Literatura académica sobre el tema "Cluster approximation"
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Artículos de revistas sobre el tema "Cluster approximation"
Borisenko, O., V. Chelnokov y V. Kushnir. "Phenomenological Renormalization Group and Cluster Approximation". Ukrainian Journal of Physics 59, n.º 7 (julio de 2014): 655–62. http://dx.doi.org/10.15407/ujpe59.07.0655.
Texto completoNenashev, Vadim A., Igor G. Khanykov y Mikhail V. Kharinov. "A Model of Pixel and Superpixel Clustering for Object Detection". Journal of Imaging 8, n.º 10 (6 de octubre de 2022): 274. http://dx.doi.org/10.3390/jimaging8100274.
Texto completoPizio, O. A. y 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, n.º 1-2 (1 de febrero de 1991): 8–18. http://dx.doi.org/10.1515/zna-1991-1-203.
Texto completoZIEGLER, ALFRED. "FERMION CLUSTER APPROACH TO THE HUBBARD MODEL". International Journal of Modern Physics B 07, n.º 01n03 (enero de 1993): 601–4. http://dx.doi.org/10.1142/s0217979293001268.
Texto completoKats, Daniel y Frederick R. Manby. "Communication: The distinguishable cluster approximation". Journal of Chemical Physics 139, n.º 2 (14 de julio de 2013): 021102. http://dx.doi.org/10.1063/1.4813481.
Texto completoKÜMMEL, HERMANN G. "A BIOGRAPHY OF THE COUPLED CLUSTER METHOD". International Journal of Modern Physics B 17, n.º 28 (10 de noviembre de 2003): 5311–25. http://dx.doi.org/10.1142/s0217979203020442.
Texto completoDESSMARK, ANDERS, JESPER JANSSON, ANDRZEJ LINGAS, EVA-MARTA LUNDELL y MIA PERSSON. "ON THE APPROXIMABILITY OF MAXIMUM AND MINIMUM EDGE CLIQUE PARTITION PROBLEMS". International Journal of Foundations of Computer Science 18, n.º 02 (abril de 2007): 217–26. http://dx.doi.org/10.1142/s0129054107004656.
Texto completoTerletska, Hanna, Yi Zhang, Ka-Ming Tam, Tom Berlijn, Liviu Chioncel, N. Vidhyadhiraja y Mark Jarrell. "Systematic Quantum Cluster Typical Medium Method for the Study of Localization in Strongly Disordered Electronic Systems". Applied Sciences 8, n.º 12 (26 de noviembre de 2018): 2401. http://dx.doi.org/10.3390/app8122401.
Texto completoFreericks, James K. "Operator Relationship between Conventional Coupled Cluster and Unitary Coupled Cluster". Symmetry 14, n.º 3 (28 de febrero de 2022): 494. http://dx.doi.org/10.3390/sym14030494.
Texto completoBorgani, S., P. Coles y L. Moscardini. "Cluster correlations in the Zel'dovich approximation". Monthly Notices of the Royal Astronomical Society 271, n.º 1 (1 de noviembre de 1994): 223–32. http://dx.doi.org/10.1093/mnras/271.1.223.
Texto completoTesis sobre el tema "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.
Texto completoBenedikt, 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.
Texto completoDie 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.
Texto completoFilor, Stephan [Verfasser], Stefan [Akademischer Betreuer] [Gutachter] Kehrein y 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.
Texto completoFilor, Stephan Verfasser], Stefan [Akademischer Betreuer] [Gutachter] [Kehrein y 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.
Texto completoSen, 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.
Texto completoHeinen, 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.
Texto completoThis 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 y 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.
Texto completoxiii, 71 leaves : ill. ; 29 cm.
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.
Texto completoMadjet, 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.
Texto completoLibros sobre el tema "Cluster approximation"
Hierarchische Klassifikation einer Objektmenge: Ein globales Verfahren zur Approximation einer Distanzmatrix. Frankfurt am Main: P. Lang, 1986.
Buscar texto completoF, Pellman Todd, Shandarin Sergei F y United States. National Aeronautics and Space Administration., eds. Optimizing the Zel'dovich approximation. [Washington, D.C: National Aeronautics and Space Administration, 1995.
Buscar texto completoMagnus, Ekdahl. Approximations of Bayes classifiers for statistical learning of clusters. Linköping: Linköpings universitet, 2006.
Buscar texto completoF, Shandarin Sergei, Weinberg David Hal y 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.
Buscar texto completoUnited 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.
Buscar texto completoHoring, 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.
Texto completoKlingler-Vidra, Robyn. The Venture Capital State. Cornell University Press, 2018. http://dx.doi.org/10.7591/cornell/9781501723377.001.0001.
Texto completoLuschgy, Harald y Siegfried Graf. Foundations of Quantization for Probability Distributions. Springer London, Limited, 2007.
Buscar texto completoGraf, Siegfried y Harald Luschgy. Foundations of Quantization for Probability Distributions (Lecture Notes in Mathematics). Springer, 2000.
Buscar texto completoBilling, Gert D., ed. The Quantum Classical Theory. Oxford University Press, 2003. http://dx.doi.org/10.1093/oso/9780195146196.001.0001.
Texto completoCapítulos de libros sobre el tema "Cluster approximation"
Fazekas, P. "Cluster Gutzwiller Approximation". En Condensed Matter Theories, 279–90. Boston, MA: Springer US, 1991. http://dx.doi.org/10.1007/978-1-4615-3686-4_23.
Texto completoFotso, H., S. Yang, K. Chen, S. Pathak, J. Moreno, M. Jarrell, K. Mikelsons, E. Khatami y D. Galanakis. "Dynamical Cluster Approximation". En 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.
Texto completoSinha, Shriprakash. "Online Cluster Approximation via Inequality". En 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.
Texto completoBerger, André, Alexander Grigoriev y Andrej Winokurow. "A PTAS for the Cluster Editing Problem on Planar Graphs". En Approximation and Online Algorithms, 27–39. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-51741-4_3.
Texto completoSchäfer, Guido y Bernard G. Zweers. "Maximum Coverage with Cluster Constraints: An LP-Based Approximation Technique". En Approximation and Online Algorithms, 63–80. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-80879-2_5.
Texto completoZhang, Xiaoyan, Donglei Du, Gregory Gutin, Qiaoxia Ming y Jian Sun. "Approximation Algorithms for General Cluster Routing Problem". En Lecture Notes in Computer Science, 472–83. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58150-3_38.
Texto completoMohri, Tetsuo. "Glass Transition within the Cluster Variation Approximation". En Advanced Materials Research, 723–26. Stafa: Trans Tech Publications Ltd., 2007. http://dx.doi.org/10.4028/0-87849-463-4.723.
Texto completoWada, K., H. Tsuchinaga y T. Uchida. "The Crystal Growth Kinetics in The Cluster Approximation". En 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.
Texto completoGuo, Longkun, Bin Xing, Peihuang Huang y Xiaoyan Zhang. "Approximation Algorithms for the General Cluster Routing Problem". En 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.
Texto completoSanten, R. A. "Theory of Heterogeneous Catalytic Reactivity Using the Cluster Approximation". En 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.
Texto completoActas de conferencias sobre el tema "Cluster approximation"
Jarrell, M., A. Macridin, K. Mikelsons, D. G. S. P. Doluweera, J. E. Gubernatis, Adolfo Avella y Ferdinando Mancini. "The Dynamical Cluster Approximation with Quantum Monte Carlo Cluster Solvers". En 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.
Texto completoHoshino, Tetsuya, Akihiro Ida, Toshihiro Hanawa y Kengo Nakajima. "Load-Balancing-Aware Parallel Algorithms of H-Matrices with Adaptive Cross Approximation for GPUs". En 2018 IEEE International Conference on Cluster Computing (CLUSTER). IEEE, 2018. http://dx.doi.org/10.1109/cluster.2018.00016.
Texto completoAbdulah, Sameh, Hatem Ltaief, Ying Sun, Marc G. Genton y David E. Keyes. "Parallel Approximation of the Maximum Likelihood Estimation for the Prediction of Large-Scale Geostatistics Simulations". En 2018 IEEE International Conference on Cluster Computing (CLUSTER). IEEE, 2018. http://dx.doi.org/10.1109/cluster.2018.00089.
Texto completoPi, Minghong, Deren Li, Jianya Gong y Guokuan Li. "Fractal approximation coding based on cluster of range blocks". En International Symposium on Multispectral Image Processing, editado por Ji Zhou, Anil K. Jain, Tianxu Zhang, Yaoting Zhu, Mingyue Ding y Jianguo Liu. SPIE, 1998. http://dx.doi.org/10.1117/12.323566.
Texto completoNabavinejad, Seyed Morteza, Lena Mashayekhy y Sherief Reda. "ApproxDNN: Incentivizing DNN Approximation in Cloud". En 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.
Texto completoLi-Chuan Chen y Hyeong-Ah Choi. "Approximation algorithms for data distribution with load balancing of web servers". En Proceedings 2001 IEEE International Conference on Cluster Computing. IEEE, 2001. http://dx.doi.org/10.1109/clustr.2001.959988.
Texto completoMiyakoshi, Shohei y Yukinori Ohta. "Vriational-Cluster-Approximation Study of the Antiferromagnetic Topological Insulator States". En 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.
Texto completoBalduzzi, Giovanni, Arghya Chatterjee, Ying Wai Li, Peter W. Doak, Urs Haehner, Ed F. D'Azevedo, Thomas A. Maier y Thomas Schulthess. "Accelerating DCA++ (Dynamical Cluster Approximation) Scientific Application on the Summit Supercomputer". En 2019 28th International Conference on Parallel Architectures and Compilation Techniques (PACT). IEEE, 2019. http://dx.doi.org/10.1109/pact.2019.00041.
Texto completoAhmed, W. M. A., S. A. Fomenkov y S. V. Gaevoy. "Reducing Approximation Time of Cluster Workload by Using Simplified Hypergamma Distribution". En 2018 International Conference on Industrial Engineering, Applications and Manufacturing (ICIEAM). IEEE, 2018. http://dx.doi.org/10.1109/icieam.2018.8728879.
Texto completoSénéchal, David. "The Variational Cluster Approximation for Hubbard Models: Practical Implementation". En 2008 22nd High performance Computing Symposium (HPCS). IEEE, 2008. http://dx.doi.org/10.1109/hpcs.2008.18.
Texto completoInformes sobre el tema "Cluster approximation"
Martinez, R. F. y G. C. Osbourn. Extending applicability of cluster based pattern recognition with efficient approximation techniques. Office of Scientific and Technical Information (OSTI), marzo de 1997. http://dx.doi.org/10.2172/464175.
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