Nonlinear programming problems belong to the realm of commonly used optimization problems. In most cases, the objective function of such problems is non-convex. However, to guarantee global convergence in the algorithms proposed based on Newton's method to solve these p More
Nonlinear programming problems belong to the realm of commonly used optimization problems. In most cases, the objective function of such problems is non-convex. However, to guarantee global convergence in the algorithms proposed based on Newton's method to solve these problems, a convexity condition is generally required. Meanwhile, the quasi-Newton techniques are more popular because they use an approximation of the Hessian matrix or its inverse. However, in these algorithms, only gradient information is used to approximate this matrix. One of the most applicable quasi-Newton algorithms in solving nonlinear programming problems is the BFGS method. This paper presents a new idea for a linear search in the BFGS method. It proves that using this technique will lead to global convergence for general problems without the need for any additional conditions. Finally, the performance of the proposed algorithm is evaluated numerically.
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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.
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The changes of expenditure pattern have caused change of land application (from the traditional form to the modern form) and many problems. Consequently this study has been conducted in Sa'aadat Abaad quarter in district 2 of Tehran city hall in order to investigate eff More
The changes of expenditure pattern have caused change of land application (from the traditional form to the modern form) and many problems. Consequently this study has been conducted in Sa'aadat Abaad quarter in district 2 of Tehran city hall in order to investigate effective factors in the changes of expenditure pattern. The volume of statistical community is 30 thousand people among whom 380 improbable sample have been chosen by Cochran formula. The study is conducted by descriptive-analytic research method and has a practical-applicable goal. The data and information have been gathered in both ways of desk study and field study. Data analyses have been done by inferential test, Pearson and Regression inferential, in addition to quantitative and descriptive subjects. The research results indicate that the first prior application ( retailers, bakeries, supermarkets, and ...) have owned the least share of 9.16 % in 1393 while the fourth prior application and unnecessary applications ( pizza places, coffee shops,...) have enhanced their share by 48% from 1385 and the previous years. The globalization variables have affected the expenditure pattern as the highest correlation index of traveling abroad and using new applications of 0,527 can be observed. It means that as much as the globalization penetrates the society's culture, their expenditure pattern will change. In addition to the globalization variables, aging, education, economical condition of the residents and the income enhancement of these applications are also effective which will pave the way for using modern applications.
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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.
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انواع زیادی از الگوریتم های گرادیان مزدوج وجود دارد. به منظور بهره گیری از ویژگی های جذاب روش های لیو و استوری (LS) و سکانت مزدوج (CD) و روش گرادیان مزدوج ، ما ترکیبی از این روش ها که در آن پارامتر به عنوان ترکیبی محدب محاسبه می شود و به ترتیب پارامتر گرادیان (برو More
انواع زیادی از الگوریتم های گرادیان مزدوج وجود دارد. به منظور بهره گیری از ویژگی های جذاب روش های لیو و استوری (LS) و سکانت مزدوج (CD) و روش گرادیان مزدوج ، ما ترکیبی از این روش ها که در آن پارامتر به عنوان ترکیبی محدب محاسبه می شود و به ترتیب پارامتر گرادیان (بروزرسانی) از معادله Secant بدست آمده است را پیشنهاد می کنیم. الگوریتم جهت نزول را ایجاد می کند و هنگامی که فشردگی تگرار می شود جهت شرایط مناسب نزول را برآورده می کند. گزارش نتایج عددی نشان دهنده کارایی روش ما است.طرح محاسباتی ترکیبی عملکرد بهتری دارد یا قابل مقایسه با الگوریتم گرادیان مزدوج شناخته شده است. همچنین نشان می دهد که روش ما در سطح جهانی با استفاده از شرایط ولف قوی همگراست.
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