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Relevant bibliographies by topics / Matrix methods

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Academic literature on the topic 'Matrix methods'

Author: Grafiati

Published: 4 June 2021

Last updated: 30 July 2024

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Journal articles on the topic "Matrix methods"

1

Davidson, Ernest R. "Super-matrix methods." Computer Physics Communications 53, no. 1-3 (May 1989): 49–60. http://dx.doi.org/10.1016/0010-4655(89)90147-1.

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Zito, Richard. "System Safety Matrix Methods." Journal of System Safety 52, no. 3 (January 1, 2017): 13–21. http://dx.doi.org/10.56094/jss.v52i3.119.

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The analysis of networks is a common feature in system safety analysis. These networks may range from electronic circuits to software flowcharts to maps of land, air, sea and communications traffic. Matrix methods are the natural tool for the analysis of these networks, and the object of this paper is to describe the basics of matrix methods in the context of three common problems encountered by systems safety engineers: the Bent Pin Problem, the Sneak Circuit Problem and the Analysis of Software Logic. Comparison of these analyses will reveal deep connections between these problems and suggest directions for future research.

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Hassan, M., F. Hammouda, and A. Asser. "MATRIX METHODS IN MAGNETOHYDRODYNAMICS." International Conference on Applied Mechanics and Mechanical Engineering 2, no. 2 (May 1, 1986): 213–20. http://dx.doi.org/10.21608/amme.1986.59015.

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Jain, Nitin A., Kushal D. Murthy, and Hamsapriye. "Matrix methods for finding." International Journal of Mathematical Education in Science and Technology 45, no. 5 (January 21, 2014): 754–62. http://dx.doi.org/10.1080/0020739x.2013.877607.

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Ridgely, Pat. "Matrix methods and circuits." American Journal of Physics 53, no. 11 (November 1985): 1038. http://dx.doi.org/10.1119/1.14028.

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Özarslan, H. S., and T. Ari. "Absolute matrix summability methods." Applied Mathematics Letters 24, no. 12 (December 2011): 2102–6. http://dx.doi.org/10.1016/j.aml.2011.06.006.

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Abraham, Leah C., J. Fred Dice, Patrick F. Finn, Nicholas T. Mesires, Kyongbum Lee, and David L. Kaplan. "Extracellular matrix remodeling—Methods to quantify cell–matrix interactions." Biomaterials 28, no. 2 (January 2007): 151–61. http://dx.doi.org/10.1016/j.biomaterials.2006.07.001.

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Dikshit, H. P., and J. A. Fridy. "Some absolutely effective product methods." International Journal of Mathematics and Mathematical Sciences 15, no. 4 (1992): 641–51. http://dx.doi.org/10.1155/s0161171292000851.

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It is proved that the product methodA(C,1), where(C,1)is the Cesàro arithmetic mean matrix, is totally effective under certain conditions concerning the matrixA. This general result is applied to study absolute Nörlund summability of Fourier series and other related series.

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Tian, Li-Ping, Lizhi Liu, and Fang-Xiang Wu. "Matrix Decomposition Methods in Bioinformatics." Current Bioinformatics 8, no. 2 (February 1, 2013): 259–66. http://dx.doi.org/10.2174/1574893611308020014.

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Zamarashkin, N. L., I. V. Oseledets, and E. E. Tyrtyshnikov. "New Applications of Matrix Methods." Computational Mathematics and Mathematical Physics 61, no. 5 (May 2021): 669–73. http://dx.doi.org/10.1134/s0965542521050183.

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Dissertations / Theses on the topic "Matrix methods"

1

Ramachandran, Karuna. "Matrix geometric methods in priority queues." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/nq28517.pdf.

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Parambath, Shameem Ahamed Puthiya. "Matrix Factorization Methods for Recommender Systems." Thesis, Umeå universitet, Institutionen för datavetenskap, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-74181.

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This thesis is a comprehensive study of matrix factorization methods used in recommender systems. We study and analyze the existing models, specifically probabilistic models used in conjunction with matrix factorization methods, for recommender systems from a machine learning perspective. We implement two different methods suggested in scientific literature and conduct experiments on the prediction accuracy of the models on the Yahoo! Movies rating dataset.

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Kressner, Daniel. "Numerical Methods for Structured Matrix Factorizations." [S.l. : s.n.], 2001. http://www.bsz-bw.de/cgi-bin/xvms.cgi?SWB10047770.

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Bright, Leslie William. "Matrix-analytic methods in applied probability /." Title page, table of contents and abstract only, 1996. http://web4.library.adelaide.edu.au/theses/09PH/09phb855.pdf.

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CATALANO, COSTANZA. "Probabilisticts methods for primitive matrix semigroups." Doctoral thesis, Gran Sasso Science Institute, 2019. http://hdl.handle.net/20.500.12571/9726.

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We study the primitivity property of finitely generated matrix semigroups from a probabilistic point of view and via two approaches. A finite set of nonnegative matrices is called primitive if there exists a product of these matrices that is entrywise positive; the length of the shortest of these products is called the exponent of the set. We firstly study the primitivity property of random matrix sets, by rephrasing it in terms of random labeled directed multigraphs. We extend classical models of random graph theory to labeled directed multigraphs and we show that these random models admit a sharp threshold with respect to the primitivity property. We also show that when primitive, these models have low exponent with high probability. We then prove that they exhibit the same threshold behavior with respect to the property of being column-primitive and we use these results for studying the 2-directability property and 3-directability property of random nondeterministic finite state automata (NDFAs). In particular, we show that an NDFA generated according to the uniform distribution admits a short 2-directing word and a short 3-directing word with high probability. Inspired by the probabilistic method, we then present a more involved randomized construction that generates primitive sets with large exponent with nonvanishing probability and we use our findings for exhibiting new families of synchronizing finite state automata with quadratic reset threshold. Secondly, we embed the primitivity problem in a probabilistic game framework in order to study its properties. We develop a tool, that we call the synchronizing probability function for primitive sets of matrices, that captures the speed at which a primitive set reaches its first positive product thus representing the convergence of the primitivity process, and we show that this function must increase regularly in some sense. We then show that this function can be used for efficiently approximating the exponent of any given primitive set made of matrices having neither zero-rows nor zero-columns (NZ-matrices) and for (potentially) improving the upper bound on the maximal exponent among the primitive sets of NZ-matrices. Finally, we prove that in a primitive semigroup of matrix size n × n, for all k ≤ √n the length of the shortest product having a row or a column with k positive entries is linear in n, question that is still open for synchronizing automata.

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Terkhova, Karina. "Capacitance matrix preconditioning." Thesis, University of Oxford, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.244593.

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Manukyan, Narine. "Improved Methods for Cluster Identification and Visualization." ScholarWorks @ UVM, 2011. http://scholarworks.uvm.edu/graddis/147.

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Self-organizing maps (SOMs) are self-organized projections of high dimensional data onto a low, typically two dimensional (2D), map wherein vector similarity is implicitly translated into topological closeness in the 2D projection. They are thus used for clustering and visualization of high dimensional data. However it is often challenging to interpret the results due to drawbacks of currently used methods for identifying and visualizing cluster boundaries in the resulting feature maps. In this thesis we introduce a new phase to the SOM that we refer to as the Cluster Reinforcement (CR) phase. The CR phase amplifies within-cluster similarity with the consequence that cluster boundaries become much more evident. We also define a new Boundary (B) matrix that makes cluster boundaries easy to visualize, can be thresholded at various levels to make cluster hierarchies apparent, and can be overlain directly onto maps of component planes (something that was not possible with previous methods). The combination of the SOM, CR phase and B-matrix comprise an automated method for improved identification and informative visualization of clusters in high dimensional data. We demonstrate these methods on three data sets: the classic 13- dimensional binary-valued “animal” benchmark test, actual 60-dimensional binaryvalued phonetic word clustering problem, and 3-dimensional real-valued geographic data clustering related to fuel efficiency of vehicle choice.

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Lamond, Bernard Fernand. "Matrix methods in queueing and dynamic programming." Thesis, University of British Columbia, 1985. http://hdl.handle.net/2429/27124.

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We investigate some modern matrix methods for the solution of finite state stochastic models with an infinite time horizon. Markov and semi-Markov decision processes and finite queues in tandem with exponential service times are considered. The methods are based on the Drazin generalized inverse and use matrix decomposition. Unlike the related Jordan canonical form, the decompositions considered are numerically tractable and use real arithmetic when the original matrix has real entries. The spectral structure of the transition matrix of a Markov chain, deduced from non-negative matrix theory, provides a decomposition from which the limiting and deviation matrices are directly obtained. The matrix decomposition approach to the solution of Markov reward processes provides a new, simple derivation of the Laurent expansion of the resolvent. Many other basic results of dynamic programming are easily derived in a similar fashion and the extension to semi-Markov decision processes is straightforward. Further, numerical algorithms for matrix decomposition can be used efficiently in the policy iteration method, for evaluating the terms of the Laurent series. The problem of finding the stationary distribution of a system with two finite queues in tandem, when the service times have an exponential distribution, can also be expressed in matrix form. Two numerical methods, one iterative and one using matrix decomposition, are reviewed for computing the stationary probabilities. Job-local-balance is used to derive some bounds on the call congestion. A numerical investigation of the bounds is included. It suggests that the bounds are insensitive to the distribution of the service times.
Business, Sauder School of
Graduate

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Haider, Shahid Abbas. "Systolic arrays for the matrix iterative methods." Thesis, Loughborough University, 1993. https://dspace.lboro.ac.uk/2134/28173.

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The systolic array research was pioneered by H. T. Kung and C. E. Leiserson. Systolic arrays are special purpose synchronous architectures consisting of simple, regular and modular processors which are regularly interconnected to form an array. Systolic arrays are well suited for computational bound problems in Linear Algebra. In this thesis, the numerical problems, especially iterative algorithms are chosen and implemented on the linear systolic array. same.

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Roberts, Jeremy Alyn. "Advanced response matrix methods for full core analysis." Thesis, Massachusetts Institute of Technology, 2014. http://hdl.handle.net/1721.1/87490.

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Thesis: Ph. D., Massachusetts Institute of Technology, Department of Nuclear Science and Engineering, 2014.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 195-200).
Modeling full reactor cores with high fidelity transport methods is a difficult task, requiring the largest computers available today. This thesis presents work on an alternative approach using the eigenvalue response matrix method (ERMM). The basic idea of ERMM is to decompose a reactor spatially into local "nodes." Each node represents an independent fixed source transport problem, and the nodes are linked via approximate boundary conditions to reconstruct the global problem using potentially many fewer explicit unknowns than a direct fine mesh solution. This thesis addresses several outstanding issues related to the ERMM based on deterministic transport. In particular, advanced transport solvers were studied for application to the relatively small and frequently repeated problems characteristic of response function generation. This includes development of preconditioners based on diffusion for use in multigroup Krylov linear solvers. These new solver combinations are up to an order of magnitude faster than competing algorithms. Additionally, orthogonal bases for space, angle, and energy variables were investigated. For the spatial variable, a new basis set that incorporates a shape function characteristic of pin assemblies was found to reduce significantly the error in representing boundary currents. For the angular variable, it was shown that bases that conserve the partial current at a boundary perform very well, particularly for low orders. For the deterministic transport used in this work, such bases require use of specialized angular quadratures. In the energy variable, it was found that an orthogonal basis constructed using a representative energy spectrum provides an accurate alternative to few group calculations. Finally, a parallel ERMM code Serment was developed, incorporating the transport and basis development along with several new algorithms for solving the response matrix equations, including variants of Picard iteration, Steffensen's method, and Newton's method. Based on results from several benchmark models, it was found that an accelerated Picard iteration provides the best performance, but Newton's method may be more robust. Furthermore, initial scoping studies demonstrated good scaling on an [omicron](100) processor machine.
by Jeremy Alyn Roberts.
Ph. D.

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Books on the topic "Matrix methods"

1

Guedj, R. A. Matrix methods. Chilton: Rutherford Appleton Laboratory, 1998.

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Thomas, King J., ed. Matrix methods andapplications. Englewood Cliffs: Prentice Hall, 1988.

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Antosik, Piotr, and Charles Swartz. Matrix Methods in Analysis. Berlin, Heidelberg: Springer Berlin Heidelberg, 1985. http://dx.doi.org/10.1007/bfb0072264.

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Groetsch, C. W. Matrix methods and applications. Englewood Cliffs, N.J: Prentice Hall, 1988.

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Antosik, Piotr. Matrix methods in analysis. Berlin: Springer-Verlag, 1985.

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Kanchi, M. B. Matrix methods of structural analysis. 2nd ed. New York: J. Wiley, 1993.

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Nagarajan, Praveen. Matrix Methods of Structural Analysis. Boca Raton : Taylor & Francis, a CRC title, part of the Taylor &: CRC Press, 2018. http://dx.doi.org/10.1201/9781351210324.

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Björck, Åke. Numerical Methods in Matrix Computations. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-05089-8.

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He, Qi-Ming. Fundamentals of Matrix-Analytic Methods. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4614-7330-5.

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Kloos, Gerhard. Matrix methods for optical layout. Bellingham, Wash: SPIE, 2007.

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Book chapters on the topic "Matrix methods"

1

Lekner, John. "Matrix methods." In Theory of Reflection of Electromagnetic and Particle Waves, 221–40. Dordrecht: Springer Netherlands, 1987. http://dx.doi.org/10.1007/978-94-015-7748-9_12.

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Rao, J. S. "Matrix Methods." In History of Mechanism and Machine Science, 115–39. Dordrecht: Springer Netherlands, 2011. http://dx.doi.org/10.1007/978-94-007-1165-5_12.

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Širca, Simon, and Martin Horvat. "Matrix Methods." In Computational Methods in Physics, 121–86. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-78619-3_3.

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Širca, Simon, and Martin Horvat. "Matrix Methods." In Graduate Texts in Physics, 109–57. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-32478-9_3.

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Larner, A. J. "Graphing Methods." In The 2x2 Matrix, 187–203. Cham: Springer International Publishing, 2024. http://dx.doi.org/10.1007/978-3-031-47194-0_7.

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Varga, Richard S. "Semi-Iterative Methods." In Matrix Iterative Analysis, 149–82. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-05156-2_5.

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Helmke, Uwe, and John B. Moore. "Matrix Eigenvalue Methods." In Communications and Control Engineering, 1–42. London: Springer London, 1994. http://dx.doi.org/10.1007/978-1-4471-3467-1_1.

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McCalla, William J. "Sparse Matrix Methods." In The Kluwer International Series in Engineering and Computer Science, 37–51. Boston, MA: Springer US, 1988. http://dx.doi.org/10.1007/978-1-4613-2011-1_3.

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Grassmann, Winfried K., and David A. Stanford. "Matrix Analytic Methods." In International Series in Operations Research & Management Science, 153–203. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/978-1-4757-4828-4_6.

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Rao, J. S. "Transfer Matrix Methods." In History of Mechanism and Machine Science, 253–67. Dordrecht: Springer Netherlands, 2011. http://dx.doi.org/10.1007/978-94-007-1165-5_15.

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Conference papers on the topic "Matrix methods"

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Mojica, Oscar F., and Reynam Pestana. "Solving one-step wave extrapolation matrix method using Krylov methods for matrix functions." In First International Meeting for Applied Geoscience & Energy. Society of Exploration Geophysicists, 2021. http://dx.doi.org/10.1190/segam2021-3595008.1.

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Carmon, Yair, Yujia Jin, Aaron Sidford, and Kevin Tian. "Coordinate Methods for Matrix Games." In 2020 IEEE 61st Annual Symposium on Foundations of Computer Science (FOCS). IEEE, 2020. http://dx.doi.org/10.1109/focs46700.2020.00035.

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Wiranto, Ferry, and I. Made Tirta. "Information Retrieval Using Matrix Methods." In International Conference on Mathematics, Geometry, Statistics, and Computation (IC-MaGeStiC 2021). Paris, France: Atlantis Press, 2022. http://dx.doi.org/10.2991/acsr.k.220202.032.

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Hanson, Kenneth M. "Probing the covariance matrix." In Bayesian Inference and Maximum Entropy Methods In Science and Engineering. AIP, 2006. http://dx.doi.org/10.1063/1.2423282.

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Barolle, V., P. Balondrade, A. Badon, K. Irsch, A. C. Boccara, M. Fink, and A. Aubry. "Distortion matrix concept for deep imaging in optical microscopy." In Adaptive Optics: Analysis, Methods & Systems. Washington, D.C.: OSA, 2020. http://dx.doi.org/10.1364/aoms.2020.of2b.6.

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Li, Sheng-Kun, and Ting-Zhu Huang. "Two Matrix Iterative Methods for Solving General Coupled Matrix Equations." In 2010 International Conference on Computational and Information Sciences (ICCIS). IEEE, 2010. http://dx.doi.org/10.1109/iccis.2010.100.

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Černá, Dana, and Václav Finěk. "Adaptive wavelet methods - Matrix-vector multiplication." In INTERNATIONAL CONFERENCE OF COMPUTATIONAL METHODS IN SCIENCES AND ENGINEERING 2009: (ICCMSE 2009). AIP, 2012. http://dx.doi.org/10.1063/1.4771823.

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Andersen, David R., Michael Fischer, and Jean-Pierre Véran. "Building an Open Loop Interaction Matrix for VOLT." In Adaptive Optics: Methods, Analysis and Applications. Washington, D.C.: OSA, 2009. http://dx.doi.org/10.1364/aopt.2009.aotha4.

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Sokolovs, Alvis, Ilja Galkin, Oskars Krievs, and Juhan Laugis. "Simulation Methods for 3x3 Matrix Converter." In 2006 12th International Power Electronics and Motion Control Conference. IEEE, 2006. http://dx.doi.org/10.1109/epepemc.2006.283262.

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Sokolovs, Alvis, Ilja Galkin, Oskars Krievs, and Juhan Laugis. "Simulation Methods for 3x3 Matrix Converter." In 2006 12th International Power Electronics and Motion Control Conference. IEEE, 2006. http://dx.doi.org/10.1109/epepemc.2006.4778501.

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Reports on the topic "Matrix methods"

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Caswell, Hal. Matrix Methods for Population Analysis. Fort Belvoir, VA: Defense Technical Information Center, January 1997. http://dx.doi.org/10.21236/ada330118.

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Kyrillidis, Anastasios, and Volkan Cevher. Matrix Recipes for Hard Thresholding Methods. Fort Belvoir, VA: Defense Technical Information Center, November 2012. http://dx.doi.org/10.21236/ada585818.

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Thompson, Ian J., Paraskevi Dimitriou, Richard J. DeBoer, Satoshi Kunieda, Mark Paris, Ian Thompson, and Andrej Trkov. Widening the Scope of R-matrix Methods. Office of Scientific and Technical Information (OSTI), March 2016. http://dx.doi.org/10.2172/1342005.

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Jessup, E. R. Numerical methods on some structured matrix algebra problems. Office of Scientific and Technical Information (OSTI), June 1996. http://dx.doi.org/10.2172/244504.

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Athey, Susan, Mohsen Bayati, Nikolay Doudchenko, Guido Imbens, and Khashayar Khosravi. Matrix Completion Methods for Causal Panel Data Models. Cambridge, MA: National Bureau of Economic Research, October 2018. http://dx.doi.org/10.3386/w25132.

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Browning, Tyson R. Design Structure Matrix (DSM) Methods and Applications for Naval Ship Design. Fort Belvoir, VA: Defense Technical Information Center, May 2013. http://dx.doi.org/10.21236/ada587357.

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Ortiz, M. Analytical Methods of Approximating the Fisher Information Matrix for the Lognormal Distribution. Office of Scientific and Technical Information (OSTI), August 2018. http://dx.doi.org/10.2172/1557955.

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Telfeyan, Katherine Christina, Stuart Douglas Ware, Paul William Reimus, and Kay Hanson Birdsell. Comparison of Experimental Methods for Estimating Matrix Diffusion Coefficients for Contaminant Transport Modeling. Office of Scientific and Technical Information (OSTI), November 2017. http://dx.doi.org/10.2172/1407916.

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Karpur, Prasanna. Nondestructive Methods for Evaluating Damage Evolution and Material Behavior in Metal Matrix Composites. Fort Belvoir, VA: Defense Technical Information Center, February 1997. http://dx.doi.org/10.21236/ada329643.

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Jarvis, K., and I. Jarvis. High-dissolved solids and matrix effects in ICP-MS: methods towards improving determination limits for geoanalysis. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 1993. http://dx.doi.org/10.4095/193257.

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