A nonmonotone line search method for noisy minimization

2015 ◽  
Vol 9 (7) ◽  
pp. 1371-1391 ◽  
Author(s):  
Nataša Krejić ◽  
Zorana Lužanin ◽  
Filip Nikolovski ◽  
Irena Stojkovska
Keyword(s):  
Line Search ◽  
Search Method ◽  
Nonmonotone Line Search ◽  
Line Search Method

scholarly journals A nonmonotone line search method for stochastic optimization problems

Filomat ◽  
2018 ◽  
Vol 32 (19) ◽  
pp. 6799-6807
Author(s):  
Natasa Krejic ◽  
Sanja Loncar
Keyword(s):  
Objective Function ◽  
Line Search ◽  
Optimization Problems ◽  
Search Method ◽  
Nonmonotone Line Search ◽  
Unconstrained Optimization Problems ◽  
Convergence Results ◽  
Line Search Method ◽  
Upper Density ◽  
Negative Gradient

A nonmonotone line search method for solving unconstrained optimization problems with the objective function in the form of mathematical expectation is proposed and analyzed. The method works with approximate values of the objective function obtained with increasing sample sizes and improves accuracy gradually. Nonmonotone rule significantly enlarges the set of admissible search directions and prevents unnecessarily small steps at the beginning of the iterative procedure. The convergence is shown for any search direction that approaches the negative gradient in the limit. The convergence results are obtained in the sense of zero upper density. Initial numerical results confirm theoretical results and show efficiency of the proposed approach.

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.

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