An unconstrained stress updating algorithm with the line search method for elastoplastic soil models

2022 ◽  
Vol 143 ◽  
pp. 104592
Author(s):  
Xin Zhou ◽  
Dechun Lu ◽  
Cancan Su ◽  
Zhiwei Gao ◽  
Xiuli Du
Keyword(s):  
Line Search ◽  
Search Method ◽  
Line Search Method ◽  
Elastoplastic Soil

scholarly journals A randomized adaptive trust region line search method

2020 ◽  
Vol 10 (2) ◽  
pp. 259-263
Author(s):  
Saman Babaie-Kafaki ◽  
Saeed Rezaee
Keyword(s):  
Simulated Annealing ◽  
Numerical Experiments ◽  
Line Search ◽  
Optimization Problems ◽  
Trust Region ◽  
Search Method ◽  
Test Problems ◽  
Unconstrained Optimization Problems ◽  
Line Search Method ◽  
Region Line

Hybridizing the trust region, line search and simulated annealing methods, we develop a heuristic algorithm for solving unconstrained optimization problems. We make some numerical experiments on a set of CUTEr test problems to investigate efficiency of the suggested algorithm. The results show that the algorithm is practically promising.

LEARNING AN ADAPTIVE DICTIONARY USING A PROJECTED GRADIENT METHOD AND ITS APPLICATION ON IMAGE DE-NOISING

2012 ◽  
Vol 10 (01) ◽  
pp. 1250004 ◽  
Author(s):  
ZEMIN CAI ◽  
JIANHUANG LAI ◽  
CHAOQIANG TAN ◽  
JINGWEN YAN
Keyword(s):  
Large Scale ◽  
Line Search ◽  
Search Method ◽  
Quadratic Program ◽  
Projected Gradient Method ◽  
Learning Problem ◽  
Projected Gradient ◽  
Programming Framework ◽  
Line Search Method ◽  
Made In

In recent years, considerable efforts have been made in the research of sparse representation for signals over overcomplete dictionaries. The dictionaries can be either pre-specified transforms or designed by learning from a set of training signals. In the paper, the dictionary learning problem was extended into a quadratic programming framework. A projected gradient with line search method was presented for solving this large-scale box-constrained quadratic program. The non-negative dictionary learned using this method was applied to image de-noising. Experimental results demonstrated that this learning-based method had better performance than the wavelet-based, the variation-based and the K-SVD methods.

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