Linear Programming { Randomization
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چکیده
Frameworks ? Bernd Gartner and Emo Welzl Institut f ur Informatik, Freie Universitat Berlin, Takustr. 9, D-14195 Berlin, Germany fgaertner,[email protected] Abstract. Recent years have brought some progress in the knowledge of the complexity of linear programming in the unit cost model, and the best result known at this point is a randomized `combinatorial' algorithm which solves a linear program over d variables and n constraints with expected Recent years have brought some progress in the knowledge of the complexity of linear programming in the unit cost model, and the best result known at this point is a randomized `combinatorial' algorithm which solves a linear program over d variables and n constraints with expected O(d 2 n + e O( p d log d) ) arithmetic operations. The bound relies on two algorithms by Clarkson, and the subexponential algorithms due to Kalai, and to Matou sek, Sharir & Welzl. We review some of the recent algorithms with their analyses. We also present abstract frameworks like LP-type problems and abstract optimization problems (due to Gartner) which allow the application of these algorithms to a number of non-linear optimization problems (like polytope distance and smallest enclosing ball of points).
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تاریخ انتشار 1996