scholarly journals On the Bridge Between Computational Results Of New Line Search Technique

2021 ◽  
Vol 1818 (1) ◽  
pp. 012135
Author(s):  
Nofl Sh. Al-Shimari ◽  
Basim Rabaa Jumaa ◽  
Ahmed Sabah. Al-Jilawi
Keyword(s):  
Line Search ◽  
Computational Results ◽  
Search Technique ◽  
Line Search Technique

scholarly journals A Nonmonotone Weighting Self-Adaptive Trust Region Algorithm for Unconstrained Nonconvex Optimization

2015 ◽  
Vol 2015 ◽  
pp. 1-8
Author(s):  
Yunlong Lu ◽  
Weiwei Yang ◽  
Wenyu Li ◽  
Xiaowei Jiang ◽  
Yueting Yang
Keyword(s):  
Nonconvex Optimization ◽  
Line Search ◽  
Optimization Problems ◽  
Trust Region Method ◽  
Trust Region ◽  
Search Technique ◽  
Trust Region Algorithm ◽  
Unconstrained Optimization Problems ◽  
Line Search Technique ◽  
Self Adaptive

A new trust region method is presented, which combines nonmonotone line search technique, a self-adaptive update rule for the trust region radius, and the weighting technique for the ratio between the actual reduction and the predicted reduction. Under reasonable assumptions, the global convergence of the method is established for unconstrained nonconvex optimization. Numerical results show that the new method is efficient and robust for solving unconstrained optimization problems.

scholarly journals The Number Density Evolution of Extreme Emission Line Galaxies in 3D-HST: Results from a Novel Automated Line Search Technique for Slitless Spectroscopy

2018 ◽  
Vol 854 (1) ◽  
pp. 29 ◽  
Author(s):  
Michael V. Maseda ◽  
Arjen van der Wel ◽  
Hans-Walter Rix ◽  
Ivelina Momcheva ◽  
Gabriel B. Brammer ◽  
...  
Keyword(s):  
Emission Line ◽  
Line Search ◽  
Number Density ◽  
Search Technique ◽  
Density Evolution ◽  
Line Search Technique ◽  
Emission Line Galaxies ◽  
Automated Line

A Simulated Annealing-Based Barzilai–Borwein Gradient Method for Unconstrained Optimization Problems

2019 ◽  
Vol 36 (04) ◽  
pp. 1950017 ◽  
Author(s):  
Wen-Li Dong ◽  
Xing Li ◽  
Zheng Peng
Keyword(s):  
Simulated Annealing ◽  
Unconstrained Optimization ◽  
Gradient Method ◽  
Line Search ◽  
High Efficiency ◽  
Optimization Problems ◽  
Search Technique ◽  
Unconstrained Optimization Problems ◽  
Armijo Line Search ◽  
Line Search Technique

In this paper, we propose a simulated annealing-based Barzilai–Borwein (SABB) gradient method for unconstrained optimization problems. The SABB method accepts the Barzilai–Borwein (BB) step by a simulated annealing rule. If the BB step cannot be accepted, the Armijo line search is used. The global convergence of the SABB method is established under some mild conditions. Numerical experiments indicate that, compared to some existing BB methods using nonmonotone line search technique, the SABB method performs well with high efficiency.

scholarly journals A Line-Search-Based Partial Proximal Alternating Directions Method for Separable Convex Optimization

2014 ◽  
Vol 2014 ◽  
pp. 1-8
Author(s):  
Yu-hua Zeng ◽  
Yu-fei Yang ◽  
Zheng Peng
Keyword(s):  
Convex Optimization ◽  
Line Search ◽  
Optimization Problems ◽  
Proximal Point Method ◽  
Search Technique ◽  
Proximal Point ◽  
Separable Convex Optimization ◽  
Line Search Technique ◽  
Alternating Directions Method ◽  
Proximal Term

We propose an appealing line-search-based partial proximal alternating directions (LSPPAD) method for solving a class of separable convex optimization problems. These problems under consideration are common in practice. The proposed method solves two subproblems at each iteration: one is solved by a proximal point method, while the proximal term is absent from the other. Both subproblems admit inexact solutions. A line search technique is used to guarantee the convergence. The convergence of the LSPPAD method is established under some suitable conditions. The advantage of the proposed method is that it provides the tractability of the subproblem in which the proximal term is absent. Numerical tests show that the LSPPAD method has better performance compared with the existing alternating projection based prediction-correction (APBPC) method if both are employed to solve the described problem.

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