Interior quasi-subgradient method with non-Euclidean distances for constrained quasi-convex optimization problems in hilbert spaces

2021 ◽  
Author(s):  
Regina S. Burachik ◽  
Yaohua Hu ◽  
Xiaoqi Yang
Keyword(s):  
Convex Optimization ◽  
Hilbert Spaces ◽  
Optimization Problems ◽  
Subgradient Method ◽  
Convex Optimization Problems ◽  
Euclidean Distances

scholarly journals Iterative approximation of solution of split variational inclusion problem

Filomat ◽  
2018 ◽  
Vol 32 (8) ◽  
pp. 2921-2932 ◽  
Author(s):  
Jeremiah Ezeora ◽  
Chinedu Izuchukwu
Keyword(s):  
Convex Optimization ◽  
Hilbert Spaces ◽  
Optimization Problems ◽  
Optimization Theory ◽  
Variational Problems ◽  
Variational Inclusion ◽  
Inclusion Problem ◽  
Iterative Approximation ◽  
Convex Optimization Problems ◽  
Variational Inclusion Problem

Following recent important results of Moudafi [Journal of Optimization Theory and Applications 150(2011), 275-283] and other related results on variational problems, we introduce a new iterative algorithm for approximating a solution of monotone variational inclusion problem involving multi-valued mapping. The sequence of the algorithm is proved to converge strongly in the setting of Hilbert spaces. As application, we solved split convex optimization problems.

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