کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
4646349 | 1342104 | 2007 | 17 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Enlarging neighborhoods of interior-point algorithms for linear programming via least values of proximity measure functions
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موضوعات مرتبط
مهندسی و علوم پایه
ریاضیات
ریاضیات محاسباتی
پیش نمایش صفحه اول مقاله
![عکس صفحه اول مقاله: Enlarging neighborhoods of interior-point algorithms for linear programming via least values of proximity measure functions Enlarging neighborhoods of interior-point algorithms for linear programming via least values of proximity measure functions](/preview/png/4646349.png)
چکیده انگلیسی
It is well known that a wide-neighborhood interior-point algorithm for linear programming performs much better in implementation than its small-neighborhood counterparts. In this paper, we provide a unified way to enlarge the neighborhoods of predictor–corrector interior-point algorithms for linear programming. We prove that our methods not only enlarge the neighborhoods but also retain the so-far best known iteration complexity and superlinear (or quadratic) convergence of the original interior-point algorithms. The idea of our methods is to use the global minimizers of proximity measure functions.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Applied Numerical Mathematics - Volume 57, Issue 9, September 2007, Pages 1033-1049
Journal: Applied Numerical Mathematics - Volume 57, Issue 9, September 2007, Pages 1033-1049