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 solve unconstrained optimization problem using a free line search steepest descent method. First, we propose a double parameter scaled quasi Newton formula for calculating an approximation of the Hessian matrix. The approximation obtained from this for More
In this paper, we solve unconstrained optimization problem using a free line search steepest descent method. First, we propose a double parameter scaled quasi Newton formula for calculating an approximation of the Hessian matrix. The approximation obtained from this formula is a positive definite matrix that is satisfied in the standard secant relation. We also show that the largest eigen value of this matrix is not greater than the number of variables of the problem. Then, using this double parameter scaled quasi Newton formula, an explicit formula for calculating the step length in the steepest descent method is presented and therefore, this method does not require the use of approximate methods for calculating step length. The numerical results obtained from the implementation of the algorithm in MATLAB software environment are presented for some optimization problems. These results show the efficiency of the proposed method in comparison with other existing methods.
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To improve the classic Hestense-Stiefel conjugate gradient method, Shengwei et al. proposed an efficient conjugate gradient method which possesses the sufficient descent property when the line search fulfills the strong Wolfe conditions (by restricting the line search p More
To improve the classic Hestense-Stiefel conjugate gradient method, Shengwei et al. proposed an efficient conjugate gradient method which possesses the sufficient descent property when the line search fulfills the strong Wolfe conditions (by restricting the line search parameters). Inspired by the scaled extension of the Hestense-Stiefel method which is recently presented by Dong et al., a scaled modification of the conjugate gradient method of Shengwei et al. is proposed which satisfies the sufficient descent condition independent of the line search technique as well as the convexity assumption of the objective function. Furthermore, the global convergence of the suggested method is discussed based on standard suppositions. In addition, a smooth approximation for the compressed sensing optimization problem is put forward. Numerical experiments are done on a set of classical problems of the CUTEr library as well as in solving compressed sensing problem. Results of the comparisons illustrate the superiority of the proposed approach.
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In this paper, we propose a modification to the use of the risk aversion coefficient in optimization models, based on research literature and mathematical methods. The modified risk aversion coefficient introduced in this paper can be applied in the maximization part of More
In this paper, we propose a modification to the use of the risk aversion coefficient in optimization models, based on research literature and mathematical methods. The modified risk aversion coefficient introduced in this paper can be applied in the maximization part of the model without any adverse effects. By doing so, it can improve the accuracy of meta-heuristic algorithms in finding optimal solutions. To test the efficacy of our proposed model, we applied it to 30 shares of the Tehran Stock Exchange, along with a zero-risk asset, taking into account some limitations in the market. We used a genetic meta-heuristic optimization method to solve the model, and to measure its efficiency, we compared the results of the optimization process with 2500 randomly generated portfolios that were within the problem's constraints. Our results show that our model outperforms the random portfolios in terms of both risk factors and return. In conclusion, our proposed modification to the risk aversion coefficient can improve the accuracy of optimization models, and our results demonstrate its effectiveness in generating optimal portfolios in the market.
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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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