scholarly journals Efficient Network Reconfiguration Using Minimum Cost Maximum Flow-Based Branch Exchanges and Random Walks-Based Loss Estimations

2011 ◽  
Vol 26 (1) ◽  
pp. 30-37 ◽  
Author(s):  
Cristinel Ababei ◽  
Rajesh Kavasseri
Keyword(s):  
Random Walks ◽  
Minimum Cost ◽  
Maximum Flow ◽  
Network Reconfiguration ◽  
Loss Estimations

scholarly journals Adaptation of Random Binomial Graphs for Testing Network Flow Problems Algorithms

Mathematics ◽  
2021 ◽  
Vol 9 (15) ◽  
pp. 1716
Author(s):  
Adrian Marius Deaconu ◽  
Delia Spridon
Keyword(s):  
Network Flow ◽  
Large Scale ◽  
Random Network ◽  
Minimum Cost ◽  
Maximum Flow ◽  
Network Flow Problems ◽  
Commodity Flow ◽  
Vast Number ◽  
Flow Problems ◽  
Execution Speed

Algorithms for network flow problems, such as maximum flow, minimum cost flow, and multi-commodity flow problems, are continuously developed and improved, and so, random network generators become indispensable to simulate the functionality and to test the correctness and the execution speed of these algorithms. For this purpose, in this paper, the well-known Erdős–Rényi model is adapted to generate random flow (transportation) networks. The developed algorithm is fast and based on the natural property of the flow that can be decomposed into directed elementary s-t paths and cycles. So, the proposed algorithm can be used to quickly build a vast number of networks as well as large-scale networks especially designed for s-t flows.

Target Controllability in Multilayer Networks via Minimum-Cost Maximum-Flow Method

2020 ◽  
pp. 1-14
Author(s):  
Jie Ding ◽  
Changyun Wen ◽  
Guoqi Li ◽  
Pengfei Tu ◽  
Dongxu Ji ◽  
...  
Keyword(s):  
Minimum Cost ◽  
Maximum Flow ◽  
Multilayer Networks ◽  
Flow Method

Bus Optimization for Low Power in High-Level Synthesis

2003 ◽  
Vol 12 (01) ◽  
pp. 1-17
Author(s):  
Sungpack Hong ◽  
Taewhan Kim
Keyword(s):  
Optimal Solution ◽  
Minimum Cost ◽  
Maximum Flow ◽  
High Level Synthesis ◽  
Benchmark Problems ◽  
Timing Constraints ◽  
Power Efficient ◽  
High Level ◽  
The Impact ◽  
Operation Scheduling

Sub-micron feature sizes have resulted in a considerable portion of power to be dissipated on the buses, causing an increased attention on savings for power at the behavioral level and the RT level of design. This paper addresses the problem of minimizing power dissipated in the switching of the buses in the high-level synthesis of data-dominated behavioral descriptions. Unlike the previous approaches in which the minimization of the power consumed in buses has not been considered until operation scheduling is completed, our approach integrates the bus binding problem into scheduling to exploit the impact of scheduling on the reduction of power dissipated on the buses more fully and effectively. We accomplish this by formulating the problem into a flow problem in a network, and devising an efficient algorithm which iteratively finds the maximum flow of minimum cost solutions in the network. Experimental results on a number of benchmark problems show that given resource and global timing constraints our designs are 19.8% power-efficient over the designs produced by a random-move based solution, and 15.5% power-efficient over the designs by a clock-step based optimal solution.

TRACTABLE AND INTRACTABLE VARIATIONS OF UNORDERED TREE EDIT DISTANCE

2014 ◽  
Vol 25 (03) ◽  
pp. 307-329 ◽  
Author(s):  
YOSHIYUKI YAMAMOTO ◽  
KOUICHI HIRATA ◽  
TETSUJI KUBOYAMA
Keyword(s):  
Edit Distance ◽  
Linear Time ◽  
Minimum Degree ◽  
Minimum Cost ◽  
Maximum Flow ◽  
Time Algorithm ◽  
Tree Edit Distance ◽  
Bottom Up ◽  
Bipartite Matching ◽  
Unordered Tree

In this paper, we investigate the problem of computing structural sensitive variations of an unordered tree edit distance. First, we focus on the variations tractable by the algorithms including the submodule of a network algorithm, either the minimum cost maximum flow algorithm or the maximum weighted bipartite matching algorithm. Then, we show that both network algorithms are replaceable, and hence the time complexity of computing these variations can be reduced to O(nmd) time, where n is the number of nodes in a tree, m is the number of nodes in another tree and d is the minimum degree of given two trees. Next, we show that the problem of computing the bottom-up distance is MAX SNP-hard. Note that the well-known linear-time algorithm for the bottom-up distance designed by Valiente (2001) computes just a bottom-up indel (insertion-deletion) distance allowing no substitutions.

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