کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
482003 | 1446168 | 2008 | 15 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Approximate methods for convex minimization problems with series-parallel structure
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
علوم کامپیوتر (عمومی)
پیش نمایش صفحه اول مقاله

چکیده انگلیسی
Consider a problem of minimizing a separable, strictly convex, monotone and differentiable function on a convex polyhedron generated by a system of m linear inequalities. The problem has a series-parallel structure, with the variables divided serially into n disjoint subsets, whose elements are considered in parallel. This special structure is exploited in two algorithms proposed here for the approximate solution of the problem. The first algorithm solves at most min{m, ν â n + 1} subproblems; each subproblem has exactly one equality constraint and at most n variables. The second algorithm solves a dynamically generated sequence of subproblems; each subproblem has at most ν â n + 1 equality constraints, where ν is the total number of variables. To solve these subproblems both algorithms use the authors' Projected Newton Bracketing method for linearly constrained convex minimization, in conjunction with the steepest descent method. We report the results of numerical experiments for both algorithms.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: European Journal of Operational Research - Volume 189, Issue 3, 16 September 2008, Pages 841-855
Journal: European Journal of Operational Research - Volume 189, Issue 3, 16 September 2008, Pages 841-855
نویسندگان
Adi Ben-Israel, Genrikh Levin, Yuri Levin, Boris Rozin,