On random minimization of functions

1991 ◽  
Vol 65 (6) ◽  
pp. 501-506 ◽  
Author(s):  
G. P. Tecchiolli ◽  
R. Brunelli
Keyword(s):  
Minimization Of Functions

Memory gradient method for the minimization of functions

1970 ◽  
pp. 252-263 ◽  
Author(s):  
A. Miele ◽  
J. W. Cantrell
Keyword(s):  
Gradient Method ◽  
Minimization Of Functions ◽  
Memory Gradient Method

CGS algorithms for unconstrained minimization of functions

1975 ◽  
Vol 16 (1-2) ◽  
pp. 67-85 ◽  
Author(s):  
R. F. Dennemeyer ◽  
E. H. Mookini
Keyword(s):  
Unconstrained Minimization ◽  
Minimization Of Functions

scholarly journals Iteration and evaluation complexity for the minimization of functions whose computation is intrinsically inexact

2019 ◽  
Vol 89 (321) ◽  
pp. 253-278 ◽  
Author(s):  
E. G. Birgin ◽  
N. Krejić ◽  
J. M. Martínez
Keyword(s):  
Minimization Of Functions ◽  
Evaluation Complexity

Linearly constrained global minimization of functions with concave minorants

1996 ◽  
Vol 88 (3) ◽  
pp. 751-763 ◽  
Author(s):  
R. Horst ◽  
M. Nast
Keyword(s):  
Global Minimization ◽  
Linearly Constrained ◽  
Minimization Of Functions

scholarly journals A Metaheuristic-Based Tool for Function Minimization

2019 ◽  
Vol 2 (3) ◽  
pp. 613-620
Author(s):  
Yigit Cagatay Kuyu ◽  
Fahri Vatansever
Keyword(s):  
Real World ◽  
Metaheuristic Algorithms ◽  
Function Minimization ◽  
Computational Time ◽  
Scientific Interest ◽  
Important Place ◽  
Optimization Function ◽  
Minimization Of Functions ◽  
User Friendly ◽  
Real World Problems

During the last decade, metaheuristic algorithms have occupied an important place in the field of optimization. Function minimization is of importance to researchers since many real-world problems can be modeled mathematically and be solved effectively through metaheuristic algorithms. Due to the growing scientific interest in the field of optimization and the good performances shown by the algorithms on function minimization, the practical and quick implementation concept is necessary to select the most appropriate algorithms on function minimization, and to assist researchers in analyzing the performance of the algorithms. In this study, a tool is developed to minimize user-defined functions in a specified range according to the chosen metaheuristic algorithms, which allows analyzing the algorithms in the general experimental environment. The tool, which has a user-friendly interface, can provide single and comparative solutions by simultaneously executing the algorithms. Each solution and computational time obtained by the algorithms is given numerically, and the convergence behavior of the algorithms is shown graphically in the tool interface. Minimization of functions can be made fast, easily and effectively through the developed tool.

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