Sciweavers

EOR
2008

Approximate methods for convex minimization problems with series-parallel structure

13 years 3 months ago
Approximate methods for convex minimization problems with series-parallel structure
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, - 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 -n+1 equality constraints, where 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 ...
Adi Ben-Israel, Genrikh Levin, Yuri Levin, Boris R
Added 10 Dec 2010
Updated 10 Dec 2010
Type Journal
Year 2008
Where EOR
Authors Adi Ben-Israel, Genrikh Levin, Yuri Levin, Boris Rozin
Comments (0)