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Relevant bibliographies by topics / Fletcher-Reeves method

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Academic literature on the topic 'Fletcher-Reeves method'

Author: Grafiati

Published: 7 June 2025

Last updated: 9 July 2025

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Journal articles on the topic "Fletcher-Reeves method"

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G. Vanaja. "Comparison Between Cauchy's Steepest Descent and Fletcher-Reeves Methods in Solving Unconstrained Neutrosophic Nonlinear Programming Problems." Communications on Applied Nonlinear Analysis 32, no. 2s (2024): 366–85. https://doi.org/10.52783/cana.v32.2411.

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The main objective of this study is to solve unconstrained single-valued Neutrosophic Nonlinear Programming Problems using Cauchy’s Steepest Descent Method(CSDM) and the Fletcher-Reeves Method(FRM). Our approach is based on new arithmetic operations and ranking on the parametric representations of Triangular Neutrosophic Numbers(TNN). We prove some important theorems for Cauchy’s Steepest Descent Method and the Fletcher-Reeves Method. Numerical examples are presented to illustrate the theory developed in this article. The outcomes of the proposed methods are compared.

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Dai, Y. "Convergence properties of the Fletcher-Reeves method." IMA Journal of Numerical Analysis 16, no. 2 (1996): 155–64. http://dx.doi.org/10.1093/imanum/16.2.155.

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Djordjevic, Snezana. "New hybrid conjugate gradient method as a convex combination of FR and PRP methods." Filomat 30, no. 11 (2016): 3083–100. http://dx.doi.org/10.2298/fil1611083d.

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We consider a newhybrid conjugate gradient algorithm,which is obtained fromthe algorithmof Fletcher-Reeves, and the algorithmof Polak-Ribi?re-Polyak. Numerical comparisons show that the present hybrid conjugate gradient algorithm often behaves better than some known algorithms.

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Anam, Syaiful, Mochamad Hakim Akbar Assidiq Maulana, Noor Hidayat, Indah Yanti, Zuraidah Fitriah, and Dwi Mifta Mahanani. "Predicting the Number of COVID-19 Sufferers in Malang City Using the Backpropagation Neural Network with the Fletcher–Reeves Method." Applied Computational Intelligence and Soft Computing 2021 (April 27, 2021): 1–9. http://dx.doi.org/10.1155/2021/6658552.

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COVID-19 is a type of an infectious disease that is caused by the new coronavirus. The spread of COVID-19 needs to be suppressed because COVID-19 can cause death, especially for sufferers with congenital diseases and a weak immune system. COVID-19 spreads through direct contact, wherein the infected individual spreads the COVID-19 virus through cough, sneeze, or close contacts. Predicting the number of COVID-19 sufferers becomes an important task in the effort to curb the spread of COVID-19. Artificial neural network (ANN) is the prediction method that delivers effective results in doing this job. Backpropagation, a type of ANN algorithm, offers predictive problem solving with good performance. However, its performance depends on the optimization method applied during the training process. In general, the optimization method in ANN is the gradient descent method, which is known to have a slow convergence rate. Meanwhile, the Fletcher–Reeves method has a faster convergence rate than the gradient descent method. Based on this hypothesis, this paper proposes a prediction model for the number of COVID-19 sufferers in Malang using the Backpropagation neural network with the Fletcher–Reeves method. The experimental results show that the Backpropagation neural network with the Fletcher–Reeves method has a better performance than the Backpropagation neural network with the gradient descent method. This is shown by the Means Square Error (MSE) resulting from the proposed method which is smaller than the MSE resulting from the Backpropagation neural network with the gradient descent method.

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KIRAN, Kadir. "Geometri Uydurma için En İyi Eşlenik Gradyan Yönteminin Tespit Edilmesi." Konya Journal of Engineering Sciences 10, no. 2 (2022): 366–75. http://dx.doi.org/10.36306/konjes.1003916.

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In this study, it is aimed to determine the optimal conjugate gradient (CG) method for the geometry fitting of 2D measured profiles. To this end, the three well-known CG methods such as the Fletcher-Reeves, Polak-Ribiere and Hestenes-Stiefel were employed. For testing those methods performances, the five primitive geometries accommodating circle, square, triangle, ellipse and rectangle were first built with a 3D printer, and then they were scanned with a coordinate measuring machine (CMM) to achieve their 2D profiles. The nonlinear least squares procedure was implemented to minimize the error between those measured data and modeled ones. An iterative line search was utilized for this task. The search direction was calculated using the above-mentioned CG methods. During the geometry fitting process, the number of function evaluations at each iteration were computed and the total number of function evaluations were set to be a performance measure of the CG method in question when it converged. By using these performance measures, the performance and data profiles were created to efficiently determine the optimal CG method. Based on performance profiles, it can be stated that the Fletcher-Reeves and Polak-Ribiere methods are the fastest ones on three test geometries out of five. In addition to that, all the CG methods were able to complete the geometry fitting of 80% of test geometries. On the other hand, by examining the data profiles, it was determined that the Polak-Ribiere and Hestenes-Stiefel methods achieve their maximum capabilities of the completing geometry fitting (i.e., 80%) with much lower number of function evaluations than the Fletcher-Reeves method. Besides, in most geometries, the Polak-Ribiere method outperformed the others, thereby it was determined to be the optimal one for the geometry fitting. As a conclusion, the reported results in this work might help the end-users who study on the CMM data processing to conduct an efficient geometry fitting.

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Setiawan, Dendy, and Solikhun Solikhun. "Machine Learning Algorithm for Determining the Best Performance in Predicting Turmeric Production in Indonesia." International Journal of Mechanical Computational and Manufacturing Research 11, no. 2 (2022): 50–59. http://dx.doi.org/10.35335/computational.v11i2.1.

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The herb that has many uses in everyday life is turmeric. Not only in Indonesia but in other countries also use turmeric for consumption. Therefore, by making predictions on the level of turmeric production in the country, so that the government or other parties can use this as a reference and reference to solve problems. The method we use is Resilient Backpropagation where this method is one of the methods that is often used to forecast data. By using turmeric plant production data in Indonesia from 2016-2021 taken on the website of the Indonesian Central Statistics Agency. According to the data to be tested a network architecture model is formed, namely 2-15-1, 2-20-1, 2-25- 1 and 2-30-1. From this model, the Fletcher-Reeves method is used. From the 4 models that have been trained and tested, a 2-15-1 model is obtained to be the best architectural model for each method. The accuracy level of the Fletcher-Reeves method with the 2-15-1 model has an MSE value of 0.002481597.

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Jabbar, Hawraz N., Yoksal A. Laylani, Issam A. R. Moghrabi, and Basim A. Hassan. "Development of a New Numerical Conjugate Gradient Technique for Image Processing." WSEAS TRANSACTIONS ON COMPUTER RESEARCH 12 (November 13, 2023): 123–31. http://dx.doi.org/10.37394/232018.2024.12.12.

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We present a new iterative conjugate gradient technique for image processing. The technique is based on a new derivation of the conjugacy coefficient and develops a variant of the classical Fletcher-Reeves conjugate gradient method. The derivation exploits a quadratic function model. The new method is intended to minimize the presence of noise by utilizing the adaptive median filter (AMF) to reduce salt-and-pepper noise, while the adaptive center-weighted median filter (ACWMF) is used to reduce random-valued noise. The theoretical convergence properties of the method are proven and then tested on a basic set of images using MATLAB. The results show that the proposed algorithm is more efficient than the classical Fletcher-Reeves (FR) method, as measured by the signal-to-noise ratio (PSNR). The number of iterations and the number of function evaluations are also lower for the proposed method. The favorable performance of the new algorithm provides promise for deriving similar techniques that enhance the speed and efficiency of image-processing libraries.

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Santos, Rodrigo de S., and Milton J. Porsani. "HYBRID INVERSION OF INTERVAL VELOCITIES IN MULTISCALE APPROACH." Revista Brasileira de Geofísica 35, no. 4 (2017): 237. http://dx.doi.org/10.22564/rbgf.v35i4.1962.

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ABSTRACT. The understanding of the interior of the planet by using the seismic method of reflection requires knowledge of the velocities with which the seismic waves propagate in the subsurface of the Earth. This work presents strategies to obtain the velocity intervals using RMS velocity inversion. Using a hybrid algorithm that combines the Very Fast Simulated Annealing (VFSA) global optimization method and the Fletcher-Reeves local search method, we have sought to reduce the dependence between the accuracy of the results and the model by which the optimization process begins. The main innovative contribution of this study was the development and presentation of the named inversion strategy of multiscale parameters. This technique allows the use of the VFSA method in inversion problems in which the number of variables is significantly large. The hybrid algorithm with multiscale approach was used to solve 1D and 2D problems, estimating models with high degrees of accuracy, which allowed to confirm the efficiency of the proposed method.Keywords: inversion, parameter multiscale, interval velocity, Very Fast Simulated Annealing, Fletcher-Reeves, hybrid.RESUMO. O entendimento do interior do planeta por meio do método sísmico de reflexão requer o conhecimento das velocidades com que as ondas sísmicas se propagam na subsuperfície da Terra. Este trabalho apresenta estratégias para a obtenção das velocidade intervalares por uso inversão de velocidades RMS. Utilizando um algoritmo híbrido, que combina o método de otimização global Very Fast Simulated Annealing (VFSA), e o método de busca local Fletcher-Reeves, buscou-se reduzir a dependência entre a acurácia dos resultados e o modelo pelo qual o processo de otimização se inicia. A principal contribuição inovadora deste estudo foi o desenvolvimento e apresentação da estratégia de inversão nomeada de multiescala de parâmetros. Esta técnica possibilita o uso do método VFSA em problemas de inversão em que o número de variáveis é significativamente grande. O algoritmo híbrido com abordagem multiescala foi usado para solucionar problemas 1D e 2D, estimando modelos com elevado grau de acurácia, o que permitiu confirmar a eficiência do método proposto.Palavras-chave: inversão, multiescala de parâmetros, velocidade intervalar, Very Fast Simulated Annealing, Fletcher-Reeves, híbrido.

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Al-Saidi, Amal. "Improved Fletcher-Reeves Methods Based on New Scaling Techniques." Sultan Qaboos University Journal for Science [SQUJS] 26, no. 2 (2021): 141–51. http://dx.doi.org/10.53539/squjs.vol26iss2pp141-151.

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This paper introduces a scaling parameter to the Fletcher-Reeves (FR) nonlinear conjugate gradient method. The main aim is to improve its theoretical and numerical properties when applied with inexact line searches to unconstrained optimization problems. We show that the sufficient descent and global convergence properties of Al-Baali for the FR method with a fairly accurate line search are maintained. We also consider the possibility of extending this result to less accurate line search for appropriate values of the scaling parameter. The reported numerical results show that several values for the proposed scaling parameter improve the performance of the FR method significantly.

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Dai, Yuhong. "Further insight into the convergence of the Fletcher-Reeves method." Science in China Series A: Mathematics 42, no. 9 (1999): 905–16. http://dx.doi.org/10.1007/bf02880382.

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Book chapters on the topic "Fletcher-Reeves method"

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Ram, Bhagwat, Shashi Kant Mishra, Kin Keung Lai, and Predrag Rajković. "Quantum Fletcher Reeves Conjugate Gradient Method." In Unconstrained Optimization and Quantum Calculus. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-2435-2_3.

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Pughat, Anuradha, Parul Tiwari, and Vidushi Sharma. "Optimal Power and Performance Using Fletcher–Reeves Method in Dynamic Voltage Scaling." In Advances in Intelligent Systems and Computing. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1819-1_10.

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"A Novel Method for Unconstrained Multivariate Optimization Based on Fletcher Reeves Theory." In International Conference on Computer and Computer Intelligence (ICCCI 2011). ASME Press, 2011. http://dx.doi.org/10.1115/1.859926.paper58.

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Conference papers on the topic "Fletcher-Reeves method"

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Meyer, Gerard G. L., and Louis J. Podrazik. "A Parallel Projected Fletcher-Reeves Method for Optimal Control." In 1992 American Control Conference. IEEE, 1992. http://dx.doi.org/10.23919/acc.1992.4792042.

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Can Li, Ling Fang, and Xiangzhao Cui. "A feasible fletcher-reeves method to linear equality constrained optimization problem." In 2010 International Conference on Apperceiving Computing and Intelligence Analysis (ICACIA). IEEE, 2010. http://dx.doi.org/10.1109/icacia.2010.5709844.

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Zhao, Liming, Keping Liu, Chunxu Li, Long Jin, and Zhongbo Sun. "Form-finding of Tensegrity Structures Utilizing a Nonlinear Fletcher-Reeves Conjugate Gradient Method." In 2021 IEEE International Conference on Real-time Computing and Robotics (RCAR). IEEE, 2021. http://dx.doi.org/10.1109/rcar52367.2021.9517591.

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Kannan, B. K., and Steven N. Kramer. "An Augmented Lagrange Multiplier Based Method for Mixed Integer Discrete Continuous Optimization and its Applications to Mechanical Design." In ASME 1993 Design Technical Conferences. American Society of Mechanical Engineers, 1993. http://dx.doi.org/10.1115/detc1993-0382.

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Abstract An algorithm for solving nonlinear optimization problems involving discrete, integer, zero-one and continuous variables is presented. The augmented Lagrange multiplier method combined with Powell’s method and Fletcher & Reeves Conjugate Gradient method are used to solve the optimization problem where penalties are imposed on the constraints for integer / discrete violations. The use of zero-one variables as a tool for conceptual design optimization is also described with an example. Several case studies have been presented to illustrate the practical use of this algorithm. The results obtained are compared with those obtained by the Branch and Bound algorithm. Also, a comparison is made between the use of Powell’s method (zeroth order) and the Conjugate Gradient method (first order) in the solution of these mixed variable optimization problems.

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