scholarly journals Accelerating sailfish optimization applied to unconstrained optimization problems on graphical processing unit

2021 ◽  
pp. 101077
Author(s):  
Hamid Reza Naji ◽  
Soodeh Shadravan ◽  
Hossien Mousa Jafarabadi ◽  
Hossien Momeni
Keyword(s):  
Unconstrained Optimization ◽  
Optimization Problems ◽  
Graphical Processing Unit ◽  
Processing Unit ◽  
Unconstrained Optimization Problems ◽  
Graphical Processing

A NEW COEFFICIENT OF CONJUGATE GRADIENT METHODS FOR NONLINEAR UNCONSTRAINED OPTIMIZATION

2016 ◽  
Vol 78 (6-4) ◽  
Author(s):  
Nur Syarafina Mohamed ◽  
Mustafa Mamat ◽  
Fatma Susilawati Mohamad ◽  
Mohd Rivaie
Keyword(s):  
Unconstrained Optimization ◽  
Conjugate Gradient ◽  
Optimization Problems ◽  
Gradient Methods ◽  
Standard Test ◽  
Processing Unit ◽  
Central Processing ◽  
Exact Line Search ◽  
Unconstrained Optimization Problems ◽  
Nonlinear Unconstrained Optimization

Conjugate gradient (CG) methods are widely used in solving nonlinear unconstrained optimization problems such as designs, economics, physics and engineering due to its low computational memory requirement. In this paper, a new modifications of CG coefficient ( ) which possessed global convergence properties is proposed by using exact line search. Based on the number of iterations and central processing unit (CPU) time, the numerical results show that the new  performs better than some other well known CG methods under some standard test functions.

A Non-Monotone Line Search Combination Technique for Unconstrained Optimization

2014 ◽  
Vol 8 (1) ◽  
pp. 218-221 ◽  
Author(s):  
Ping Hu ◽  
Zong-yao Wang
Keyword(s):  
Global Convergence ◽  
Unconstrained Optimization ◽  
Numerical Experiments ◽  
Line Search ◽  
Optimization Problems ◽  
Search Algorithm ◽  
New Combination ◽  
Combination Rule ◽  
Combination Technique ◽  
Unconstrained Optimization Problems

We propose a non-monotone line search combination rule for unconstrained optimization problems, the corresponding non-monotone search algorithm is established and its global convergence can be proved. Finally, we use some numerical experiments to illustrate the new combination of non-monotone search algorithm’s effectiveness.

Mixed-mode database miner classifier: Parallel computation of graphical processing unit mining

2021 ◽  
pp. 002072092098849
Author(s):  
Soumya Ranjan Nayak ◽  
S Sivakumar ◽  
Akash Kumar Bhoi ◽  
Gyoo-Soo Chae ◽  
Pradeep Kumar Mallick
Keyword(s):  
Credit Card ◽  
Mixed Mode ◽  
Processing Time ◽  
Gpu Computing ◽  
Graphical Processing Unit ◽  
Computational Time ◽  
Processing Unit ◽  
Large Set ◽  
Minimal Processing ◽  
Graphical Processing

Graphical processing unit (GPU) has gained more popularity among researchers in the field of decision making and knowledge discovery systems. However, most of the earlier studies have GPU memory utilization, computational time, and accuracy limitations. The main contribution of this paper is to present a novel algorithm called the Mixed Mode Database Miner (MMDBM) classifier by implementing multithreading concepts on a large number of attributes. The proposed method use the quick sort algorithm in GPU parallel computing to overcome the state of the art limitations. This method applies the dynamic rule generation approach for constructing the decision tree based on the predicted rules. Moreover, the implementation results are compared with both SLIQ and MMDBM using Java and GPU with the computed acceleration ratio time using the BP dataset. The primary objective of this work is to improve the performance with less processing time. The results are also analyzed using various threads in GPU mining using eight different datasets of UCI Machine learning repository. The proposed MMDBM algorithm have been validated on these chosen eight different dataset with accuracy of 91.3% in diabetes, 89.1% in breast cancer, 96.6% in iris, 89.9% in labor, 95.4% in vote, 89.5% in credit card, 78.7% in supermarket and 78.7% in BP, and simultaneously, it also takes less computational time for given datasets. The outcome of this work will be beneficial for the research community to develop more effective multi thread based GPU solution in GPU mining to handle large set of data in minimal processing time. Therefore, this can be considered a more reliable and precise method for GPU computing.

Parallel-vector computation for linear structural analysis and non-linear unconstrained optimization problems

1991 ◽  
Vol 2 (2-3) ◽  
pp. 175-182 ◽  
Author(s):  
D.T. Nguyen ◽  
O.O. Storaasli ◽  
E.A. Carmona ◽  
M. Al-Nasra ◽  
Y. Zhang ◽  
...  
Keyword(s):  
Structural Analysis ◽  
Unconstrained Optimization ◽  
Optimization Problems ◽  
Unconstrained Optimization Problems ◽  
Non Linear
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