@Article{cmc.2020.02993,
AUTHOR = {Wujie Hu, Gonglin Yuan, Hongtruong Pham},
TITLE = {A Modified Three-Term Conjugate Gradient Algorithm for Large-Scale Nonsmooth Convex Optimization},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {62},
YEAR = {2020},
NUMBER = {2},
PAGES = {787--800},
URL = {http://www.techscience.com/cmc/v62n2/38276},
ISSN = {1546-2226},
ABSTRACT = {It is well known that Newton and quasi-Newton algorithms are effective to small 
and medium scale smooth problems because they take full use of corresponding gradient 
function’s information but fail to solve nonsmooth problems. The perfect algorithm stems 
from concept of ‘bundle’ successfully addresses both smooth and nonsmooth complex 
problems, but it is regrettable that it is merely effective to small and medium optimization 
models since it needs to store and update relevant information of parameter’s bundle. The 
conjugate gradient algorithm is effective both large-scale smooth and nonsmooth 
optimization model since its simplicity that utilizes objective function’s information and the 
technique of Moreau-Yosida regularization. Thus, a modified three-term conjugate gradient 
algorithm was proposed, and it has a sufficiently descent property and a trust region 
character. At the same time, it possesses the global convergence under mild assumptions 
and numerical test proves it is efficient than similar optimization algorithms.},
DOI = {10.32604/cmc.2020.02993}
}