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Solving fractional packing problems in Oast(1/ε) iterations

机译:解决Past(1 /ε)迭代中的分数堆积问题

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We adapt a method proposed by Nesterov [16] to design an algorithm that computes ε-optimal solutions to fractional packing problems by solving O*-1Kn) separable convex quadratic programs, where K is the maximum number of non-zeros per row and n is the number of variables. We also show that the quadratic program can be approximated to any degree of accuracy by an appropriately defined piecewise-linear program. For the special case of the maximum concurrent flow problem on a graph G =(V,E) with rational capacities and demands we obtain an algorithm that computes an Ε-optimal flow by solving O*(ε-1 K3/2|E| √|V| (log 1/ε+ LU + LD)) shortest path problems, where K is the number of commodities, and LU, LD are, respectively, the number of bits needed to store the capacities and demands. We also show that the complexity of computing a maximum multicommodity flow is O*(1/εlog2(1/ε)). In contrast, previous algorithms required Ω(ε-2) iterations.
机译:我们采用Nesterov [16]提出的方法来设计一种算法,该算法通过求解 O * (ε -1 < / SUP>√ Kn )可分离的凸二次程序,其中 K 是每行非零的最大数量,而 n 是变量。我们还表明,通过适当定义的分段线性程序,二次程序可以近似到任何精度。对于具有合理容量和需求的图 G =( V,E )上的最大并发流问题的特殊情况,我们获得了一种计算ε最优流的算法通过求解 O *(ε -1 K 3/2 | E | √| V |(log 1 /ε+ L U + L D ))最短路径问题,其中 K 是商品数量,而 L U ,L D 是,存储容量和需求所需的位数。我们还表明,计算最大多商品流的复杂度为 O * (1 /εlog2(1 /ε))。相反,以前的算法需要Ω(ε -2 )迭代。

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