scholarly journals Optimal Combination of Tensor Optimization Methods

2020 ◽  
pp. 166-183
Author(s):  
Dmitry Kamzolov ◽  
Alexander Gasnikov ◽  
Pavel Dvurechensky
Keyword(s):  
Optimization Methods ◽  
Optimal Combination ◽  
Tensor Optimization

Three-Stage Low Pressure Compressor Modernization by Means of Optimization Methods

2015 ◽  
Author(s):  
Evgenii Goryachkin ◽  
Grigorii Popov ◽  
Oleg Baturin ◽  
Daria Kolmakova
Keyword(s):  
Mass Flow ◽  
Pressure Ratio ◽  
Optimization Methods ◽  
Low Pressure ◽  
Optimal Combination ◽  
Pareto Set ◽  
Optimization Task ◽  
Efficiency Increase ◽  
Optimization Parameters ◽  
Pressure Compressor

Low pressure compressor operation has some features. Firstly, the LPC stages work with cold air. For this reason there is transonic or subsonic flow in LPC. Secondly, the flow in LPC has complex spatial structure. Blade geometry of LPC is described by a large number of parameters. For this reason, it is difficult to pick up optimal combination of parameters manually. The solution of this problem is the usage of optimization methods to find the optimal combination of parameters. This approach was tested in this work. The main goal of this work was the LPC modernization for new parameters of gas turbine engine. Set of unimprovable solutions (Pareto set) was obtained as a result of solving optimization task. Pareto set was a compromise between the efficiency increase and the mass flow decrease. Each point from Pareto set had a correspondence with LPC unique geometry represented as an array of optimization parameters. One point of the Pareto set met all the required parameters of modernized LPC. The LPC geometry that guaranteed the efficiency increase by 1,3%, the total pressure ratio increase by 4% and mass flow rate decrease by 11% in comparison with the original LPC was obtained as a result of the investigation.

scholarly journals Drilling of a Bidirectional Jute Fibre and Cork-Reinforced Polymer Biosandwich Structure: ANN and RSM approaches for Modelling and Optimization

2021 ◽  
Author(s):  
Zohir Tabet ◽  
Ahmed Belaadi ◽  
Messaouda Boumaaza ◽  
Mostefa Bourchak
Keyword(s):  
Cutting Parameters ◽  
Optimization Methods ◽  
Optimal Combination ◽  
Jute Fibre ◽  
Optimum Conditions ◽  
Severe Damage ◽  
Reinforced Polymer ◽  
Drilling Parameters ◽  
Twist Drills ◽  
Modelling And Optimization

Abstract The present study examined the effects of drilling parameters such as spindle speed (N), feed rate (f), diameter of the tools (d) and drill geometry such as twist drills (HSS-TiN) and brad & spur drills (BSD) used on delamination damage in a biosandwich structure consisting of an epoxy matrix reinforced with bidirectional jute fibres and cork (JFCE). Response surface methodology (RSM) and artificial neural networks (ANNs) were exploited to evaluate the influence and interaction of the cutting parameters on the delamination factor (Fd) at the output during drilling. In addition, several optimization methods, such as desirability-based RSM, the genetic algorithm (GA) and the fmincon function, were applied to validate the optimal combination of cutting parameters (f, N and d) in the structures studied in biosandwiches during this research. According to the experimental results, severe damage was indeed observed with the BSD tool (Fd = 1.684) compared to the HSS-TiN tool (Fd = 1.555) for the same cutting conditions. To obtain the minimum Fd, the optimum conditions obtained by GA were respectively 1397.54 rev/min, 51.162 mm/min and 5.981 mm for HSS-TiN for f, N and d.

scholarly journals Weighted Random Search for CNN Hyperparameter Optimization

2020 ◽  
Vol 15 (2) ◽  
Author(s):  
Razvan Andonie ◽  
Adrian-Catalin Florea
Keyword(s):  
Neural Network ◽  
Classification Accuracy ◽  
State Of The Art ◽  
Random Search ◽  
Optimization Methods ◽  
Optimal Combination ◽  
The Other ◽  
Greedy Heuristic ◽  
Training Phase ◽  
Hyperparameter Optimization

Nearly all model algorithms used in machine learning use two different sets of parameters: the training parameters and the meta-parameters (hyperparameters). While the training parameters are learned during the training phase, the values of the hyperparameters have to be specified before learning starts. For a given dataset, we would like to find the optimal combination of hyperparameter values, in a reasonable amount of time. This is a challenging task because of its computational complexity. In previous work [11], we introduced the Weighted Random Search (WRS) method, a combination of Random Search (RS) and probabilistic greedy heuristic. In the current paper, we compare the WRS method with several state-of-the art hyperparameter optimization methods with respect to Convolutional Neural Network (CNN) hyperparameter optimization. The criterion is the classification accuracy achieved within the same number of tested combinations of hyperparameter values. According to our experiments, the WRS algorithm outperforms the other methods.

Optimization Methods in Finance

2018 ◽  
Author(s):  
Gérard Cornuéjols ◽  
Javier Peña ◽  
Reha Tütüncü
Keyword(s):  
Optimization Methods

Optimization Methods in Finance

2006 ◽  
Author(s):  
Gerard Cornuejols ◽  
Reha Tutuncu
Keyword(s):  
Optimization Methods

Applied Optimization Methods for Wireless Networks

2009 ◽  
Author(s):  
Y. Thomas Hou ◽  
Yi Shi ◽  
Hanif D. Sherali
Keyword(s):  
Wireless Networks ◽  
Optimization Methods ◽  
Applied Optimization

Maksimalisasi Keuntungan Dengan Pendekatan Metode Simpleks Kasus Pada Pabrik Sosis SM

Liquidity ◽  
2018 ◽  
Vol 2 (1) ◽  
pp. 59-65 ◽  
Author(s):  
Yanti Budiasih
Keyword(s):  
Linear Programming ◽  
Simplex Method ◽  
Optimal Combination ◽  
Optimal Input ◽  
Maximum Profit ◽  
Input Combination

The purpose of this study are to (1) determine the combination of inputs used in producing products such as beef sausages and veal sausage meatball; and (2) determine the optimal combination whether the product can provide the maximum profit. In order to determine the combination of inputs and maximum benefits can be used linear programming with graphical and simplex method. The valuation result shows that the optimal input combination would give a profit of Rp. 1.115 million per day.

Economics of coated paper production made from deinked pulp

TAPPI Journal ◽  
2013 ◽  
Vol 12 (4) ◽  
pp. 19-27
Author(s):  
PATRICK HUBER ◽  
LAURENT LYANNAZ ◽  
BRUNO CARRÉ
Keyword(s):  
Mechanical Properties ◽  
Optimization Methods ◽  
Coating Process ◽  
Coated Paper ◽  
Paper Production ◽  
Base Paper

The fraction of deinked pulp for coated paper production is continually increasing, with some mills using 100% deinked pulp for the base paper. The brightness of the coated paper made from deinked pulp may be reached through a combination of more or less extensive deinking, compensated by appropriate coating, to optimize costs overall. The authors proposed general optimization methods combined with Kubelka-Munk multilayer calculations to find the most economical combination of deinking and coating process that would produce a coated paper made from DIP, at a given target brightness, while maintaining mechanical properties.

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