Displaying similar documents to “Convergence of Prox-Regularization Methods for Generalized Fractional Programming”

Fractional Korovkin Theory Based on Statistical Convergence

Anastassiou, George A., Duman, Oktay (2009)

Serdica Mathematical Journal

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2000 Mathematics Subject Classification: 41A25, 41A36, 40G15. In this paper, we obtain some statistical Korovkin-type approximation theorems including fractional derivatives of functions. We also show that our new results are more applicable than the classical ones.

Fractional Trigonometric Korovkin Theory in Statistical Sense

Anastassiou, George A., Duman, Oktay (2010)

Serdica Mathematical Journal

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2000 Mathematics Subject Classification: 41A25, 41A36. In the present paper, we improve the classical trigonometric Korovkin theory by using the concept of statistical convergence from the summability theory and also by considering the fractional derivatives of functions. We also show that our new results are more applicable than the classical ones.

A new branch and bound algorithm for minimax ratios problems

Yingfeng Zhao, Sanyang Liu, Hongwei Jiao (2017)

Open Mathematics

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This study presents an efficient branch and bound algorithm for globally solving the minimax fractional programming problem (MFP). By introducing an auxiliary variable, an equivalent problem is firstly constructed and the convex relaxation programming problem is then established by utilizing convexity and concavity of functions in the problem. Other than usual branch and bound algorithm, an adapted partition skill and a practical reduction technique performed only in an unidimensional...

A new application of the homotopy analysis method in solving the fractional Volterra's population system

Mehdi Ghasemi, Mojtaba Fardi, Reza Khoshsiar Ghaziani (2014)

Applications of Mathematics

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This paper considers a Volterra's population system of fractional order and describes a bi-parametric homotopy analysis method for solving this system. The homotopy method offers a possibility to increase the convergence region of the series solution. Two examples are presented to illustrate the convergence and accuracy of the method to the solution. Further, we define the averaged residual error to show that the obtained results have reasonable accuracy.