List of Articles Gradient method

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  1 - A New Algorithm of the Variational Inequality Problems with Application on the Asymmetric Traffic Equilibrium Problem
  Morad Payvand Sadigheh Jahdi Hamid Reza maleki sarvestani
  In this paper, we introduce a double projection algorithm based on extragradient method for solving the variational inequality problems and we prove the convergence theorem of proposed algorithm. One of the parameters that determine the efficiency and accuracy of the pr More

  In this paper, we introduce a double projection algorithm based on extragradient method for solving the variational inequality problems and we prove the convergence theorem of proposed algorithm. One of the parameters that determine the efficiency and accuracy of the projection method is a properly selected step size. This selection is based on the contractive properties of operator which is projected on the feasible region. For example, if the Lipschitz constant is not known, we have trouble choosing step size of the algorithm. The proposed algorithm eliminates the need to know the Lipschitz constant and provides a method that facilitates step size selection.We formulate the asymmetric traffic equilibrium problem as a variational inequality on the path flows space. According to the decomposable structure of the feasible set of this model, we obtain the traffic network equilibrium state, by using the proposed algorithm. Finally, we present the numerical results of using this algorithm on the Sioux-Falls test traffic network. Manuscript profile
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  2 - A Recurrent Neural Network to Identify Efficient Decision Making Units in Data Envelopment Analysis
  A. Ghomashi G. R. Jahanshahloo F. Hosseinzadeh Lotfi
  In this paper we present a recurrent neural network model to recognize efficient Decision Making Units(DMUs) in Data Envelopment Analysis(DEA). The proposed neural network model is derived from an unconstrained minimization problem. In theoretical aspect, it is shown th More

  In this paper we present a recurrent neural network model to recognize efficient Decision Making Units(DMUs) in Data Envelopment Analysis(DEA). The proposed neural network model is derived from an unconstrained minimization problem. In theoretical aspect, it is shown that the proposed neural network is stable in the sense of lyapunov and globally convergent. The proposed model has a single-layer structure. Simulation shows that the proposed model is effective to identify efficient DMUs in DEA. Manuscript profile
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  3 - A conjugate gradient based method for Decision Neural Network training
  M. Nadershahi A. D. Safi Samghabadi R. Tavakkoli-Moghaddam
  Decision Neural Network is a new approach for solving multi-objective decision-making problems based on artificial neural networks. Using inaccurate evaluation data, network training has improved and the number of educational data sets has decreased. The available trai More

  Decision Neural Network is a new approach for solving multi-objective decision-making problems based on artificial neural networks. Using inaccurate evaluation data, network training has improved and the number of educational data sets has decreased. The available training method is based on the gradient decent method (BP). One of its limitations is related to its convergence speed. Therefore, decision makers can simply guess the necessary data. In this paper, for increasing the Decision Neural Network training efficiency, a conjugate gradient based method has developed for network training. The key point in decision neural network training is to keep the same structures and parameters of the two sub network (multilayer perceptron) through training process. The efficiency of the proposed method is evaluated by estimating linear and nonlinear utility function of multi-objective decision problems. The results of the proposed method are compared with previous existing method and show that in the proposed method, convergence is faster than previous methods and the results are more favorable. Manuscript profile
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  4 - Estimation of the Time-Dependent Heat Flux Using Temperature Distribution at a Point in a Three Layer System with None Homogeneous Boundary Conditions
  Mohammad Mohammadiun
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  5 - Deep Learning Application in Rainbow Options Pricing
  Ali Bolfake Seyed Nourollah Mousavi Sima Mashayekhi
  10.22034/amfa.2023.1970841.1812
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  6 - A Three-Term Extension of a Descent Conjugate Gradient Method
  Zohre Aminifard
  10.30495/fomj.2022.1953254.1063
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  7 - یک مدل شبکه عصبی بازگشتی برای حل مدل CCR در تحلیل پوششی داده ها
  معصومه عباسی عباس قماشی
  در این مقاله ما یک شبکه عصبی برای حل مدل CCR در تحلیل پوششی داده ها (DEA) معرفی می کنیم. مدل شبکه عصبی پیشنهادی از یک مسئله مینیمم سازی نامقید حاصل می شود. از دیدگاه تئوری، نشان داده می شود که شبکه عصبی پیشنهادی به مفهوم لیاپانف پایدار و همگرای عمومی به جواب بهینه مدل C More

  در این مقاله ما یک شبکه عصبی برای حل مدل CCR در تحلیل پوششی داده ها (DEA) معرفی می کنیم. مدل شبکه عصبی پیشنهادی از یک مسئله مینیمم سازی نامقید حاصل می شود. از دیدگاه تئوری، نشان داده می شود که شبکه عصبی پیشنهادی به مفهوم لیاپانف پایدار و همگرای عمومی به جواب بهینه مدل CCR می باشد. مدل پیشنهادی ساختار تک لایه دارد. با یک مثال عددی موثر بودن مدل پیشنهادی برای حل مدل CCR در DEA نشان داده می شود. Manuscript profile
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  8 - An Optimum Line Search for Unconstrained Non-Polynomial Test Functions ‎U‎sing Nonlinear Conjugate Gradient Methods
  Adam Ishaq Tolulope Latunde Folashade Jimoh
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