Academic literature on the topic 'Cluster approximation'
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Journal articles on the topic "Cluster approximation"
Borisenko, O., V. Chelnokov, and V. Kushnir. "Phenomenological Renormalization Group and Cluster Approximation." Ukrainian Journal of Physics 59, no. 7 (July 2014): 655–62. http://dx.doi.org/10.15407/ujpe59.07.0655.
Full textNenashev, Vadim A., Igor G. Khanykov, and Mikhail V. Kharinov. "A Model of Pixel and Superpixel Clustering for Object Detection." Journal of Imaging 8, no. 10 (October 6, 2022): 274. http://dx.doi.org/10.3390/jimaging8100274.
Full textPizio, O. A., and Z. B. Halytch. "Structural Properties of the Ion-Dipole Model of Electrolyte Solutions in the Bulk and Near a Charged Hard Wall.Application of the Truncated Optimized Cluster Series." Zeitschrift für Naturforschung A 46, no. 1-2 (February 1, 1991): 8–18. http://dx.doi.org/10.1515/zna-1991-1-203.
Full textZIEGLER, ALFRED. "FERMION CLUSTER APPROACH TO THE HUBBARD MODEL." International Journal of Modern Physics B 07, no. 01n03 (January 1993): 601–4. http://dx.doi.org/10.1142/s0217979293001268.
Full textKats, Daniel, and Frederick R. Manby. "Communication: The distinguishable cluster approximation." Journal of Chemical Physics 139, no. 2 (July 14, 2013): 021102. http://dx.doi.org/10.1063/1.4813481.
Full textKÜMMEL, HERMANN G. "A BIOGRAPHY OF THE COUPLED CLUSTER METHOD." International Journal of Modern Physics B 17, no. 28 (November 10, 2003): 5311–25. http://dx.doi.org/10.1142/s0217979203020442.
Full textDESSMARK, ANDERS, JESPER JANSSON, ANDRZEJ LINGAS, EVA-MARTA LUNDELL, and MIA PERSSON. "ON THE APPROXIMABILITY OF MAXIMUM AND MINIMUM EDGE CLIQUE PARTITION PROBLEMS." International Journal of Foundations of Computer Science 18, no. 02 (April 2007): 217–26. http://dx.doi.org/10.1142/s0129054107004656.
Full textTerletska, Hanna, Yi Zhang, Ka-Ming Tam, Tom Berlijn, Liviu Chioncel, N. Vidhyadhiraja, and Mark Jarrell. "Systematic Quantum Cluster Typical Medium Method for the Study of Localization in Strongly Disordered Electronic Systems." Applied Sciences 8, no. 12 (November 26, 2018): 2401. http://dx.doi.org/10.3390/app8122401.
Full textFreericks, James K. "Operator Relationship between Conventional Coupled Cluster and Unitary Coupled Cluster." Symmetry 14, no. 3 (February 28, 2022): 494. http://dx.doi.org/10.3390/sym14030494.
Full textBorgani, S., P. Coles, and L. Moscardini. "Cluster correlations in the Zel'dovich approximation." Monthly Notices of the Royal Astronomical Society 271, no. 1 (November 1, 1994): 223–32. http://dx.doi.org/10.1093/mnras/271.1.223.
Full textDissertations / Theses on the topic "Cluster approximation"
Zhang, Kai. "Kernel-based clustering and low rank approximation /." View abstract or full-text, 2008. http://library.ust.hk/cgi/db/thesis.pl?CSED%202008%20ZHANG.
Full textBenedikt, Udo. "Low-Rank Tensor Approximation in post Hartree-Fock Methods." Doctoral thesis, Universitätsbibliothek Chemnitz, 2014. http://nbn-resolving.de/urn:nbn:de:bsz:ch1-qucosa-133194.
Full textDie vorliegende Arbeit beschäftigt sich mit der Anwendung neuartiger Tensorzerlegungs- und Tensorrepesentationstechniken in hochgenauen post Hartree-Fock Methoden um das hohe Skalierungsverhalten dieser Verfahren mit steigender Systemgröße zu verringern und somit den "Fluch der Dimensionen" zu brechen. Nach einer vergleichenden Betrachtung verschiedener Representationsformate wird auf die Anwendung des "canonical polyadic" Formates (CP) detailliert eingegangen. Dabei stehen zunächst die Umwandlung eines normalen, indexbasierten Tensors in das CP Format (Tensorzerlegung) und eine Methode der Niedrigrang Approximation (Rangreduktion) für Zweielektronenintegrale in der AO Basis im Vordergrund. Die entscheidende Größe für die Anwendbarkeit ist dabei das Skalierungsverhalten das Ranges mit steigender System- und Basissatzgröße, da der Speicheraufwand und die Berechnungskosten für Tensormanipulationen im CP Format zwar nur noch linear von der Anzahl der Dimensionen des Tensors abhängen, allerdings auch mit der Expansionslänge (Rang) skalieren. Im Anschluss wird die AO-MO Transformation und der MP2 Algorithmus mit zerlegten Tensoren im CP Format diskutiert und erneut das Skalierungsverhalten mit steigender System- und Basissatzgröße untersucht. Abschließend wird ein Coupled-Cluster Algorithmus vorgestellt, welcher ausschließlich mit Tensoren in einer Niedrigrang CP Darstellung arbeitet. Dabei wird vor allem auf die sukzessive Tensorkontraktion während der iterativen Bestimmung der Amplituden eingegangen und die Fehlerfortpanzung durch Anwendung des Rangreduktions-Algorithmus analysiert. Abschließend wird die Komplexität des gesamten Verfahrens bewertet und Verbesserungsmöglichkeiten der Reduktionsprozedur aufgezeigt
Scherrer, Alexander. "Adaptive approximation of nonlinear minimization problems : the adaptive clustering method in inverse radiation therapy planning /." Aachen : Shaker, 2006. http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&doc_number=015733837&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA.
Full textFilor, Stephan [Verfasser], Stefan [Akademischer Betreuer] [Gutachter] Kehrein, and Andreas [Gutachter] Honecker. "A Variational Cluster Approximation for the Heisenberg Model / Stephan Filor ; Gutachter: Stefan Kehrein, Andreas Honecker ; Betreuer: Stefan Kehrein." Göttingen : Niedersächsische Staats- und Universitätsbibliothek Göttingen, 2017. http://d-nb.info/1129956415/34.
Full textFilor, Stephan Verfasser], Stefan [Akademischer Betreuer] [Gutachter] [Kehrein, and Andreas [Gutachter] Honecker. "A Variational Cluster Approximation for the Heisenberg Model / Stephan Filor ; Gutachter: Stefan Kehrein, Andreas Honecker ; Betreuer: Stefan Kehrein." Göttingen : Niedersächsische Staats- und Universitätsbibliothek Göttingen, 2017. http://d-nb.info/1129956415/34.
Full textSen, Asok Kumar. "Part I, traveling cluster approximation for uncorrelated amorphous systems ; Part II, influence of long-range forces on the wetting transition /." The Ohio State University, 1985. http://rave.ohiolink.edu/etdc/view?acc_num=osu1487260859496667.
Full textHeinen, Milton Roberto. "A connectionist approach for incremental function approximation and on-line tasks." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2011. http://hdl.handle.net/10183/29015.
Full textThis work proposes IGMN (standing for Incremental Gaussian Mixture Network), a new connectionist approach for incremental function approximation and real time tasks. It is inspired on recent theories about the brain, specially the Memory-Prediction Framework and the Constructivist Artificial Intelligence, which endows it with some unique features that are not present in most ANN models such as MLP, RBF and GRNN. Moreover, IGMN is based on strong statistical principles (Gaussian mixture models) and asymptotically converges to the optimal regression surface as more training data arrive. The main advantages of IGMN over other ANN models are: (i) IGMN learns incrementally using a single scan over the training data (each training pattern can be immediately used and discarded); (ii) it can produce reasonable estimates based on few training data; (iii) the learning process can proceed perpetually as new training data arrive (there is no separate phases for leaning and recalling); (iv) IGMN can handle the stability-plasticity dilemma and does not suffer from catastrophic interference; (v) the neural network topology is defined automatically and incrementally (new units added whenever is necessary); (vi) IGMN is not sensible to initialization conditions (in fact there is no random initialization/ decision in IGMN); (vii) the same neural network can be used to solve both forward and inverse problems (the information flow is bidirectional) even in regions where the target data are multi-valued; and (viii) IGMN can provide the confidence levels of its estimates. Another relevant contribution of this thesis is the use of IGMN in some important state-of-the-art machine learning and robotic tasks such as model identification, incremental concept formation, reinforcement learning, robotic mapping and time series prediction. In fact, the efficiency of IGMN and its representational power expand the set of potential tasks in which the neural networks can be applied, thus opening new research directions in which important contributions can be made. Through several experiments using the proposed model it is demonstrated that IGMN is also robust to overfitting, does not require fine-tunning of its configuration parameters and has a very good computational performance, thus allowing its use in real time control applications. Therefore, IGMN is a very useful machine learning tool for incremental function approximation and on-line prediction.
Choudhury, Salimur Rashid, and University of Lethbridge Faculty of Arts and Science. "Approximation algorithms for a graph-cut problem with applications to a clustering problem in bioinformatics." Thesis, Lethbridge, Alta. : University of Lethbridge, Deptartment of Mathematics and Computer Science, 2008, 2008. http://hdl.handle.net/10133/774.
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Nachaoui, Mourad. "Étude théorique et approximation numérique d'un problème inverse de transfert de la chaleur." Phd thesis, Université de Nantes, 2011. http://tel.archives-ouvertes.fr/tel-00678032.
Full textMadjet, Mohamed El-Amine. "Etude théorique des propriétés électroniques et dynamiques des agrégats métalliques simples dans le modèle du jellium." Université Joseph Fourier (Grenoble), 1994. http://www.theses.fr/1994GRE10094.
Full textBooks on the topic "Cluster approximation"
Hierarchische Klassifikation einer Objektmenge: Ein globales Verfahren zur Approximation einer Distanzmatrix. Frankfurt am Main: P. Lang, 1986.
Find full textF, Pellman Todd, Shandarin Sergei F, and United States. National Aeronautics and Space Administration., eds. Optimizing the Zel'dovich approximation. [Washington, D.C: National Aeronautics and Space Administration, 1995.
Find full textMagnus, Ekdahl. Approximations of Bayes classifiers for statistical learning of clusters. Linköping: Linköpings universitet, 2006.
Find full textF, Shandarin Sergei, Weinberg David Hal, and United States. National Aeronautics and Space Administration., eds. A test of the adhesion approximation for gravitational clustering. [Washington, D.C: National Aeronautics and Space Administration, 1995.
Find full textUnited States. National Aeronautics and Space Administration., ed. Comparison of dynamical approximation schemes for non-linear gravitational clustering. [Washington, D.C: National Aeronautics and Space Administration, 1995.
Find full textHoring, Norman J. Morgenstern. Non-Equilibrium Green’s Functions: Variational Relations and Approximations for Particle Interactions. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198791942.003.0009.
Full textKlingler-Vidra, Robyn. The Venture Capital State. Cornell University Press, 2018. http://dx.doi.org/10.7591/cornell/9781501723377.001.0001.
Full textLuschgy, Harald, and Siegfried Graf. Foundations of Quantization for Probability Distributions. Springer London, Limited, 2007.
Find full textGraf, Siegfried, and Harald Luschgy. Foundations of Quantization for Probability Distributions (Lecture Notes in Mathematics). Springer, 2000.
Find full textBilling, Gert D., ed. The Quantum Classical Theory. Oxford University Press, 2003. http://dx.doi.org/10.1093/oso/9780195146196.001.0001.
Full textBook chapters on the topic "Cluster approximation"
Fazekas, P. "Cluster Gutzwiller Approximation." In Condensed Matter Theories, 279–90. Boston, MA: Springer US, 1991. http://dx.doi.org/10.1007/978-1-4615-3686-4_23.
Full textFotso, H., S. Yang, K. Chen, S. Pathak, J. Moreno, M. Jarrell, K. Mikelsons, E. Khatami, and D. Galanakis. "Dynamical Cluster Approximation." In Springer Series in Solid-State Sciences, 271–302. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21831-6_9.
Full textSinha, Shriprakash. "Online Cluster Approximation via Inequality." In IFIP Advances in Information and Communication Technology, 176–81. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33412-2_18.
Full textBerger, André, Alexander Grigoriev, and Andrej Winokurow. "A PTAS for the Cluster Editing Problem on Planar Graphs." In Approximation and Online Algorithms, 27–39. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-51741-4_3.
Full textSchäfer, Guido, and Bernard G. Zweers. "Maximum Coverage with Cluster Constraints: An LP-Based Approximation Technique." In Approximation and Online Algorithms, 63–80. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-80879-2_5.
Full textZhang, Xiaoyan, Donglei Du, Gregory Gutin, Qiaoxia Ming, and Jian Sun. "Approximation Algorithms for General Cluster Routing Problem." In Lecture Notes in Computer Science, 472–83. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58150-3_38.
Full textMohri, Tetsuo. "Glass Transition within the Cluster Variation Approximation." In Advanced Materials Research, 723–26. Stafa: Trans Tech Publications Ltd., 2007. http://dx.doi.org/10.4028/0-87849-463-4.723.
Full textWada, K., H. Tsuchinaga, and T. Uchida. "The Crystal Growth Kinetics in The Cluster Approximation." In Dynamics of Ordering Processes in Condensed Matter, 29–33. Boston, MA: Springer US, 1988. http://dx.doi.org/10.1007/978-1-4613-1019-8_6.
Full textGuo, Longkun, Bin Xing, Peihuang Huang, and Xiaoyan Zhang. "Approximation Algorithms for the General Cluster Routing Problem." In Parallel and Distributed Computing, Applications and Technologies, 264–73. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-69244-5_23.
Full textSanten, R. A. "Theory of Heterogeneous Catalytic Reactivity Using the Cluster Approximation." In Chemisorption and Reactivity on Supported Clusters and Thin Films, 371–93. Dordrecht: Springer Netherlands, 1997. http://dx.doi.org/10.1007/978-94-015-8911-6_13.
Full textConference papers on the topic "Cluster approximation"
Jarrell, M., A. Macridin, K. Mikelsons, D. G. S. P. Doluweera, J. E. Gubernatis, Adolfo Avella, and Ferdinando Mancini. "The Dynamical Cluster Approximation with Quantum Monte Carlo Cluster Solvers." In LECTURES ON THE PHYSICS OF STRONGLY CORRELATED SYSTEMS XII: Twelfth Training Course in the Physics of Strongly Correlated Systems. AIP, 2008. http://dx.doi.org/10.1063/1.2940445.
Full textHoshino, Tetsuya, Akihiro Ida, Toshihiro Hanawa, and Kengo Nakajima. "Load-Balancing-Aware Parallel Algorithms of H-Matrices with Adaptive Cross Approximation for GPUs." In 2018 IEEE International Conference on Cluster Computing (CLUSTER). IEEE, 2018. http://dx.doi.org/10.1109/cluster.2018.00016.
Full textAbdulah, Sameh, Hatem Ltaief, Ying Sun, Marc G. Genton, and David E. Keyes. "Parallel Approximation of the Maximum Likelihood Estimation for the Prediction of Large-Scale Geostatistics Simulations." In 2018 IEEE International Conference on Cluster Computing (CLUSTER). IEEE, 2018. http://dx.doi.org/10.1109/cluster.2018.00089.
Full textPi, Minghong, Deren Li, Jianya Gong, and Guokuan Li. "Fractal approximation coding based on cluster of range blocks." In International Symposium on Multispectral Image Processing, edited by Ji Zhou, Anil K. Jain, Tianxu Zhang, Yaoting Zhu, Mingyue Ding, and Jianguo Liu. SPIE, 1998. http://dx.doi.org/10.1117/12.323566.
Full textNabavinejad, Seyed Morteza, Lena Mashayekhy, and Sherief Reda. "ApproxDNN: Incentivizing DNN Approximation in Cloud." In 2020 20th IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing (CCGRID). IEEE, 2020. http://dx.doi.org/10.1109/ccgrid49817.2020.00-29.
Full textLi-Chuan Chen and Hyeong-Ah Choi. "Approximation algorithms for data distribution with load balancing of web servers." In Proceedings 2001 IEEE International Conference on Cluster Computing. IEEE, 2001. http://dx.doi.org/10.1109/clustr.2001.959988.
Full textMiyakoshi, Shohei, and Yukinori Ohta. "Vriational-Cluster-Approximation Study of the Antiferromagnetic Topological Insulator States." In Proceedings of the International Conference on Strongly Correlated Electron Systems (SCES2013). Journal of the Physical Society of Japan, 2014. http://dx.doi.org/10.7566/jpscp.3.016011.
Full textBalduzzi, Giovanni, Arghya Chatterjee, Ying Wai Li, Peter W. Doak, Urs Haehner, Ed F. D'Azevedo, Thomas A. Maier, and Thomas Schulthess. "Accelerating DCA++ (Dynamical Cluster Approximation) Scientific Application on the Summit Supercomputer." In 2019 28th International Conference on Parallel Architectures and Compilation Techniques (PACT). IEEE, 2019. http://dx.doi.org/10.1109/pact.2019.00041.
Full textAhmed, W. M. A., S. A. Fomenkov, and S. V. Gaevoy. "Reducing Approximation Time of Cluster Workload by Using Simplified Hypergamma Distribution." In 2018 International Conference on Industrial Engineering, Applications and Manufacturing (ICIEAM). IEEE, 2018. http://dx.doi.org/10.1109/icieam.2018.8728879.
Full textSénéchal, David. "The Variational Cluster Approximation for Hubbard Models: Practical Implementation." In 2008 22nd High performance Computing Symposium (HPCS). IEEE, 2008. http://dx.doi.org/10.1109/hpcs.2008.18.
Full textReports on the topic "Cluster approximation"
Martinez, R. F., and G. C. Osbourn. Extending applicability of cluster based pattern recognition with efficient approximation techniques. Office of Scientific and Technical Information (OSTI), March 1997. http://dx.doi.org/10.2172/464175.
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