AlgorithmAn M-convex function is a nonlinear discrete function defined on integer points introduced by Murota in 1996, and the M-convex submodular flow problem is one of the most general frameworks of efficiently solvable combinatorial optimization problems. It includes the minimum cost flow and the...
A Horseshoe for Multidimensional Scaling(多维缩放的马蹄铁) 热度: A Capacity Scaling Algorithm for the Constrained Maximum Flow… 热度: Downscaling or Decision- Scaling降尺度或决策的缩放 热度: ScalingoftheCapacityRegionof WirelessNetworks PiyushGupta ...
The case that all adversaries have limited eavesdropping capabilities is first considered, and it is shown that the corresponding zero sum game is indeed a bilinear game, and its Nash equilibrium solution can be readily approximated using a saddle point Frank Wolfe(SP-FW)algorithm. Then the ...
The Application Auto Scaling target tracking algorithm seeks to keep the target utilization at or near your chosen value over the long term. Sudden, short-duration spikes of activity are accommodated by the table's built-in burst capacity. For more information, see Burst capacity. To enable ...
We study the asymptotic throughput capacity and delay in mobile ad hoc networks following the 2-hop relaying algorithm proposed by Grossglauser and Tse (2001). We assume the nodes to be uniformly distributed on a sphere, and consider two canonical mobility models: the Brownian mobility model (...
a first bin having a fixed capacity for handing the first load requirement of the first tenant. In response to the first load requirement of the first tenant exceeding a first threshold of the fixed capacity of the first bin, packing a second bin allocated to handle a second load requirement...
Orlin, A capacity scaling algorithm for the constrained maximum flow problem, Networks 25 (2) (1995) 89-98.Ravindra K. Ahuja, James B. Orlin. A Capacity Scaling Algorithm for the Con- strained Maximum Flow Problem, Networks 25 (1995) pp. 89-98....
algorithmAn M-convex function is a nonlinear discrete function defined on integer points introduced by Murota in 1996, and the M-convex submodular flow problem is one of the most general frameworks of efficiently solvable combinatorial optimization problems. It includes the minimum cost flow and the...
A Faster Capacity Scaling Algorithm for Minimum Cost Submodular Flow - Fleischer, Iwata, et al. - 2002L.K. Fleischer, S. Iwata, and S.T. McCormick, "A Faster Capacity Scaling Algorithm for Minimum Cost Submodular Flow," Math. Programming, vol. 92, pp. 119-139, 2002....
The algorithm extends the capacity scaling approach for the submodular flow problem by Fleischer, Iwata and McCormick (2002) with the aid of a novel technique of changing the potential by solving maximum submodular flow problems.doi:10.1007/s10107-004-0562-3Satoru Iwata...