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The algebraic path problem is a generalization of the shortest path problem in graphs. Various instances of this abstract problem have appeared in the literature, and similar solutions have been independently discovered and rediscovered. The repeated appearance of a problem is evidence of its relevance. This book aims to help current and future researchers add this powerful tool to their arsenal, so that they can easily identify and use it in their own work. Path problems in networks can be conceptually divided into two parts: A distillation of the extensive theory behind the algebraic path problem, and an exposition of a broad range of applications. First of all, the shortest path problem is presented so as to fix terminology and concepts: existence and uniqueness of solutions, robustness to parameter changes, and centralized and distributed computation algorithms. Then, these concepts are generalized to the algebraic context of semirings. Methods for creating new semirings, useful for modeling new problems, are provided. A large part of the book is then devoted to numerous applications of the algebraic path problem, ranging from mobile network routing to BGP routing to social networks. These applications show what kind of problems can be modeled as algebraic path problems; they also serve as examples on how to go about modeling new problems. This monograph will be useful to network researchers, engineers, and graduate students. It can be used either as an introduction to the topic, or as a quick reference to the theoretical facts, algorithms, and application examples. The theoretical background assumed for the reader is that of a graduate or advanced undergraduate student in computer science or engineering. Some familiarity with algebra and algorithms is helpful, but not necessary. Algebra, in particular, is used as a convenient and concise language to describe problems that are essentially combinatorial. Table of Contents: Classical Shortest Path / The Algebraic Path Problem / Properties and Computation of Solutions / Applications / Related Areas / List of Semirings and Applications
Before the appearance of broadband links and wireless systems, networks have been used to connect people in new ways. Now, the modern world is connected through large-scale, computational networked systems such as the Internet. Because of the ever-advancing technology of networking, efficient algorithms have become increasingly necessary to solve some of the problems developing in this area. "Mathematical Aspects of Network Routing Optimization" focuses on computational issues arising from the process of optimizing network routes, such as quality of the resulting links and their reliability. Algorithms are a cornerstone for the understanding of the protocols underlying multicast routing. The main objective in the text is to derive efficient algorithms, with or without guarantee of approximation. Notes have been provided for basic topics such as graph theory and linear programming to assist those who are not fully acquainted with the mathematical topics presented throughout the book. "Mathematical Aspects of Network Routing Optimization" provides a thorough introduction to the subject of algorithms for network routing, and focuses especially on multicast and wireless ad hoc systems. This book is designed for graduate students, researchers, and professionals interested in understanding the algorithmic and mathematical ideas behind routing in computer networks. It is suitable for advanced undergraduate students, graduate students, and researchers in the area of network algorithms.
The proposed RSP model and solution algorithm are extended to incorporate travel time temporal correlations in those stochastic time-dependent (STD) networks where link travel time distributions vary by time intervals throughout the day. In the STD networks, travellers' experienced link travel time variation depends on the time instance vehicles entering the link; and the link travel time distribution is typically assumed to be fixed when these vehicles travelling on that link. This assumption, however, may violate the first in first out (FIFO) property, since traffic conditions cannot be updated when vehicles travelling on the link. To address this non-FIFO problem, a stochastic travel speed model (S-TSM) that can update travellers' experienced travel speeds during different time intervals on the link is proposed in this research. The proposed S-TSM can ensure the FIFO property of link travel times, so that the efficient multi-criteria A* algorithm can be adopted to solve the RSP problems in STD networks. Based on the proposed multi-criteria A* algorithm, a real-world ATIS-based routing system is developed to aid road users of Hong Kong making route choice decisions in road networks with travel time spatiotemporal correlations. Secondly, the proposed RSP model is incorporated in reliability-based user equilibrium (RUE) problems for traffic assignment. In this research, an effective reliable shortest path algorithm is developed to determine RSP for all user classes in one search process so as to avoid the repeated path searching for each user class. The proposed reliable shortest path algorithm is then, further incorporated into a path-based RUE assignment algorithm using a column generation method. The proposed RUE assignment algorithm does not require path enumeration and can achieve highly accurate RUE results within reasonable computational time. A numerical example demonstrates that the proposed RUE assignment algorithm is capable for solving relevant problems in road networks with demand and / or supply uncertainties. Thirdly, the proposed RSP and RUE algorithms are applied to identify critical links in large-scale road networks. The traditional method, to identify critical links, is to use a full scan approach to assess all possible link closure scenarios by means of traffic assignment methods. This full scan approach is not viable for identifying critical links in large-scale road networks, because of the large number of link closure scenarios and computational intensity of traffic assignment methods in these large-scale networks. An impact area vulnerability analysis approach is proposed in this research to evaluate the consequences of a link failure within a local impact area, rather than the entire network. Such vulnerability analysis approach reduces the problem size of the critical link identification so as to reduce the computational burden involved. Case studies on large-scale real-world networks are presented to illustrate the proposed impact area vulnerability approach and investigate the effects of stochastic demand and heterogeneous travellers' risk-taking behaviour.
Fundamentals of Brain Network Analysis is a comprehensive and accessible introduction to methods for unraveling the extraordinary complexity of neuronal connectivity. From the perspective of graph theory and network science, this book introduces, motivates and explains techniques for modeling brain networks as graphs of nodes connected by edges, and covers a diverse array of measures for quantifying their topological and spatial organization. It builds intuition for key concepts and methods by illustrating how they can be practically applied in diverse areas of neuroscience, ranging from the analysis of synaptic networks in the nematode worm to the characterization of large-scale human brain networks constructed with magnetic resonance imaging. This text is ideally suited to neuroscientists wanting to develop expertise in the rapidly developing field of neural connectomics, and to physical and computational scientists wanting to understand how these quantitative methods can be used to understand brain organization. Winner of the 2017 PROSE Award in Biomedicine & Neuroscience and the 2017 British Medical Association (BMA) Award in Neurology Extensively illustrated throughout by graphical representations of key mathematical concepts and their practical applications to analyses of nervous systems Comprehensively covers graph theoretical analyses of structural and functional brain networks, from microscopic to macroscopic scales, using examples based on a wide variety of experimental methods in neuroscience Designed to inform and empower scientists at all levels of experience, and from any specialist background, wanting to use modern methods of network science to understand the organization of the brain
In the past few decades, there has been a large amount of work on algorithms for linear network flow problems, special classes of network problems such as assignment problems (linear and quadratic), Steiner tree problem, topology network design and nonconvex cost network flow problems.Network optimization problems find numerous applications in transportation, in communication network design, in production and inventory planning, in facilities location and allocation, and in VLSI design.The purpose of this book is to cover a spectrum of recent developments in network optimization problems, from linear networks to general nonconvex network flow problems./a
Survivable Networks: Algorithms for Diverse Routing provides algorithms for diverse routing to enhance the survivability of a network. It considers the common mesh-type network and describes in detail the construction of physically disjoint paths algorithms for diverse routing. The algorithms are developed in a systematic manner, starting with shortest path algorithms appropriate for disjoint paths construction. Key features of the algorithms are optimality and simplicity. Although the algorithms have been developed for survivability of communication networks, they are in a generic form, and thus applicable in other scientific and technical disciplines to problems that can be modeled as a network. A notable highlight of this book is the consideration of real-life telecommunication networks in detail. Such networks are described not only by nodes and links, but also by the actual physical elements, called span nodes and spans. The sharing of spans (the actual physical links) by the network (logical) links complicates the network, requiring new algorithms. This book is the first one to provide algorithms for such networks. Survivable Networks: Algorithms for Diverse Routing is a comprehensive work on physically disjoint paths algorithms. It is an invaluable resource and reference for practicing network designers and planners, researchers, professionals, instructors, students, and others working in computer networking, telecommunications, and related fields.
Column Generation is an insightful overview of the state of the art in integer programming column generation and its many applications. The volume begins with "A Primer in Column Generation" which outlines the theory and ideas necessary to solve large-scale practical problems, illustrated with a variety of examples. Other chapters follow this introduction on "Shortest Path Problems with Resource Constraints," "Vehicle Routing Problem with Time Window," "Branch-and-Price Heuristics," "Cutting Stock Problems," each dealing with methodological aspects of the field. Three chapters deal with transportation applications: "Large-scale Models in the Airline Industry," "Robust Inventory Ship Routing by Column Generation," and "Ship Scheduling with Recurring Visits and Visit Separation Requirements." Production is the focus of another three chapters: "Combining Column Generation and Lagrangian Relaxation," "Dantzig-Wolfe Decomposition for Job Shop Scheduling," and "Applying Column Generation to Machine Scheduling." The final chapter by François Vanderbeck, "Implementing Mixed Integer Column Generation," reviews how to set-up the Dantzig-Wolfe reformulation, adapt standard MIP techniques to the column generation context (branching, preprocessing, primal heuristics), and deal with specific column generation issues (initialization, stabilization, column management strategies).
A revised and expanded advanced-undergraduate/graduate text (first ed., 1978) about optimization algorithms for problems that can be formulated on graphs and networks. This edition provides many new applications and algorithms while maintaining the classic foundations on which contemporary algorithm