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A path following interior-point algorithm for semidefinite optimization problem based on new kernel function | ||
| Journal of Mathematical Modeling | ||
| مقاله 3، دوره 4، شماره 1، آبان 2016، صفحه 35-58 اصل مقاله (316.62 K) | ||
| نوع مقاله: Research Article | ||
| نویسندگان | ||
| El Amir Djeffal* ؛ Lakhdar Djeffal | ||
| Department of Mathematics, University of Batna 2, Batna, Algeria | ||
| چکیده | ||
| In this paper, we deal to obtain some new complexity results for solving semidefinite optimization (SDO) problem by interior-point methods (IPMs). We define a new proximity function for the SDO by a new kernel function. Furthermore we formulate an algorithm for a primal dual interior-point method (IPM) for the SDO by using the proximity function and give its complexity analysis, and then we show that the worst-case iteration bound for our IPM is $O(6(m+1)^{\frac{3m+4}{2(m+1)}}\Psi _{0}^{\frac{m+2}{2(m+1)}}\frac{1}{\theta }\log \frac{n\mu ^{0}}{\varepsilon })$, where $m>4$. | ||
| کلیدواژهها | ||
| quadratic programming؛ convex nonlinear programming؛ interior point methods | ||
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آمار تعداد مشاهده مقاله: 1,472 تعداد دریافت فایل اصل مقاله: 1,455 |
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