Relevant bibliographies by topics / Large Scale Probleme

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Large Scale Probleme'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Large Scale Probleme"

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Ahrens, Gerd-Axel, Klaus J. Beckmann, Manfred Boltze, Alexander Eisenkopf, Hartmut Fricke, Günther Knieps, Andreas Knorr, et al. "Auswahl und Abwicklung von Großprojekten/Selection and Execution of large-scale projects." Bauingenieur 90, no. 03 (2015): 129–39. http://dx.doi.org/10.37544/0005-6650-2015-03-63.

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In der Öffentlichkeit entsteht aufgrund von Kostensteigerungen und Zeitverzügen oft der Eindruck, die hoch geschätzte Ingenieurskunst in Deutschland ginge verloren. Tatsächliche Ursachen für die Probleme sind aber oft Verzögerungen in den Phasen der strategischen Planung durch Abstimmungsprozesse mit Maximalforderungen, langwierige politische Grundsatzdebatten mit Blockaden notwendiger Entscheidungen oder Verzögerungen bei der Finanzierung und Mittelbereitstellung, der Projektplanung und deren rechtlichen Sicherung bzw. Genehmigung. Darüber hinaus steht bei Einigen eine grundsätzliche Skepsis gegenüber Großprojekten im Hintergrund, die manchmal mit einer partiellen Innovations-, Technik- und Veränderungsfeindlichkeit gepaart ist. Dabei sind sowohl überzogene Egoismen („Nimby-Effekte“) als auch tatsächlich hohe belastende Wirkungen für Betroffene zu beobachten. Ein weiterer Anlass für Kritik betrifft solche öffentliche Projekte, die oft aus lokalpolitischem Geltungsdrang und Prestigegründen oder mit deutlich überzogenen Nutzenerwartungen (Image, Standortmarketing, Wirtschaftsförderung) hoch subventioniert und ohne hinreichende Effizienznachweise politisch beschlossen und umgesetzt werden.   Der Beitrag zeigt Problemursachen auf und beschreibt Möglichkeiten zur frühzeitigen Identifikation und Problembeseitigung. Dazu zählen klare Definitionen der Projektziele und des Zeitrahmens, der adäquate Umgang mit Unsicherheiten und Risiken, die Qualitätssicherung für Entscheidungen und Prozesse, die Bereitstellung ausreichender Ressourcen, die Vermeidung von Umplanungen während der Bauabwicklung, die Schaffung klarer Verantwortlichkeiten und Haftungsregelungen, die Verbesserung von Kommunikation und Partizipation sowie von Datengrundlagen und Instrumenten.
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P.Sumathi, P. Sumathi, and A. Gangadharan A.Gangadharan. "New Technique to DetectRedundant Constraints in Large Scale Linear Programming Problems." International Journal of Scientific Research 3, no. 6 (June 1, 2012): 236–38. http://dx.doi.org/10.15373/22778179/june2014/77.

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MUKADDES, A. M. M., Masao OGINO, Ryuji SHIOYA, and Hiroshi KANAYAMA. "704 A Scalable Balancing Domain Decomposition Based Preconditioner for Large Scale Thermal-Solid Coupling Problems." Proceedings of The Computational Mechanics Conference 2005.18 (2005): 523–24. http://dx.doi.org/10.1299/jsmecmd.2005.18.523.

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Mazhar, Hammad, Dan Negrut, Arman Pazouki, and Alessandro Tasora. "59079 A SCALABLE PARALLEL METHOD FOR LARGE SCALE COLLISION DETECTION PROBLEMS(Contact, Impact, and Friction)." Proceedings of the Asian Conference on Multibody Dynamics 2010.5 (2010): _59079–1_—_59079–12_. http://dx.doi.org/10.1299/jsmeacmd.2010.5._59079-1_.

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Vakhnin, A. V., E. A. Sopov, I. A. Panfilov, A. S. Polyakova, and D. V. Kustov. "A problem decomposition approach for large-scale global optimization problems." IOP Conference Series: Materials Science and Engineering 537 (June 18, 2019): 052031. http://dx.doi.org/10.1088/1757-899x/537/5/052031.

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CONLON, JOSEPH P. "HIERARCHY PROBLEMS IN STRING THEORY AND LARGE VOLUME MODELS." Modern Physics Letters A 23, no. 01 (January 10, 2008): 1–16. http://dx.doi.org/10.1142/s0217732308025930.

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Nature generates many hierarchically different scales. It is necessary to explain where these scales come from and how they are related. Three such scales are the weak scale, the scale associated with axion physics, and the scale associated with neutrino masses. I review the large volume models that arise in flux compactifications of type IIB string theory and explain how an intermediate string scale can quantitatively explain the above three scales. The models also predict a new physical scale at 1 MeV, associated to a gravitationally coupled scalar.
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Banihashemi, Mohamadreza, and Ali Haghani. "Optimization Model for Large-Scale Bus Transit Scheduling Problems." Transportation Research Record: Journal of the Transportation Research Board 1733, no. 1 (January 2000): 23–30. http://dx.doi.org/10.3141/1733-04.

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A procedure is presented for solving real-world large-scale multiple depot vehicle scheduling (MDVS) problems considering the route time constraints (RTCs). The procedure is applied to some test problems and then to a real-world problem. The real-world problem is the transit bus scheduling problem of the mass transit administration (MTA) in Baltimore, Maryland. The RTCs are added to the MDVS problem to account for real-world operational restrictions such as fuel consumption. Formulation of the MDVS problem, the set of constraints for considering the time restriction, and a heuristic procedure for solving the MDVS problems with RTCs are discussed. Application of the proposed procedures in solving bus scheduling problems in large cities requires a reduction in size of those problems in terms of number of variables and constraints. Two techniques are proposed to decrease the size of the real-world problems. Combining these techniques results in a strategy to reduce the MTA problem size into a manageable and solvable size. The solutions to the reduced size problems are further improved by solving a series of single depot vehicle scheduling problems for each of the MTA depots. The final results from the proposed model are compared with the MTA’s January 1998 schedule. The comparison indicates that the proposed model improves on the MTA schedules in all aspects. The improvements are 7.90 percent in the number of vehicles, 4.66 percent in the operational time, and 5.77 percent in the total cost.
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Bredereck, Robert, Piotr Faliszewski, Rolf Niedermeier, and Nimrod Talmon. "Large-Scale Election Campaigns: Combinatorial Shift Bribery." Journal of Artificial Intelligence Research 55 (March 16, 2016): 603–52. http://dx.doi.org/10.1613/jair.4927.

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We study the complexity of a combinatorial variant of the Shift Bribery problem in elections. In the standard Shift Bribery problem, we are given an election where each voter has a preference order over the set of candidates and where an outside agent, the briber, can pay each voter to rank the briber's favorite candidate a given number of positions higher. The goal is to ensure the victory of the briber's preferred candidate. The combinatorial variant of the problem, introduced in this paper, models settings where it is possible to affect the position of the preferred candidate in multiple votes, either positively or negatively, with a single bribery action. This variant of the problem is particularly interesting in the context of large-scale campaign management problems (which, from the technical side, are modeled as bribery problems). We show that, in general, the combinatorial variant of the problem is highly intractable; specifically, NP-hard, hard in the parameterized sense, and hard to approximate. Nevertheless, we provide parameterized algorithms and approximation algorithms for natural restricted cases.
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Gratch, J., and S. Chien. "Adaptive Problem-solving for Large-scale Scheduling Problems: A Case Study." Journal of Artificial Intelligence Research 4 (May 1, 1996): 365–96. http://dx.doi.org/10.1613/jair.177.

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Although most scheduling problems are NP-hard, domain specific techniques perform well in practice but are quite expensive to construct. In adaptive problem-solving solving, domain specific knowledge is acquired automatically for a general problem solver with a flexible control architecture. In this approach, a learning system explores a space of possible heuristic methods for one well-suited to the eccentricities of the given domain and problem distribution. In this article, we discuss an application of the approach to scheduling satellite communications. Using problem distributions based on actual mission requirements, our approach identifies strategies that not only decrease the amount of CPU time required to produce schedules, but also increase the percentage of problems that are solvable within computational resource limitations.
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Jagers, Sverker C., Niklas Harring, Åsa Löfgren, Martin Sjöstedt, Francisco Alpizar, Bengt Brülde, David Langlet, et al. "On the preconditions for large-scale collective action." Ambio 49, no. 7 (November 12, 2019): 1282–96. http://dx.doi.org/10.1007/s13280-019-01284-w.

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Abstract The phenomenon of collective action and the origin of collective action problems have been extensively and systematically studied in the social sciences. Yet, while we have substantial knowledge about the factors promoting collective action at the local level, we know far less about how these insights travel to large-scale collective action problems. Such problems, however, are at the heart of humanity’s most pressing challenges, including climate change, large-scale natural resource depletion, biodiversity loss, nuclear proliferation, antibiotic resistance due to overconsumption of antibiotics, and pollution. In this paper, we suggest an analytical framework that captures the theoretical understanding of preconditions for large-scale collective action. This analytical framework aims at supporting future empirical analyses of how to cope with and overcome larger-scale collective action problems. More specifically, we (i) define and describe the main characteristics of a large-scale collective action problem and (ii) explain why voluntary and, in particular, spontaneous large-scale collective action among individual actors becomes more improbable as the collective action problem becomes larger, thus demanding interventions by an external authority (a third party) for such action to be generated. Based on this, we (iii) outline an analytical framework that illustrates the connection between third-party interventions and large-scale collective action. We conclude by suggesting avenues for future research.
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Dissertations / Theses on the topic "Large Scale Probleme"

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Falter, Daniela [Verfasser], and Bruno [Akademischer Betreuer] Merz. "A novel approach for large-scale flood risk assessments : continuous and long-term simulation of the full flood risk chain / Daniela Falter ; Betreuer: Bruno Merz." Potsdam : Universität Potsdam, 2016. http://d-nb.info/1218400412/34.

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Brunner, Carl. "Pairwise Classification and Pairwise Support Vector Machines." Doctoral thesis, Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2012. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-87820.

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Several modifications have been suggested to extend binary classifiers to multiclass classification, for instance the One Against All technique, the One Against One technique, or Directed Acyclic Graphs. A recent approach for multiclass classification is the pairwise classification, which relies on two input examples instead of one and predicts whether the two input examples belong to the same class or to different classes. A Support Vector Machine (SVM), which is able to handle pairwise classification tasks, is called pairwise SVM. A common pairwise classification task is face recognition. In this area, a set of images is given for training and another set of images is given for testing. Often, one is interested in the interclass setting. The latter means that any person which is represented by an image in the training set is not represented by any image in the test set. From the mentioned multiclass classification techniques only the pairwise classification technique provides meaningful results in the interclass setting. For a pairwise classifier the order of the two examples should not influence the classification result. A common approach to enforce this symmetry is the use of selected kernels. Relations between such kernels and certain projections are provided. It is shown, that those projections can lead to an information loss. For pairwise SVMs another approach for enforcing symmetry is the symmetrization of the training sets. In other words, if the pair (a,b) of examples is a training pair then (b,a) is a training pair, too. It is proven that both approaches do lead to the same decision function for selected parameters. Empirical tests show that the approach using selected kernels is three to four times faster. For a good interclass generalization of pairwise SVMs training sets with several million training pairs are needed. A technique is presented which further speeds up the training time of pairwise SVMs by a factor of up to 130 and thus enables the learning of training sets with several million pairs. Another element affecting time is the need to select several parameters. Even with the applied speed up techniques a grid search over the set of parameters would be very expensive. Therefore, a model selection technique is introduced that is much less computationally expensive. In machine learning, the training set and the test set are created by using some data generating process. Several pairwise data generating processes are derived from a given non pairwise data generating process. Advantages and disadvantages of the different pairwise data generating processes are evaluated. Pairwise Bayes' Classifiers are introduced and their properties are discussed. It is shown that pairwise Bayes' Classifiers for interclass generalization tasks can differ from pairwise Bayes' Classifiers for interexample generalization tasks. In face recognition the interexample task implies that each person which is represented by an image in the test set is also represented by at least one image in the training set. Moreover, the set of images of the training set and the set of images of the test set are disjoint. Pairwise SVMs are applied to four synthetic and to two real world datasets. One of the real world datasets is the Labeled Faces in the Wild (LFW) database while the other one is provided by Cognitec Systems GmbH. Empirical evidence for the presented model selection heuristic, the discussion about the loss of information and the provided speed up techniques is given by the synthetic databases and it is shown that classifiers of pairwise SVMs lead to a similar quality as pairwise Bayes' classifiers. Additionally, a pairwise classifier is identified for the LFW database which leads to an average equal error rate (EER) of 0.0947 with a standard error of the mean (SEM) of 0.0057. This result is better than the result of the current state of the art classifier, namely the combined probabilistic linear discriminant analysis classifier, which leads to an average EER of 0.0993 and a SEM of 0.0051
Es gibt verschiedene Ansätze, um binäre Klassifikatoren zur Mehrklassenklassifikation zu nutzen, zum Beispiel die One Against All Technik, die One Against One Technik oder Directed Acyclic Graphs. Paarweise Klassifikation ist ein neuerer Ansatz zur Mehrklassenklassifikation. Dieser Ansatz basiert auf der Verwendung von zwei Input Examples anstelle von einem und bestimmt, ob diese beiden Examples zur gleichen Klasse oder zu unterschiedlichen Klassen gehören. Eine Support Vector Machine (SVM), die für paarweise Klassifikationsaufgaben genutzt wird, heißt paarweise SVM. Beispielsweise werden Probleme der Gesichtserkennung als paarweise Klassifikationsaufgabe gestellt. Dazu nutzt man eine Menge von Bildern zum Training und ein andere Menge von Bildern zum Testen. Häufig ist man dabei an der Interclass Generalization interessiert. Das bedeutet, dass jede Person, die auf wenigstens einem Bild der Trainingsmenge dargestellt ist, auf keinem Bild der Testmenge vorkommt. Von allen erwähnten Mehrklassenklassifikationstechniken liefert nur die paarweise Klassifikationstechnik sinnvolle Ergebnisse für die Interclass Generalization. Die Entscheidung eines paarweisen Klassifikators sollte nicht von der Reihenfolge der zwei Input Examples abhängen. Diese Symmetrie wird häufig durch die Verwendung spezieller Kerne gesichert. Es werden Beziehungen zwischen solchen Kernen und bestimmten Projektionen hergeleitet. Zudem wird gezeigt, dass diese Projektionen zu einem Informationsverlust führen können. Für paarweise SVMs ist die Symmetrisierung der Trainingsmengen ein weiter Ansatz zur Sicherung der Symmetrie. Das bedeutet, wenn das Paar (a,b) von Input Examples zur Trainingsmenge gehört, dann muss das Paar (b,a) ebenfalls zur Trainingsmenge gehören. Es wird bewiesen, dass für bestimmte Parameter beide Ansätze zur gleichen Entscheidungsfunktion führen. Empirische Messungen zeigen, dass der Ansatz mittels spezieller Kerne drei bis viermal schneller ist. Um eine gute Interclass Generalization zu erreichen, werden bei paarweisen SVMs Trainingsmengen mit mehreren Millionen Paaren benötigt. Es wird eine Technik eingeführt, die die Trainingszeit von paarweisen SVMs um bis zum 130-fachen beschleunigt und es somit ermöglicht, Trainingsmengen mit mehreren Millionen Paaren zu verwenden. Auch die Auswahl guter Parameter für paarweise SVMs ist im Allgemeinen sehr zeitaufwendig. Selbst mit den beschriebenen Beschleunigungen ist eine Gittersuche in der Menge der Parameter sehr teuer. Daher wird eine Model Selection Technik eingeführt, die deutlich geringeren Aufwand erfordert. Im maschinellen Lernen werden die Trainingsmenge und die Testmenge von einem Datengenerierungsprozess erzeugt. Ausgehend von einem nicht paarweisen Datengenerierungsprozess werden unterschiedliche paarweise Datengenerierungsprozesse abgeleitet und ihre Vor- und Nachteile bewertet. Es werden paarweise Bayes-Klassifikatoren eingeführt und ihre Eigenschaften diskutiert. Es wird gezeigt, dass sich diese Bayes-Klassifikatoren für Interclass Generalization Aufgaben und für Interexample Generalization Aufgaben im Allgemeinen unterscheiden. Bei der Gesichtserkennung bedeutet die Interexample Generalization, dass jede Person, die auf einem Bild der Testmenge dargestellt ist, auch auf mindestens einem Bild der Trainingsmenge vorkommt. Außerdem ist der Durchschnitt der Menge der Bilder der Trainingsmenge mit der Menge der Bilder der Testmenge leer. Paarweise SVMs werden an vier synthetischen und an zwei Real World Datenbanken getestet. Eine der verwendeten Real World Datenbanken ist die Labeled Faces in the Wild (LFW) Datenbank. Die andere wurde von Cognitec Systems GmbH bereitgestellt. Die Annahmen der Model Selection Technik, die Diskussion über den Informationsverlust, sowie die präsentierten Beschleunigungstechniken werden durch empirische Messungen mit den synthetischen Datenbanken belegt. Zudem wird mittels dieser Datenbanken gezeigt, dass Klassifikatoren von paarweisen SVMs zu ähnlich guten Ergebnissen wie paarweise Bayes-Klassifikatoren führen. Für die LFW Datenbank wird ein paarweiser Klassifikator bestimmt, der zu einer durchschnittlichen Equal Error Rate (EER) von 0.0947 und einem Standard Error of The Mean (SEM) von 0.0057 führt. Dieses Ergebnis ist besser als das des aktuellen State of the Art Klassifikators, dem Combined Probabilistic Linear Discriminant Analysis Klassifikator. Dieser führt zu einer durchschnittlichen EER von 0.0993 und einem SEM von 0.0051
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Brinkel, Johanna [Verfasser]. "A user-centred evaluation of a mobile phone-based interactive voice response system to support infectious disease surveillance and access to healthcare for sick children in Ghana: users’ experiences, challenges and opportunities for large-scale application. Part of a concept and pilot study for mobile phone-based Electronic Health Information and Surveillance System (eHISS) for Africa / Johanna Brinkel." Bielefeld : Universitätsbibliothek Bielefeld, 2020. http://d-nb.info/1204561826/34.

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Tran, Van-Hoai. "Solving large scale crew pairing problems." [S.l. : s.n.], 2005. http://deposit.ddb.de/cgi-bin/dokserv?idn=975292714.

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Grigoleit, Mark Ted. "Optimisation of large scale network problems." Curtin University of Technology, Department of Mathematics and Statistics, 2008. http://espace.library.curtin.edu.au:80/R/?func=dbin-jump-full&object_id=115092.

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The Constrained Shortest Path Problem (CSPP) consists of finding the shortest path in a graph or network that satisfies one or more resource constraints. Without these constraints, the shortest path problem can be solved in polynomial time; with them, the CSPP is NP-hard and thus far no polynomial-time algorithms exist for solving it optimally. The problem arises in a number of practical situations. In the case of vehicle path planning, the vehicle may be an aircraft flying through a region with obstacles such as mountains or radar detectors, with an upper bound on the fuel consumption, the travel time or the risk of attack. The vehicle may be a submarine travelling through a region with sonar detectors, with a time or risk budget. These problems all involve a network which is a discrete model of the physical domain. Another example would be the routing of voice and data information in a communications network such as a mobile phone network, where the constraints may include maximum call delays or relay node capacities. This is a problem of current economic importance, and one for which time-sensitive solutions are not always available, especially if the networks are large. We consider the simplest form of the problem, large grid networks with a single side constraint, which have been studied in the literature. This thesis explores the application of Constraint Programming combined with Lagrange Relaxation to achieve optimal or near-optimal solutions of the CSPP. The following is a brief outline of the contribution of this thesis. Lagrange Relaxation may or may not achieve optimal or near-optimal results on its own. Often, large duality gaps are present. We make a simple modification to Dijkstra’s algorithm that does not involve any additional computational work in order to generate an estimate of path time at every node.
We then use this information to constrain the network along a bisecting meridian. The combination of Lagrange Relaxation (LR) and a heuristic for filtering along the meridian provide an aggressive method for finding near-optimal solutions in a short time. Two network problems are studied in this work. The first is a Submarine Transit Path problem in which the transit field contains four sonar detectors at known locations, each with the same detection profile. The side constraint is the total transit time, with the submarine capable of 2 speeds. For the single-speed case, the initial LR duality gap may be as high as 30%. The first hybrid method uses a single centre meridian to constrain the network based on the unused time resource, and is able to produce solutions that are generally within 1% of optimal and always below 3%. Using the computation time for the initial Lagrange Relaxation as a baseline, the average computation time for the first hybrid method is about 30% to 50% higher, and the worst case CPU times are 2 to 4 times higher. The second problem is a random valued network from the literature. Edge costs, times, and lengths are uniform, randomly generated integers in a given range. Since the values given in the literature problems do not yield problems with a high duality gap, the values are varied and from a population of approximately 100,000 problems only the worst 200 from each set are chosen for study. These problems have an initial LR duality gap as high as 40%. A second hybrid method is developed, using values for the unused time resource and the lower bound values computed by Dijkstra’s algorithm as part of the LR method. The computed values are then used to position multiple constraining meridians in order to allow LR to find better solutions.
This second hybrid method is able to produce solutions that are generally within 0.1% of optimal, with computation times that are on average 2 times the initial Lagrange Relaxation time, and in the worst case only about 5 times higher. The best method for solving the Constrained Shortest Path Problem reported in the literature thus far is the LRE-A method of Carlyle et al. (2007), which uses Lagrange Relaxation for preprocessing followed by a bounded search using aggregate constraints. We replace Lagrange Relaxation with the second hybrid method and show that optimal solutions are produced for both network problems with computation times that are between one and two orders of magnitude faster than LRE-A. In addition, these hybrid methods combined with the bounded search are up to 2 orders of magnitude faster than the commercial CPlex package using a straightforward MILP formulation of the problem. Finally, the second hybrid method is used as a preprocessing step on both network problems, prior to running CPlex. This preprocessing reduces the network size sufficiently to allow CPlex to solve all cases to optimality up to 3 orders of magnitude faster than without this preprocessing, and up to an order of magnitude faster than using Lagrange Relaxation for preprocessing. Chapter 1 provides a review of the thesis and some terminology used. Chapter 2 reviews previous approaches to the CSPP, in particular the two current best methods. Chapter 3 applies Lagrange Relaxation to the Submarine Transit Path problem with 2 speeds, to provide a baseline for comparison. The problem is reduced to a single speed, which demonstrates the large duality gap problem possible with Lagrange Relaxation, and the first hybrid method is introduced.
Chapter 4 examines a grid network problem using randomly generated edge costs and weights, and introduces the second hybrid method. Chapter 5 then applies the second hybrid method to both network problems as a preprocessing step, using both CPlex and a bounded search method from the literature to solve to optimality. The conclusion of this thesis and directions for future work are discussed in Chapter 6.
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Shim, Sangho. "Large scale group network optimization." Diss., Atlanta, Ga. : Georgia Institute of Technology, 2009. http://hdl.handle.net/1853/31737.

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Thesis (Ph.D)--Industrial and Systems Engineering, Georgia Institute of Technology, 2010.
Committee Chair: Ellis L. Johnson; Committee Member: Brady Hunsaker; Committee Member: George Nemhauser; Committee Member: Jozef Siran; Committee Member: Shabbir Ahmed; Committee Member: William Cook. Part of the SMARTech Electronic Thesis and Dissertation Collection.
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Bulin, Johannes. "Large-scale time parallelization for molecular dynamics problems." Thesis, KTH, Numerisk analys, NA, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-129301.

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As modern supercomputers draw their power from the sheer number of cores, an efficient parallelization of programs is crucial for achieving good performance. When one tries to solve differential equations in parallel this is usually done by parallelizing the computation of one single time step. As the speedup of such parallelization schemes is usually limited, e.g. by the spatial size of the problem, additional parallelization in time may be useful to achieve better scalability. This thesis will introduce two well-known schemes for time-parallelization, namely the waveform relaxation method and the parareal algorithm. These methods are then applied to a molecular dynamics problem which is a useful test example as the number of required time steps is high while the number of unknowns is relatively low. Afterwards it is investigated how these methods can be adapted to large-scale computations.
Moderna superdatorer använder ett stort antal processorer för att uppnå hög prestanda. Därför är det nödvändigt att parallellisera sina program på ett effektivt sätt. När man löser differentialekvationer så brukar man parallellisera beräkningen av en enda tidspunkt. Speedupen av sådana program är ofta begränsad, till exempel av problemets storlek. Genom att använda ytterligare parallellisering i tid kan man uppnå bättre skalbarhet. Denna avhandling presenterar två välkända algoritmer för tidsparallellisering: waveform relaxation och parareal. Dessa metoder används för att lösa ett molekyldynamikproblem där tidsdomänen är stor jämförd med antalet obekanta. Slutligen undersöks några förbättringar för att möjliggöra storskaliga beräkningar.
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Bacarella, Daniele. "Distributed clustering algorithm for large scale clustering problems." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-212089.

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Clustering is a task which has got much attention in data mining. The task of finding subsets of objects sharing some sort of common attributes is applied in various fields such as biology, medicine, business and computer science. A document search engine for instance, takes advantage of the information obtained clustering the document database to return a result with relevant information to the query. Two main factors that make clustering a challenging task are the size of the dataset and the dimensionality of the objects to cluster. Sometimes the character of the object makes it difficult identify its attributes. This is the case of the image clustering. A common approach is comparing two images using their visual features like the colors or shapes they contain. However, sometimes they come along with textual information claiming to be sufficiently descriptive of the content (e.g. tags on web images). The purpose of this thesis work is to propose a text-based image clustering algorithm through the combined application of two techniques namely Minhash Locality Sensitive Hashing (MinHash LSH) and Frequent itemset Mining.
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Futamura, Natsuhiko. "Algorithms for large-scale problems in computational biology." Related electronic resource: Current Research at SU : database of SU dissertations, recent titles available full text, 2002. http://wwwlib.umi.com/cr/syr/main.

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Sohrabi, Babak. "Solving large scale distribution problems using heuristic algorithms." Thesis, Lancaster University, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.369654.

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Books on the topic "Large Scale Probleme"

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Fathi, Mahdi, Marzieh Khakifirooz, and Panos M. Pardalos, eds. Optimization in Large Scale Problems. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-28565-4.

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Tsurkov, Vladimir. Large-scale Optimization - Problems and Methods. Boston, MA: Springer US, 2001.

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Large-scale optimization: Problems and methods. Dordrecht: Kluwer Academic Publishers, 2001.

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Beilina, Larisa, and Yury V. Shestopalov, eds. Inverse Problems and Large-Scale Computations. Cham: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-00660-4.

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Tsurkov, Vladimir. Large-scale Optimization — Problems and Methods. Boston, MA: Springer US, 2001. http://dx.doi.org/10.1007/978-1-4757-3243-6.

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Spohn, Herbert. Large scale dynamics ofinteracting particles. Berlin: Springer-Verlag, 1991.

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Smith, Peter. Large scale models and large scale problems: The case of the health services. York: York University, Centre for Health Economics, 1993.

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Smith, Peter. Large scale models and large scale problems: The case of the health services. York: Centre for Health Economics, University of York, 1993.

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Stephen, Tindale, ed. Repowering communities: Small-scale solutions for large-scale energy problems. London: Earthscan, 2011.

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Ridyard, Beverley. Problems of a large-scale legacy system. Manchester: UMIST, 1997.

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Book chapters on the topic "Large Scale Probleme"

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Lee, Yusin, and James B. Orlin. "On Very Large Scale Assignment Problems." In Large Scale Optimization, 206–44. Boston, MA: Springer US, 1994. http://dx.doi.org/10.1007/978-1-4613-3632-7_12.

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Banagaaya, N., G. Alì, and W. H. A. Schilders. "Large Scale Problems." In Atlantis Studies in Scientific Computing in Electromagnetics, 71–82. Paris: Atlantis Press, 2016. http://dx.doi.org/10.2991/978-94-6239-189-5_5.

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Allower, Eugene L., and Kurt Georg. "Large Scale Problems." In Numerical Continuation Methods, 96–111. Berlin, Heidelberg: Springer Berlin Heidelberg, 1990. http://dx.doi.org/10.1007/978-3-642-61257-2_10.

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Chossat, Pascal, and Gérard Iooss. "Large-scale Effects." In The Couette-Taylor Problem, 167–205. New York, NY: Springer New York, 1994. http://dx.doi.org/10.1007/978-1-4612-4300-7_7.

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Kohut, Roman, Jiří Starý, Radim Blaheta, and Karel Krečmer. "Parallel Computing of Thermoelasticity Problems." In Large-Scale Scientific Computing, 671–78. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11666806_77.

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Tzaferopoulos, M. Ap, E. S. Mistakidis, C. D. Bisbos, and P. D. Panagiotopoulos. "On Two Algorithms for Nonconvex Nonsmooth Optimization Problems in Structural Mechanics." In Large Scale Optimization, 428–56. Boston, MA: Springer US, 1994. http://dx.doi.org/10.1007/978-1-4613-3632-7_21.

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Maranas, Costas D., and Christodoulos A. Floudas. "A Global Optimization Method For Weber’s Problem With Attraction And Repulsion." In Large Scale Optimization, 259–85. Boston, MA: Springer US, 1994. http://dx.doi.org/10.1007/978-1-4613-3632-7_14.

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Poore, Aubrey B., and Nenad Rijavec. "A Numerical Study of Some Data Association Problems Arising in Multitarget Tracking." In Large Scale Optimization, 339–61. Boston, MA: Springer US, 1994. http://dx.doi.org/10.1007/978-1-4613-3632-7_17.

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Spielberg, Kurt, and Uwe H. Suhl. "Solving Large-Scale Integer Optimization Problems." In Large Scale Scientific Computing, 271–86. Boston, MA: Birkhäuser Boston, 1987. http://dx.doi.org/10.1007/978-1-4684-6754-3_17.

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Fidanova, Stefka, and Mariya Durchova. "Ant Algorithm for Grid Scheduling Problem." In Large-Scale Scientific Computing, 405–12. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11666806_46.

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Conference papers on the topic "Large Scale Probleme"

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Iyer, Chander, Christopher Carothers, and Petros Drineas. "Randomized Sketching for Large-Scale Sparse Ridge Regression Problems." In 2016 7th Workshop on Latest Advances in Scalable Algorithms for Large-Scale Systems (ScalA). IEEE, 2016. http://dx.doi.org/10.1109/scala.2016.013.

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Ling, Andrew C., Deshanand P. Singh, and Stephen D. Brown. "Incremental placement for structured ASICs using the transportation problem." In 2007 IFIP International Conference on Very Large Scale Integration. IEEE, 2007. http://dx.doi.org/10.1109/vlsisoc.2007.4402493.

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Zubair, Mohammad, James Warner, and David Wagner. "Optimization of a Solver for Computational Materials and Structures Problems on NVIDIA Volta and AMD Instinct GPUs." In 2019 IEEE/ACM 10th Workshop on Latest Advances in Scalable Algorithms for Large-Scale Systems (ScalA). IEEE, 2019. http://dx.doi.org/10.1109/scala49573.2019.00007.

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Evgeny, R. Gafarov, Al Lazarev Aleksandr, and V. Zinovyev Aleksandr. "Algorithms for workforce assignment problem." In 2017 Tenth International Conference Management of Large-Scale System Development (MLSD). IEEE, 2017. http://dx.doi.org/10.1109/mlsd.2017.8109621.

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Ivanyuk, Vera, and Vladimir Soloviev. "Efficiency of Neural Networks in Forecasting Problems." In 2019 Twelfth International Conference "Management of large-scale system development" (MLSD). IEEE, 2019. http://dx.doi.org/10.1109/mlsd.2019.8911046.

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Akinfiev, Valery. "Dynamic Capacity Expansion Problem in Competitive Markets." In 2019 Twelfth International Conference "Management of large-scale system development" (MLSD). IEEE, 2019. http://dx.doi.org/10.1109/mlsd.2019.8911107.

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Savushkin, S. A. "Problems of Scenario Modeling of the Transport Complex." In 2020 13th International Conference Management of large-scale system development (MLSD). IEEE, 2020. http://dx.doi.org/10.1109/mlsd49919.2020.9247713.

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Ivanyuk, Vera, and Niyaz Abdikeev. "Practical Application of Neural Networks in Classification Problems." In 2020 13th International Conference Management of large-scale system development (MLSD). IEEE, 2020. http://dx.doi.org/10.1109/mlsd49919.2020.9247776.

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Gorelik, Victor, and Tatiana Zolotova. "Risk Management in Stochastic Problems of Stock Investment." In 2020 13th International Conference Management of large-scale system development (MLSD). IEEE, 2020. http://dx.doi.org/10.1109/mlsd49919.2020.9247801.

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Burkov, V. N., N. A. Korgin, and V. A. Sergeev. "Identification of Integrated Rating Mechanisms as Optimization Problem." In 2020 13th International Conference Management of large-scale system development (MLSD). IEEE, 2020. http://dx.doi.org/10.1109/mlsd49919.2020.9247638.

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Reports on the topic "Large Scale Probleme"

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GEORGE MASON UNIV FAIRFAX VA. Solving Large-Scale Combinatorial Optimization Problems. Fort Belvoir, VA: Defense Technical Information Center, August 1996. http://dx.doi.org/10.21236/ada327597.

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coleman, thomas f. Large Scale Computational Problems in Numerical Optimization. Office of Scientific and Technical Information (OSTI), July 2000. http://dx.doi.org/10.2172/1087647.

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Bondarenko, A. S., D. M. Bortz, and J. J. More. COPS: Large-scale nonlinearly constrained optimization problems. Office of Scientific and Technical Information (OSTI), February 2000. http://dx.doi.org/10.2172/751934.

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Penfield, Jr, Agarwal Paul, Dally Anant, Devadas William J., Knight Srinivas, Thomas F. Jr, F. T. Leighton, et al. Critical Problems in Very Large Scale Computer Systems. Fort Belvoir, VA: Defense Technical Information Center, September 1988. http://dx.doi.org/10.21236/ada202129.

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Munson, Todd S., Francisco Facchinei, Michael C. Ferris, Andreas Fischer, and Christian Kanzow. The Semismooth Algorithm for Large Scale Complementarity Problems. Fort Belvoir, VA: Defense Technical Information Center, June 1999. http://dx.doi.org/10.21236/ada375452.

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Carin, Lawrence. Fast Electromagnetic Solvers for Large-Scale Naval Scattering Problems. Fort Belvoir, VA: Defense Technical Information Center, September 2008. http://dx.doi.org/10.21236/ada486567.

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Finn, Gregory G. Routing and Addressing Problems in Large Metropolitan-Scale Internetworks. Fort Belvoir, VA: Defense Technical Information Center, March 1987. http://dx.doi.org/10.21236/ada180187.

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Zhaojun Bai, James Demmel, and Jack Dongarra. Toolboxes and Templates for Large Scale Linear Algebra Problems. Office of Scientific and Technical Information (OSTI), October 2002. http://dx.doi.org/10.2172/841936.

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Schnabel, Robert B., and Richard H. Byrd. Large Scale Optimization Methods with a Focus on Chemistry Problems. Fort Belvoir, VA: Defense Technical Information Center, November 2002. http://dx.doi.org/10.21236/ada418451.

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Pee, E. Y., and J. O. Royset. On Solving Large-Scale Finite Minimax Problems using Exponential Smoothing. Fort Belvoir, VA: Defense Technical Information Center, January 2010. http://dx.doi.org/10.21236/ada518716.

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