Christos D. Zaroliagis
Other affiliations: University of London, Karlsruhe Institute of Technology, Max Planck Society ...read more
Bio: Christos D. Zaroliagis is an academic researcher from University of Patras. The author has contributed to research in topics: Shortest path problem & Yen's algorithm. The author has an hindex of 27, co-authored 174 publications receiving 3002 citations. Previous affiliations of Christos D. Zaroliagis include University of London & Karlsruhe Institute of Technology.
Papers published on a yearly basis
TL;DR: The time-expanded approach turns out to be more robust for modeling more complex scenarios, whereas the time-dependent approach shows a clearly better performance.
Abstract: We consider two approaches that model timetable information in public transportation systems as shortest-path problems in weighted graphs. In the time-expanded approach, every event at a station, e.g., the departure of a train, is modeled as a node in the graph, while in the time-dependent approach the graph contains only one node per station. Both approaches have been recently considered for (a simplified version of) the earliest arrival problem, but little is known about their relative performance. Thus far, there are only theoretical arguments in favor of the time-dependent approach. In this paper, we provide the first extensive experimental comparison of the two approaches. Using several real-world data sets, we evaluate the performance of the basic models and of several new extensions towards realistic modeling. Furthermore, new insights on solving bicriteria optimization problems in both models are presented. The time-expanded approach turns out to be more robust for modeling more complex scenarios, whereas the time-dependent approach shows a clearly better performance.
TL;DR: This work discusses the concept of Recoverable Robustness, Linear Programming Recovery, and Railway Applications, as well as meta-heuristic and Constraint-Based Approaches for Single-Line Railway Timetabling and Route Planning.
01 Jan 1996
TL;DR: In this paper, the authors consider the problem of finding an obstacle-avoiding path between two points s and t in the plane, amidst a set of disjoint polygonal obstacles with a total of n vertices.
Abstract: We consider the problem of finding an obstacle-avoiding path between two points s and t in the plane, amidst a set of disjoint polygonal obstacles with a total of n vertices The length of this path should be within a small constant factor c of the length of the shortest possible obstacle-avoiding s-t path measured in the L p -metric Such an approximate shortest path is called a c-short path, or a short path with stretch factor c The goal is to preprocess the obstacle-scattered plane by creating an efficient data structure that enables fast reporting of a c-short path (or its length) In this paper, we give a family of algorithms for the above problem that achieve an interesting trade-off between the stretch factor, the query time and the preprocessing bounds Our main results are algorithms that achieve logarithmic length query time, after subquadratic time and space preprocessing
••04 Jan 2002
TL;DR: This paper performs a detailed analysis and experimental evaluation of shortest path computations based on multi-level graph decomposition for one specific application scenario from the field of timetable information in public transport.
Abstract: In many fields of application, shortest path finding problems in very large graphs arise. Scenarios where large numbers of on-line queries for shortest paths have to be processed in real-time appear for example in traffic information systems. In such systems, the techniques considered to speed up the shortest path computation are usually based on precomputed information. One approach proposed often in this context is a space reduction, where precomputed shortest paths are replaced by single edges with weight equal to the length of the corresponding shortest path. In this paper, we give a first systematic experimental study of such a space reduction approach. We introduce the concept of multi-level graph decomposition. For one specific application scenario from the field of timetable information in public transport, we perform a detailed analysis and experimental evaluation of shortest path computations based on multi-level graph decomposition.
20 Jun 2004
TL;DR: In this paper, an overview of models and efficient algorithms for optimally solving timetable information problems like "given a departure and an arrival station as well as a departure time, which is the connection that arrives as early as possible at the arrival station?" Two main approaches that transform the problems into shortest path problems are reviewed.
Abstract: We give an overview of models and efficient algorithms for optimally solving timetable information problems like "given a departure and an arrival station as well as a departure time, which is the connection that arrives as early as possible at the arrival station?" Two main approaches that transform the problems into shortest path problems are reviewed, including issues like the modeling of realistic details (e.g., train transfers) and further optimization criteria (e.g., the number of transfers). An important topic is also multi-criteria optimization, where in general all attractive connections with respect to several criteria shall be determined. Finally, we discuss the performance of the described algorithms, which is crucial for their application in a real system.
22 Jan 2006
TL;DR: Some of the major results in random graphs and some of the more challenging open problems are reviewed, including those related to the WWW.
Abstract: We will review some of the major results in random graphs and some of the more challenging open problems. We will cover algorithmic and structural questions. We will touch on newer models, including those related to the WWW.
02 Jan 1991
••23 Jan 2005
TL;DR: Experimental results show that the most efficient of the new shortest path algorithms outperforms previous algorithms, in particular A* search with Euclidean bounds, by a wide margin on road networks and on some synthetic problem families.
Abstract: We propose shortest path algorithms that use A* search in combination with a new graph-theoretic lower-bounding technique based on landmarks and the triangle inequality. Our algorithms compute optimal shortest paths and work on any directed graph. We give experimental results showing that the most efficient of our new algorithms outperforms previous algorithms, in particular A* search with Euclidean bounds, by a wide margin on road networks and on some synthetic problem families.
TL;DR: This article captures the state of the art in routing protocols in DTNs with three main approaches: the tree approach, the space and time approach, and the modified shortest shortest path approach.
Abstract: n the last few years, there has been much research activity in mobile, wireless, ad hoc networks (MANET). MANETs are infrastructure-less, and nodes in the networks are constantly moving. In MANETs, nodes can directly communicate with each other if they enter each others' communication range. A node can terminate packets or forward packets (serve as a relay). Thus, a packet traverses an ad hoc network by being relayed from one node to another, until it reaches its destination. As nodes are moving, this becomes a challenging task, since the topology of the network is in constant change. How to find a destination, how to route to that destination, and how to insure robust communication in the face of constant topology change are major challenges in mobile ad hoc networks. Routing in mobile ad hoc networks is a well-studied topic. To accommodate the dynamic topology of mobile ad hoc networks, an abundance of routing protocols have recent-For all these routing protocols, it is implicitly assumed that the network is connected and there is a contemporaneous end-to-end path between any source and destination pair. However, in a physical ad hoc network, the assumption that there is a contemporaneous end-to-end path between any source and destination pair may not be true, as illustrated below. In MANETs, when nodes are in motion, links can be obstructed by intervening objects. When nodes must conserve power, links are shut down periodically. These events result in intermittent connectivity. At any given time, when no path exists between source and destination, network partition is said to occur. Thus, it is perfectly possible that two nodes may never be part of the same connected portion of the network. Figure 1 illustrates the time evolving behavior in intermittent-ABSTRACT Recently there has been much research activity in the emerging area of intermittently connected ad hoc networks and delay/disruption tolerant networks (DTN). There are different types of DTNs, depending on the nature of the network environment. Routing in DTNs is one of the key components in the DTN architecture. Therefore, in the last few years researchers have proposed different routing protocols for different types of DTNs. In this article we capture the state of the art in routing protocols in DTNs. We categorize these routing protocols based on information used. For deter-ministic time evolving networks, three main approaches are discussed: the tree approach, the space and time approach, and the modified shortest …
TL;DR: The state of the art in the design and analysis of external memory algorithms and data structures, where the goal is to exploit locality in order to reduce the I/O costs is surveyed.
Abstract: Data sets in large applications are often too massive to fit completely inside the computers internal memory. The resulting input/output communication (or I/O) between fast internal memory and slower external memory (such as disks) can be a major performance bottleneck. In this article we survey the state of the art in the design and analysis of external memory (or EM) algorithms and data structures, where the goal is to exploit locality in order to reduce the I/O costs. We consider a variety of EM paradigms for solving batched and online problems efficiently in external memory. For the batched problem of sorting and related problems such as permuting and fast Fourier transform, the key paradigms include distribution and merging. The paradigm of disk striping offers an elegant way to use multiple disks in parallel. For sorting, however, disk striping can be nonoptimal with respect to I/O, so to gain further improvements we discuss distribution and merging techniques for using the disks independently. We also consider useful techniques for batched EM problems involving matrices (such as matrix multiplication and transposition), geometric data (such as finding intersections and constructing convex hulls), and graphs (such as list ranking, connected components, topological sorting, and shortest paths). In the online domain, canonical EM applications include dictionary lookup and range searching. The two important classes of indexed data structures are based upon extendible hashing and B-trees. The paradigms of filtering and bootstrapping provide a convenient means in online data structures to make effective use of the data accessed from disk. We also reexamine some of the above EM problems in slightly different settings, such as when the data items are moving, when the data items are variable-length (e.g., text strings), or when the allocated amount of internal memory can change dynamically. Programming tools and environments are available for simplifying the EM programming task. During the course of the survey, we report on some experiments in the domain of spatial databases using the TPIE system (transparent parallel I/O programming environment). The newly developed EM algorithms and data structures that incorporate the paradigms we discuss are significantly faster than methods currently used in practice.