limited memory bfgs method
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2014 ◽  
Vol 530-531 ◽  
pp. 367-371
Author(s):  
Ting Feng Li ◽  
Yu Ting Zhang ◽  
Sheng Hui Yan

In this paper, a modified limited memory BFGS method for solving large-scale unconstrained optimization problems is proposed. A remarkable feature of the proposed method is that it possesses a global convergence property even without convexity assumption on the objective function. The implementations of the algorithm on CUTE test problems are reported, which suggest that a slight improvement has been achieved.


2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Xiangrong Li ◽  
Xiaoliang Wang ◽  
Xiabin Duan

A limited memory BFGS (L-BFGS) algorithm is presented for solving large-scale symmetric nonlinear equations, where a line search technique without derivative information is used. The global convergence of the proposed algorithm is established under some suitable conditions. Numerical results show that the given method is competitive to those of the normal BFGS methods.


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