Seismic feature extraction using steiner tree methods

Schmidt, Ludwig; Hegde, Chinmay; Indyk, Piotr; Lu, Ligang; Chi, Xin-gang; Hohl, Detlef

doi:10.1109/icassp.2015.7178250

Cited by 4 publications

(1 citation statement)

References 11 publications

(20 reference statements)

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“…By setting sufficiently high node weights for its terminals, each SPG instance can be reduced to a PCSTP. However, while the number of real-world applications of the classical Steiner tree problem in graphs is limited [13], the PCSTP entails many practical applications, which can be found in various areas, for instance in the design of telecommunication networks [24], electricity planning [6], computational biology [17], or geophysics [31].…”

Section: Introductionmentioning

confidence: 99%

On the Exact Solution of Prize-Collecting Steiner Tree Problems

Rehfeldt

Koch

2022

INFORMS Journal on Computing

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The prize-collecting Steiner tree problem (PCSTP) is a well-known generalization of the classic Steiner tree problem in graphs, with a large number of practical applications. It attracted particular interest during the 11th DIMACS Challenge in 2014, and since then, several PCSTP solvers have been introduced in the literature. Although these new solvers further, and often drastically, improved on the results of the DIMACS Challenge, many PCSTP benchmark instances have remained unsolved. The following article describes further advances in the state of the art in exact PCSTP solving. It introduces new techniques and algorithms for PCSTP, involving various new transformations (or reductions) of PCSTP instances to equivalent problems, for example, to decrease the problem size or to obtain a better integer programming formulation. Several of the new techniques and algorithms provably dominate previous approaches. Further theoretical properties of the new components, such as their complexity, are discussed. Also, new complexity results for the exact solution of PCSTP and related problems are described, which form the base of the algorithm design. Finally, the new developments also translate into a strong computational performance: the resulting exact PCSTP solver outperforms all previous approaches, both in terms of runtime and solvability. In particular, it solves several formerly intractable benchmark instances from the 11th DIMACS Challenge to optimality. Moreover, several recently introduced large-scale instances with up to 10 million edges, previously considered to be too large for any exact approach, can now be solved to optimality in less than two hours. Summary of Contribution: The prize-collecting Steiner tree problem (PCSTP) is a well-known generalization of the classic Steiner tree problem in graphs, with many practical applications. The article introduces and analyses new techniques and algorithms for PCSTP that ultimately aim for improved (practical) exact solution. The algorithmic developments are underpinned by results on theoretical aspects, such as fixed-parameter tractability of PCSTP. Computationally, we considerably push the limits of tractibility, being able to solve PCSTP instances with up to 10 million edges. The new solver, which also considerably outperforms the state of the art on smaller instances, will be made publicly available as part of the SCIP Optimization Suite.

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Section: Introductionmentioning

confidence: 99%

On the Exact Solution of Prize-Collecting Steiner Tree Problems

Rehfeldt

Koch

2022

INFORMS Journal on Computing

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Solving Steiner trees: Recent advances, challenges, and perspectives

Ljubić

2020

Networks

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The Steiner tree problem (STP) in graphs is one of the most studied problems in combinatorial optimization. Since its inception in 1970, numerous articles published in the journal Networks have stimulated new theoretical and computational studies on Steiner trees: from approximation algorithms, heuristics, metaheuristics, all the way to exact algorithms based on (mixed) integer linear programming, fixed parameter tractability, or combinatorial branch‐and‐bounds. The pervasive applicability and relevance of Steiner trees have been reinforced by the recent 11th DIMACS Implementation Challenge in 2014 and the PACE 2018 Challenge. This article provides an overview of the rich developments from the last three decades for the STP in graphs and highlights the most recent computational studies for some of its closely related variants.

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Employing Remote Sensing, Data Communication Networks, AI, and Optimization Methodologies in Seismology

Abdalzaher

El-Sayed

Fouda

2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Seismology is among the intrinsic sciences that strictly affect human lives. Many research efforts are presented in the literature aiming at achieving risk mitigation and disaster management. More particularly, modern technologies have been employed in such a pivot. However, the day-to-day challenges and complexities of such natural science that face the stack holders still need more reliable and intelligent solutions. The solution can depend on a partial or integrated system of modern technologies. In this paper, we extensively survey the co-related modern technologies aiming to gather the major efforts exerted in this regard. It also outlines the desirability of seismology to modern technologies. Then, we present a detailed analysis of remote sensing and data communication networks (DCNs), which are considered the backend of seismic networks. Furthermore, for seismology, we depict both classical and non-classical approaches based on DCN principles, such as optical fiber-based acoustic sensors, social media, and the internet of things (IoT). Following that, a comprehensive description of the various optimization techniques utilized for seismic wave analysis and for prolonging network lifetime is offered. A description of the important functions that artificial intelligence (AI) can play in different fields of seismology is also included. Finally, we present some recommendations for stack holders to prevent natural calamities and preserve human lives.

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Seismic feature extraction using steiner tree methods

Cited by 4 publications

References 11 publications

On the Exact Solution of Prize-Collecting Steiner Tree Problems

On the Exact Solution of Prize-Collecting Steiner Tree Problems

Solving Steiner trees: Recent advances, challenges, and perspectives

Employing Remote Sensing, Data Communication Networks, AI, and Optimization Methodologies in Seismology

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