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Algorithms
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Algorithm Engineering: Towards Practically Efficient Solutions to Combinatorial Problems
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Algorithm Engineering: Towards Practically Efficient Solutions to Combinatorial Problems

A special issue of Algorithms (ISSN 1999-4893).

Deadline for manuscript submissions: closed (30 June 2019) | Viewed by 40778

Special Issue Editors

Prof. Dr. Daniele Frigioni

E-Mail Website
Guest Editor
Department of Information Engineering, Computer Science and Mathematics, University of L’Aquila, 67100 L’Aquila, Italy
Interests: algorithms and data structures; algorithm engineering; distributed algorithms; large-scale optimization
Dr. Mattia D'Emidio

E-Mail Website
Guest Editor
Department of Information Engineering, Computer Science and Mathematics, University of L’Aquila, Via Vetoio, I-67100 L’Aquila, Italy
Interests: design and efficient implementation of algorithms; experimental algorithmics; algorithms for massive datasets; distributed algorithms
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Algorithm Engineering is a new and emerging discipline in which the aim is bridging the increasing gap between classical algorithm theory and algorithmics in practice. In particular, advancements in computer hardware have rendered traditional computer models more and more unrealistic, and have led to a constantly increasing demand for practically efficient solutions to real world problems. Driven by concrete applications, Algorithm Engineering complements theory by the benefits of experimentation and puts equal emphasis on all aspects arising during a cyclic solution process ranging from realistic modelling, design, analysis, robust and efficient implementations to careful experiments.

The purpose of this Special Issue is to attract papers presenting original research in the area of Algorithm Engineering. In particular, we encourage submissions concerning the design, implementation, tuning, and experimental evaluation of discrete algorithms and data structures, or addressing methodological issues and standards in algorithmic experimentation. Papers dealing with advanced models of computing, including memory hierarchies, cloud architectures, and parallel processing are also welcome.

We solicit contributions from all relevant areas of applied algorithmic research which include but are not limited to: databases; geometry; graphs; big data; complex networks; combinatorial aspects of scientific computing; and computational problems in natural sciences or engineering.

Prof. Daniele Frigioni
Dr. Mattia D'Emidio
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Algorithms is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Algorithm Engineering
  • Algorithms and Data Structures
  • Experimental Algorithmics
  • Algorithms for Large Scale Combinatorial Problems
  • Efficient Solutions for Real World Applications

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Published Papers (7 papers)

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Editorial

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3 pages, 161 KiB  
Editorial
Special Issue on “Algorithm Engineering: Towards Practically Efficient Solutions to Combinatorial Problems”
by Mattia D’Emidio and Daniele Frigioni
Algorithms 2019, 12(11), 229; https://doi.org/10.3390/a12110229 - 3 Nov 2019
Viewed by 2963
Abstract
The purpose of this special issue of Algorithms was to attract papers presenting original research in the area of algorithm engineering. In particular, submissions concerning the design, analysis, implementation, tuning, and experimental evaluation of discrete algorithms and data structures, and/or addressing methodological issues [...] Read more.
The purpose of this special issue of Algorithms was to attract papers presenting original research in the area of algorithm engineering. In particular, submissions concerning the design, analysis, implementation, tuning, and experimental evaluation of discrete algorithms and data structures, and/or addressing methodological issues and standards in algorithmic experimentation were encouraged. Papers dealing with advanced models of computing, including memory hierarchies, cloud architectures, and parallel processing were also welcome. In this regard, we solicited contributions from all most prominent areas of applied algorithmic research, which include but are not limited to graphs, databases, computational geometry, big data, networking, combinatorial aspects of scientific computing, and computational problems in the natural sciences or engineering. Full article
(This article belongs to the Special Issue Algorithm Engineering: Towards Practically Efficient Solutions to Combinatorial Problems)

Research

Jump to: Editorial

16 pages, 568 KiB  
Article
Multimodal Dynamic Journey-Planning
by Kalliopi Giannakopoulou, Andreas Paraskevopoulos and Christos Zaroliagis
Algorithms 2019, 12(10), 213; https://doi.org/10.3390/a12100213 - 13 Oct 2019
Cited by 20 | Viewed by 5009
Abstract
In this paper, a new model, known as the multimodal dynamic timetable model (DTM), is presented for computing optimal multimodal journeys in schedule-based public transport systems. The new model constitutes an extension of the dynamic timetable model (DTM), which was developed originally [...] Read more.
In this paper, a new model, known as the multimodal dynamic timetable model (DTM), is presented for computing optimal multimodal journeys in schedule-based public transport systems. The new model constitutes an extension of the dynamic timetable model (DTM), which was developed originally for a different setting (unimodal journey-planning). Multimodal DTM demonstrates a very fast query algorithm that meets the requirement for real-time response to best journey queries, and an ultra-fast update algorithm for updating the timetable information in case of delays of scheduled-based vehicles. An experimental study on real-world metropolitan networks demonstrates that the query and update algorithms of Multimodal DTM compare favorably with other state-of-the-art approaches when public transport, including unrestricted—with respect to departing time—traveling (e.g., walking and electric vehicles) is considered. Full article
(This article belongs to the Special Issue Algorithm Engineering: Towards Practically Efficient Solutions to Combinatorial Problems)
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22 pages, 1427 KiB  
Article
Approximating the Temporal Neighbourhood Function of Large Temporal Graphs
by Pierluigi Crescenzi, Clémence Magnien and Andrea Marino
Algorithms 2019, 12(10), 211; https://doi.org/10.3390/a12100211 - 10 Oct 2019
Cited by 13 | Viewed by 3962
Abstract
Temporal networks are graphs in which edges have temporal labels, specifying their starting times and their traversal times. Several notions of distances between two nodes in a temporal network can be analyzed, by referring, for example, to the earliest arrival time or to [...] Read more.
Temporal networks are graphs in which edges have temporal labels, specifying their starting times and their traversal times. Several notions of distances between two nodes in a temporal network can be analyzed, by referring, for example, to the earliest arrival time or to the latest starting time of a temporal path connecting the two nodes. In this paper, we mostly refer to the notion of temporal reachability by using the earliest arrival time. In particular, we first show how the sketch approach, which has already been used in the case of classical graphs, can be applied to the case of temporal networks in order to approximately compute the sizes of the temporal cones of a temporal network. By making use of this approach, we subsequently show how we can approximate the temporal neighborhood function (that is, the number of pairs of nodes reachable from one another in a given time interval) of large temporal networks in a few seconds. Finally, we apply our algorithm in order to analyze and compare the behavior of 25 public transportation temporal networks. Our results can be easily adapted to the case in which we want to refer to the notion of distance based on the latest starting time. Full article
(This article belongs to the Special Issue Algorithm Engineering: Towards Practically Efficient Solutions to Combinatorial Problems)
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12 pages, 307 KiB  
Article
Recommending Links to Control Elections via Social Influence
by Federico Corò, Gianlorenzo D’Angelo and Yllka Velaj
Algorithms 2019, 12(10), 207; https://doi.org/10.3390/a12100207 - 1 Oct 2019
Cited by 5 | Viewed by 3692
Abstract
Political parties recently learned that they must use social media campaigns along with advertising on traditional media to defeat their opponents. Before the campaign starts, it is important for a political party to establish and ensure its media presence, for example by enlarging [...] Read more.
Political parties recently learned that they must use social media campaigns along with advertising on traditional media to defeat their opponents. Before the campaign starts, it is important for a political party to establish and ensure its media presence, for example by enlarging their number of connections in the social network in order to assure a larger portion of users. Indeed, adding new connections between users increases the capabilities of a social network of spreading information, which in turn can increase the retention rate and the number of new voters. In this work, we address the problem of selecting a fixed-size set of new connections to be added to a subset of voters that, with their influence, will change the opinion of the network’s users about a target candidate, maximizing its chances to win the election. We provide a constant factor approximation algorithm for this problem and we experimentally show that, with few new links and small computational time, our algorithm is able to maximize the chances to make the target candidate win the elections. Full article
(This article belongs to the Special Issue Algorithm Engineering: Towards Practically Efficient Solutions to Combinatorial Problems)
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37 pages, 711 KiB  
Article
Guidelines for Experimental Algorithmics: A Case Study in Network Analysis
by Eugenio Angriman, Alexander van der Grinten, Moritz von Looz, Henning Meyerhenke, Martin Nöllenburg, Maria Predari and Charilaos Tzovas
Algorithms 2019, 12(7), 127; https://doi.org/10.3390/a12070127 - 26 Jun 2019
Cited by 22 | Viewed by 6969
Abstract
The field of network science is a highly interdisciplinary area; for the empirical analysis of network data, it draws algorithmic methodologies from several research fields. Hence, research procedures and descriptions of the technical results often differ, sometimes widely. In this paper we focus [...] Read more.
The field of network science is a highly interdisciplinary area; for the empirical analysis of network data, it draws algorithmic methodologies from several research fields. Hence, research procedures and descriptions of the technical results often differ, sometimes widely. In this paper we focus on methodologies for the experimental part of algorithm engineering for network analysis—an important ingredient for a research area with empirical focus. More precisely, we unify and adapt existing recommendations from different fields and propose universal guidelines—including statistical analyses—for the systematic evaluation of network analysis algorithms. This way, the behavior of newly proposed algorithms can be properly assessed and comparisons to existing solutions become meaningful. Moreover, as the main technical contribution, we provide SimexPal, a highly automated tool to perform and analyze experiments following our guidelines. To illustrate the merits of SimexPal and our guidelines, we apply them in a case study: we design, perform, visualize and evaluate experiments of a recent algorithm for approximating betweenness centrality, an important problem in network analysis. In summary, both our guidelines and SimexPal shall modernize and complement previous efforts in experimental algorithmics; they are not only useful for network analysis, but also in related contexts. Full article
(This article belongs to the Special Issue Algorithm Engineering: Towards Practically Efficient Solutions to Combinatorial Problems)
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23 pages, 5491 KiB  
Article
Bamboo Garden Trimming Problem: Priority Schedulings
by Mattia D’Emidio, Gabriele Di Stefano and Alfredo Navarra
Algorithms 2019, 12(4), 74; https://doi.org/10.3390/a12040074 - 13 Apr 2019
Cited by 6 | Viewed by 5789
Abstract
The paper deals with the Bamboo Garden Trimming (BGT) problem introduced in [Gąsieniec et al., SOFSEM’17]. The problem is difficult to solved due to its close relationship to Pinwheel scheduling. The garden with n bamboos is an analogue of a system of [...] Read more.
The paper deals with the Bamboo Garden Trimming (BGT) problem introduced in [Gąsieniec et al., SOFSEM’17]. The problem is difficult to solved due to its close relationship to Pinwheel scheduling. The garden with n bamboos is an analogue of a system of n machines that have to be attended (e.g., serviced) with different frequencies. During each day, bamboo b i grows an extra height h i , for i = 1 , … , n and, on the conclusion of the day, at most one bamboo has its entire height cut.The goal is to design a perpetual schedule of cuts to keep the height of the tallest ever bamboo as low as possible. The contribution in this paper is twofold, and is both theoretical and experimental. In particular, the focus is on understanding what has been called priority schedulings, i.e., cutting strategies where priority is given to bamboos whose current height is above a threshold greater than or equal to H = ∑ i = 1 n h i . Value H represents the total daily growth of the system and it is known that one cannot keep bamboos in the garden below this threshold indefinitely. As the first result, it is proved that, for any distribution of integer growth rates h 1 , … , h n and any priority scheduling, the system stabilises in a fixed cycle of cuts. Then, the focus is on the so-called ReduceMax strategy, a greedy priority scheduling that each day cuts the tallest bamboo, regardless of the growth rates distribution. ReduceMax is known to provide a O ( log n ) -approximation, with respect to the lower bound H. One of the main results achieved is that, if ReduceMax stabilises in a round-robin type cycle, then it guarantees 2-approximation. Furthermore, preliminary results are provided relating the structure of the input instance, in terms of growth rates, and the behavior of ReduceMax when applied to such inputs. Finally, a conjecture that ReduceMax is 2-approximating for the BGT problem is claimed, hence an extended experimental evaluation was conducted to support the conjecture and to compare ReduceMax with other relevant scheduling algorithms. The obtained results show that ReduceMax : (i) provides 2-approximation in all considered inputs; and (ii) always outperforms other considered strategies, even those for which better worst case approximation guarantees have been proven. Full article
(This article belongs to the Special Issue Algorithm Engineering: Towards Practically Efficient Solutions to Combinatorial Problems)
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27 pages, 494 KiB  
Article
Convex-Hull Algorithms: Implementation, Testing, and Experimentation
by Ask Neve Gamby and Jyrki Katajainen
Algorithms 2018, 11(12), 195; https://doi.org/10.3390/a11120195 - 28 Nov 2018
Cited by 20 | Viewed by 10587
Abstract
From a broad perspective, we study issues related to implementation, testing, and experimentation in the context of geometric algorithms. Our focus is on the effect of quality of implementation on experimental results. More concisely, we study algorithms that compute convex hulls for a [...] Read more.
From a broad perspective, we study issues related to implementation, testing, and experimentation in the context of geometric algorithms. Our focus is on the effect of quality of implementation on experimental results. More concisely, we study algorithms that compute convex hulls for a multiset of points in the plane. We introduce several improvements to the implementations of the studied algorithms: plane-sweep, torch, quickhull, and throw-away. With a new set of space-efficient implementations, the experimental results—in the integer-arithmetic setting—are different from those of earlier studies. From this, we conclude that utmost care is needed when doing experiments and when trying to draw solid conclusions upon them. Full article
(This article belongs to the Special Issue Algorithm Engineering: Towards Practically Efficient Solutions to Combinatorial Problems)
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