboard Cite

show abstract

A New Spectral Three‐Term Conjugate Gradient Method with Random Parameter Based on Modified Secant Equation and Its Application to Low‐Carbon Supply Chain Optimization

Zhou

Yang

Cao

2022

Journal of Mathematics

View full text Add to dashboard Cite

In this work, considering the advantages of spectral conjugate gradient method and quasi-Newton method, a spectral three-term conjugate gradient method with random parameter is proposed. The parameter in the search direction of the new method is determined by minimizing the Frobenius norm of difference between search direction matrix and self-scaled memoryless BFGS matrix based on modified secant equation. Then, the search direction satisfying the sufficient descent condition is obtained. The global convergence of new method is proved under appropriate assumptions. Numerical experiments show that our method has better performance by comparing with the up-to-date method. Furthermore, the new method has been successfully applied to the optimization of low-carbon supply chain.

show abstract

Global Convergence Condition for a New Spectral Conjugate Gradient Method for Large-Scale Optimization

Cited by 2 publications

References 14 publications

Global convergence properties of a Dai-Liao-type CGM for unconstrained optimization

Global convergence properties of a Dai-Liao-type CGM for unconstrained optimization

A New Spectral Three‐Term Conjugate Gradient Method with Random Parameter Based on Modified Secant Equation and Its Application to Low‐Carbon Supply Chain Optimization

Contact Info

Product

Resources

About