A parallel processing multi-coordinate descent method with line search for a class of large-scale optimization-algorithm and convergence

Author(s):  
S.-Y. Lin
Author(s):  
Jie Guo ◽  
Zhong Wan

A new spectral three-term conjugate gradient algorithm in virtue of the Quasi-Newton equation is developed for solving large-scale unconstrained optimization problems. It is proved that the search directions in this algorithm always satisfy a sufficiently descent condition independent of any line search. Global convergence is established for general objective functions if the strong Wolfe line search is used. Numerical experiments are employed to show its high numerical performance in solving large-scale optimization problems. Particularly, the developed algorithm is implemented to solve the 100 benchmark test problems from CUTE with different sizes from 1000 to 10,000, in comparison with some similar ones in the literature. The numerical results demonstrate that our algorithm outperforms the state-of-the-art ones in terms of less CPU time, less number of iteration or less number of function evaluation.


2021 ◽  
Author(s):  
Hazim Nasir Ghafil ◽  
Shaymaa Alsamia ◽  
Károly Jármai

Abstract This work, presents a novel optimizer called fertilization optimization algorithm, which is based on levy flight and random search within a search space. It is a biologically inspired algorithm by the fertilization of the egg in reproduction of mammals. The performance of the algorithm was compared with other optimization algorithms on CEC2015 time expensive benchmarks and large scale optimization problems. Remarkably, the fertilization optimization algorithm has overcome other optimizers in many cases and the examination and comparison results are encouraging to use the fertilization optimization algorithm in other possible applications.


2018 ◽  
Vol 272 ◽  
pp. 471-480 ◽  
Author(s):  
Xiangfeng Wang ◽  
Wenjie Zhang ◽  
Junchi Yan ◽  
Xiaoming Yuan ◽  
Hongyuan Zha

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