A Novel Value for the Parameter in the Dai-Liao-Type Conjugate Gradient Method

Autor: Branislav Ivanov, Predrag S. Stanimirović, Bilall I. Shaini, Hijaz Ahmad, Miao-Kun Wang
Jazyk: angličtina
Rok vydání: 2021
Předmět:
Zdroj: Journal of Function Spaces, Vol 2021 (2021)
Druh dokumentu: article
ISSN: 2314-8896
2314-8888
DOI: 10.1155/2021/6693401
Popis: A new rule for calculating the parameter t involved in each iteration of the MHSDL (Dai-Liao) conjugate gradient (CG) method is presented. The new value of the parameter initiates a more efficient and robust variant of the Dai-Liao algorithm. Under proper conditions, theoretical analysis reveals that the proposed method in conjunction with backtracking line search is of global convergence. Numerical experiments are also presented, which confirm the influence of the new value of the parameter t on the behavior of the underlying CG optimization method. Numerical comparisons and the analysis of obtained results considering Dolan and Moré’s performance profile show better performances of the novel method with respect to all three analyzed characteristics: number of iterative steps, number of function evaluations, and CPU time.
Databáze: Directory of Open Access Journals
Externí odkaz:
Nepřihlášeným uživatelům se plný text nezobrazuje