Network problems & algorythms

Special structure linear programming problems have received considerable attention during the last two decades and among them network problems are of particular importance and have found numerous applications in manage- ment science and technology. The mathematical models of the shortest route, maximal flow, and pure minimum cost flow problems are presented and various interrelationships among them are investigated. Finally three algorithms due to Dijkstra and Ford and Fulkerson which deal with the solution of the above three network problems are discussed

Some Recent Advances in Network Flows

The literature on network flow problems is extensive, and over the past 40 years researchers have made continuous improvements to algorithms for solving several classes of problems. However, the surge of activity on the algorithmic aspects of network flow problems over the past few years has been particularly striking. Several techniques have proven to be very successful in permitting researchers to make these recent contributions: (i) scaling of the problem data; (ii) improved analysis of algorithms, especially amortized average case performance and the use of potential functions; and (iii) enhanced data structures. In this survey, we illustrate some of these techniques and their usefulness in developing faster network flow algorithms. Our discussion focuses on the design of faster algorithms from the worst case perspective and we limit our discussion to the following fundamental problems: the shortest path problem, the maximum flow problem, and the minimum cost flow problem. We consider several representative algorithms from each problem class including the radix heap algorithm for the shortest path problem, preflow push algorithms for the maximum flow problem, and the pseudoflow push algorithms for the minimum cost flow problem

Ameliorative Minimum Cost Flow Algorithm for Phase Unwrapping

AbstractAmeliorative minimum cost flow algorithm is put forward in this paper Based on MCF,. As we all know, MCF is one phase unwrapping algorithm based on network flow and it is used widely because of many merits. However, efficiency of MCF is relative low and it demands upper computer capability during phase unwrapping. The ameliorative minimum cost flow algorithm is that interferograms are divided into skit sub-areas so that it can be unwrapped respectively, after this, interferograms are fused by supper wavelet based on contourlet transform. In the end, Minimum Cost Flow and its Ameliorative algorithm are validated by practical example, results indicate Ameliorative algorithm are effective phase unwrapping algorithms

Network flow algorithms and applications

This paper looks at several methods for solving network flow problems. The first chapter gives a brief background for linear programming (LP) problems. It includes basic definitions and theorems. The second chapter gives an overview of graph theory including definitions, theorems, and examples. Chapters 3-5 are the heart of this thesis. Chapter 3 includes algorithms and applications for maximum flow problems. It includes a look at a very important theorem. Maximum Flow/Minimum Cut Theorem. There is also a section on the Augmenting Path Algorithm. Chapter 4 Deals with shortest path problem. It includes Dijsksta\u27s Algorithm and the All-Pairs Labeling Algorithm. Chapter 5 includes information on algorithms and applications for the minimum cost flow(MCF)problem. The algorithms covered include the Cycle Canceling,Successive ShortestPath,and Primal-Dual Algorithms. Each of these chapters 3-5 contain definitions,theorems,and algorithms to solve network flow problems. Throughout the paper the computer program LINDO is used. It serves a couple of functions. First it is a way of checking each solution. The second use is to expose the reader to a very valuable tool in linear programming

Mathematical Models and Algorithms for Network Flow Problems Arising in Wireless Sensor Network Applications

We examine multiple variations on two classical network flow problems, the maximum flow and minimum-cost flow problems. These two problems are well-studied within the optimization community, and many models and algorithms have been presented for their solution. Due to the unique characteristics of the problems we consider, existing approaches cannot be directly applied. The problem variations we examine commonly arise in wireless sensor network (WSN) applications. A WSN consists of a set of sensors and collection sinks that gather and analyze environmental conditions. In addition to providing a taxonomy of relevant literature, we present mathematical programming models and algorithms for solving such problems. First, we consider a variation of the maximum flow problem having node-capacity restrictions. As an alternative to solving a single linear programming (LP) model, we present two alternative solution techniques. The first iteratively solves two smaller auxiliary LP models, and the second is a heuristic approach that avoids solving any LP. We also examine a variation of the maximum flow problem having semicontinuous restrictions that requires the flow, if positive, on any path to be greater than or equal to a minimum threshold. To avoid solving a mixed-integer programming (MIP) model, we present a branch-and-price algorithm that significantly improves the computational time required to solve the problem. Finally, we study two dynamic network flow problems that arise in wireless sensor networks under non-simultaneous flow assumptions. We first consider a dynamic maximum flow problem that requires an arc to transmit a minimum amount of flow each time it begins transmission. We present an MIP for solving this problem along with a heuristic algorithm for its solution. Additionally, we study a dynamic minimum-cost flow problem, in which an additional cost is incurred each time an arc begins transmission. In addition to an MIP, we present an exact algorithm that iteratively solves a relaxed version of the MIP until an optimal solution is found