scholarly journals Power packet transferability via symbol propagation matrix

2018 ◽  
Vol 474 (2213) ◽  
pp. 20170552 ◽  
Author(s):  
Shinya Nawata ◽  
Atsuto Maki ◽  
Takashi Hikihara
Keyword(s):  
Network Flow ◽  
Flow Problem ◽  
Temporal Structure ◽  
Minimum Cost ◽  
Finite Duration ◽  
Power Pulse ◽  
Minimum Cost Flow Problem ◽  
Propagation Matrix ◽  
Spatio Temporal ◽  
Symbol Sequences

A power packet is a unit of electric power composed of a power pulse and an information tag. In Shannon’s information theory, messages are represented by symbol sequences in a digitized manner. Referring to this formulation, we define symbols in power packetization as a minimum unit of power transferred by a tagged pulse. Here, power is digitized and quantized. In this paper, we consider packetized power in networks for a finite duration, giving symbols and their energies to the networks. A network structure is defined using a graph whose nodes represent routers, sources and destinations. First, we introduce the concept of a symbol propagation matrix (SPM) in which symbols are transferred at links during unit times. Packetized power is described as a network flow in a spatio-temporal structure. Then, we study the problem of selecting an SPM in terms of transferability, that is, the possibility to represent given energies at sources and destinations during the finite duration. To select an SPM, we consider a network flow problem of packetized power. The problem is formulated as an M-convex submodular flow problem which is a solvable generalization of the minimum cost flow problem. Finally, through examples, we verify that this formulation provides reasonable packetized power.

scholarly journals Complexity Analysis of New Task Allocation Problem Using Network Flow Method on Multicore Clusters

2014 ◽  
Vol 2014 ◽  
pp. 1-7
Author(s):  
Jixiang Yang
Keyword(s):  
Task Allocation ◽  
Network Flow ◽  
Flow Problem ◽  
Minimum Cost ◽  
Communication Cost ◽  
Minimum Cost Flow ◽  
Flow Method ◽  
Minimum Cost Flow Problem ◽  
Cost Flow ◽  
Multicore Clusters

The task allocation problem (TAP) generally aims to minimize total execution cost and internode communication cost in traditional parallel computing systems. New TAP (NTAP) considering additive intranode communication cost in emerging multicore cluster systems is investigated in this paper. We analyze the complexity of NTAP with network flow method and conclude that the intranode communication cost is a key to the complexity of NTAP, and prove that (1) the NTAP can be cast as a generalized linear network minimum cost flow problem and can be solved inO(m2n4)time if the intranode communication cost equals the internode communication cost, and (2) the NTAP can be cast as a generalized convex cost network minimum cost flow problem and can be solved in polynomial time if the intranode communication cost is more than the internode communication cost. More in particular, the uniform cost NTAP can be cast as a convex cost flow problem and can be solved inO(m2n2log(m+n))time. Furthermore, solutions to the NTAP are also discussed. Our work extends currently known theoretical results and the theorems and conclusions presented in this paper can provide theoretical basis for task allocating strategies on multicore clusters.

Networks Flow Applications

2013 ◽  
pp. 246-256
Author(s):  
Alireza Boloori ◽  
Monirehalsadat Mahmoudi
Keyword(s):  
Network Flow ◽  
Large Scale ◽  
Flow Problem ◽  
Minimum Cost ◽  
Maximum Flow ◽  
Shortest Path Problems ◽  
Flow Problems ◽  
Large Scale Integration ◽  
Minimum Cost Flow Problem ◽  
Scale Integration

In this chapter, some applications of network flow problems are addressed based on each type of problem being discussed. For example, in the case of shortest path problems, their concept in facility layout, facility location, robotics, transportation, and very large-scale integration areas are pointed out in the first section. Furthermore, the second section deals with the implementation of the maximum flow problem in image segmentation, transportation, web communities, and wireless networks and telecommunication areas. Moreover, in the third section, the minimum-cost flow problem is discussed in fleeting and routing problems, petroleum, and scheduling areas. Meanwhile, a brief explanation about each application as well as some corresponding literature and research papers are presented in each section. In addition, based on available literature in each of these areas, some research gaps are identified, and future trends as well as chapter’s conclusion are pointed out in the fourth section.

Minimum cost flow problem with conflicts

Networks ◽  
2021 ◽  
Author(s):  
Zeynep Şuvak ◽  
İ. Kuban Altınel ◽  
Necati Aras
Keyword(s):  
Flow Problem ◽  
Minimum Cost ◽  
Minimum Cost Flow ◽  
Minimum Cost Flow Problem ◽  
Cost Flow

scholarly journals Minimum cost network flows: Problems, algorithms, and software

2013 ◽  
Vol 23 (1) ◽  
pp. 3-17 ◽  
Author(s):  
Angelo Sifaleras
Keyword(s):  
Network Flow ◽  
Flow Problem ◽  
Network Flows ◽  
Minimum Cost ◽  
Network Flow Problem ◽  
Software Packages ◽  
Wide Range ◽  
Solution Methods ◽  
Minimum Cost Network Flow ◽  
Algorithmic Approaches

We present a wide range of problems concerning minimum cost network flows, and give an overview of the classic linear single-commodity Minimum Cost Network Flow Problem (MCNFP) and some other closely related problems, either tractable or intractable. We also discuss state-of-the-art algorithmic approaches and recent advances in the solution methods for the MCNFP. Finally, optimization software packages for the MCNFP are presented.

Minimum Cost Flow Problem

2008 ◽  
pp. 2095-2108
Author(s):  
Ravindra K. Ahuja ◽  
Thomas L. Magnanti ◽  
James B. Orlin
Keyword(s):  
Flow Problem ◽  
Minimum Cost ◽  
Minimum Cost Flow ◽  
Minimum Cost Flow Problem ◽  
Cost Flow

The Minimum Cost Flow Problem of Uncertain Random Network

2018 ◽  
Vol 35 (03) ◽  
pp. 1850016
Author(s):  
Soheila Abdi ◽  
Fahimeh Baroughi ◽  
Behrooz Alizadeh
Keyword(s):  
Flow Model ◽  
Flow Problem ◽  
Random Network ◽  
Minimum Cost ◽  
Random Variable ◽  
Minimum Cost Flow ◽  
Feasible Set ◽  
Uncertain Random Network ◽  
Minimum Cost Flow Problem ◽  
Cost Flow

The aim of this paper is to present a novel method for solving the minimum cost flow problem on networks with uncertain-random capacities and costs. The objective function of this problem is an uncertain random variable and the constraints of the problem do not make a deterministic feasible set. Under the framework of uncertain random programming, a corresponding [Formula: see text]-minimum cost flow model with a prespecified confidence level [Formula: see text], is formulated and its main properties are analyzed. It is proven that there exists an equivalence relationship between this model and the classical deterministic minimum cost flow model. Then an algorithm is proposed to find the maximum amount of [Formula: see text] such that for it, the feasible set of [Formula: see text]-minimum cost flow model is nonempty. Finally, a numerical example is presented to illustrate the efficiency of our proposed method.

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