Academic literature on the topic 'Scale decomposition'
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Journal articles on the topic "Scale decomposition"
Mendez, M. A., M. Balabane, and J. M. Buchlin. "Multi-scale proper orthogonal decomposition of complex fluid flows." Journal of Fluid Mechanics 870 (May 15, 2019): 988–1036. http://dx.doi.org/10.1017/jfm.2019.212.
Full textSchmidt, Marie Foged, Martin Benning, and Carola-Bibiane Schönlieb. "Inverse scale space decomposition." Inverse Problems 34, no. 4 (March 13, 2018): 045008. http://dx.doi.org/10.1088/1361-6420/aab0ae.
Full textAluie, Hussein. "Scale decomposition in compressible turbulence." Physica D: Nonlinear Phenomena 247, no. 1 (March 2013): 54–65. http://dx.doi.org/10.1016/j.physd.2012.12.009.
Full textCamps, O. I., T. Kanungo, and R. M. Haralick. "Gray-scale structuring element decomposition." IEEE Transactions on Image Processing 5, no. 1 (January 1996): 111–20. http://dx.doi.org/10.1109/83.481675.
Full textFühr, Hartmut, and Azita Mayeli. "Homogeneous Besov Spaces on Stratified Lie Groups and Their Wavelet Characterization." Journal of Function Spaces and Applications 2012 (2012): 1–41. http://dx.doi.org/10.1155/2012/523586.
Full textMoktadir, Z. "Scale decomposition of molecular beam epitaxy." Journal of Physics: Condensed Matter 20, no. 23 (May 13, 2008): 235240. http://dx.doi.org/10.1088/0953-8984/20/23/235240.
Full textBertsekas, D. P. "Thevenin decomposition and large-scale optimization." Journal of Optimization Theory and Applications 89, no. 1 (April 1996): 1–15. http://dx.doi.org/10.1007/bf02192638.
Full textJi, Jingyu, Yuhua Zhang, Yongjiang Hu, Yongke Li, Changlong Wang, Zhilong Lin, Fuyu Huang, and Jiangyi Yao. "Fusion of Infrared and Visible Images Based on Three-Scale Decomposition and ResNet Feature Transfer." Entropy 24, no. 10 (September 24, 2022): 1356. http://dx.doi.org/10.3390/e24101356.
Full textLi, Ming Jing, Xiao Li Wang, and Yu Bing Dong. "Research and Development of Multi-Scale to Pixel-Level Image Fusion." Applied Mechanics and Materials 448-453 (October 2013): 3625–28. http://dx.doi.org/10.4028/www.scientific.net/amm.448-453.3625.
Full textAbolfazl Hajisami and Dario Pompili. "MSICA: multi-scale signal decomposition based on independent component analysis with application to denoising and reliable multi-channel." ITU Journal on Future and Evolving Technologies 1, no. 1 (December 11, 2020): 25–35. http://dx.doi.org/10.52953/psmv3163.
Full textDissertations / Theses on the topic "Scale decomposition"
Hawley, Stephen Dwyer. "Adaptive time-scale decomposition for multiscale systems /." Thesis, Connect to this title online; UW restricted, 2008. http://hdl.handle.net/1773/6009.
Full textFinney, John D. "Decomposition and decentralized output control of large-scale systems." Diss., Georgia Institute of Technology, 1995. http://hdl.handle.net/1853/15606.
Full textShankar, Jayashree. "Analysis of a nonhierarchical decomposition algorithm." Thesis, This resource online, 1992. http://scholar.lib.vt.edu/theses/available/etd-09192009-040336/.
Full textSanneman, Lindsay (Lindsay Michelle). "Decomposition techniques for large-scale optimization in the supply chain." Thesis, Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/118674.
Full textThis electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 103-105).
Integrated supply chain models provide an opportunity to optimize costs and production times in the supply chain while taking into consideration the many steps in the production and delivery process and the many constraints on time, shared resources, and throughput capabilities. In this work, mixed integer linear programming (MILP) models are developed to describe the manufacturing plant, consolidation transport, and distribution center components of the supply chain. Initial optimization results are obtained for each of these models. Additionally, an integrated model including a single plant, multiple consolidation transport vehicles, and a single distribution center is formulated and initial results are obtained. All models are implemented and optimized for their given objectives using a standard MILP solver. Initial optimization results suggest that it is intractable to solve problems of relevant scale using standard MILP solvers. The natural hierarchical structure in the supply chain problem lends itself well to application of decomposition techniques intended to speed up solution time. Exact techniques, such as Benders decomposition, are explored as a baseline. Classical Benders decomposition is applied to the manufacturing plant model, and results indicate that Benders decomposition on its own will not improve solve times for the manufacturing plant problem and instead leads to longer solve times for the problems that are solved. This is likely due to the large number of discrete variables in manufacturing plant model. To improve upon solve times for the manufacturing plant model, an approximate decomposition technique is developed, applied to the plant model, and evaluated. The approximate algorithm developed in this work decomposes the problem into a three-level hierarchical structure and integrates a heuristic approach at two of the three levels in order to solve abstracted versions of the larger problem and guide towards high-quality solutions. Results indicate that the approximate technique solves problems faster than those solved by the standard MILP solver and all solutions are within approximately 20% of the true optimal solutions. Additionally, the approximate technique can solve problems twice the size of those solved by the standard MILP solver within a one hour timeframe.
by Lindsay Sanneman.
S.M.
Becker, Adrian Bernard Druke. "Decomposition methods for large scale stochastic and robust optimization problems." Thesis, Massachusetts Institute of Technology, 2011. http://hdl.handle.net/1721.1/68969.
Full textCataloged from PDF version of thesis.
Includes bibliographical references (p. 107-112).
We propose new decomposition methods for use on broad families of stochastic and robust optimization problems in order to yield tractable approaches for large-scale real world application. We introduce a new type of a Markov decision problem named the Generalized Rest less Bandits Problem that encompasses a broad generalization of the restless bandit problem. For this class of stochastic optimization problems, we develop a nested policy heuristic which iteratively solves a series of sub-problems operating on smaller bandit systems. We also develop linear-optimization based bounds for the Generalized Restless Bandit problem and demonstrate promising computational performance of the nested policy heuristic on a large-scale real world application of search term selection for sponsored search advertising. We further study the distributionally robust optimization problem with known mean, covariance and support. These optimization models are attractive in their real world applications as they require the model consumer to only rely on those statistics of uncertainty that are known with relative confidence rather than making arbitrary assumptions about the exact dynamics of the underlying distribution of uncertainty. Known to be AP - hard, current approaches invoke tractable but often weak relaxations for real-world applications. We develop a decomposition method for this family of problems which recursively derives sub-policies along projected dimensions of uncertainty and provides a sequence of bounds on the value of the derived policy. In the development of this method, we prove that non-convex quadratic optimization in n-dimensions over a box in two-dimensions is efficiently solvable. We also show that this same decomposition method yields a promising heuristic for the MAXCUT problem. We then provide promising computational results in the context of a real world fixed income portfolio optimization problem. The decomposition methods developed in this thesis recursively derive sub-policies on projected dimensions of the master problem. These sub-policies are optimal on relaxations which admit "tight" projections of the master problem; that is, the projection of the feasible region for the relaxation is equivalent to the projection of that of master problem along the dimensions of the sub-policy. Additionally, these decomposition strategies provide a hierarchical solution structure that aids in solving large-scale problems.
by Adrian Bernard Druke Becker.
Ph.D.
Ortiz, Diaz Camilo. "Block-decomposition and accelerated gradient methods for large-scale convex optimization." Diss., Georgia Institute of Technology, 2014. http://hdl.handle.net/1853/53438.
Full textScott, Drew. "Decomposition Methods for Routing and Planning of Large-Scale Aerospace Systems." University of Cincinnati / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1617108065278479.
Full textPrescott, Thomas Paul. "Large-scale layered systems and synthetic biology : model reduction and decomposition." Thesis, University of Oxford, 2014. http://ora.ox.ac.uk/objects/uuid:205a18fb-b21f-4148-ba7d-3238f4b1f25b.
Full textChan, Chi-keung, and 陳志強. "Minimum bounding boxes and volume decomposition of CAD models." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2003. http://hub.hku.hk/bib/B29947340.
Full textKemenov, Konstantin A. "A New Two-Scale Decomposition Approach for Large-Eddy Simulation of Turbulent Flows." Diss., Georgia Institute of Technology, 2006. http://hdl.handle.net/1853/11520.
Full textBooks on the topic "Scale decomposition"
Large-scale optimization: Problems and methods. Dordrecht: Kluwer Academic Publishers, 2001.
Find full textSakawa, Masatoshi. Large Scale Interactive Fuzzy Multiobjective Programming: Decomposition Approaches. Heidelberg: Physica-Verlag HD, 2000.
Find full textSuvrajeet, Sen, ed. Stochastic decomposition: A statistical method for large scale stochastic linear programming. Dordrecht: Kluwer, 1996.
Find full textNemoto, Jiro. Productivity, efficiency, scale economies and technical change: A new decomposition analysis. Cambridge, MA: National Bureau of Economic Research, 2005.
Find full textL, Tourrette, and Halpern Laurence, eds. Absorbing boundaries and layers, domain decomposition methods: Applications to large scale computers. Huntington, N.Y: Nova Science Publishers, 2001.
Find full textLange, Heinrich. Solution of large-scale multicommodity network flow problems via a logarithmic barrier function decomposition. Monterey, California: Naval Postgraduate School, 1988.
Find full textDecentralized control and filtering in interconnected dynamical systems. Boca Raton: CRC Press, 2010.
Find full textNemoto, Jiro. Productivity, efficiency, scale economies and technical change: A new decomposition analysis of TFP applied to the Japanese prefectures. Cambridge, Mass: National Bureau of Economic Research, 2005.
Find full textHogan, Jeffrey A. Time-frequency and time-scale methods: Adaptive decompositions, uncertainty principles, and sampling. Boston, MA: Birkhauser, 2004.
Find full text1963-, Lakey Joseph D., ed. Time-frequency and time-scale methods: Adaptive decompositions, uncertainty principles, and sampling. Boston: Birkhauser, 2005.
Find full textBook chapters on the topic "Scale decomposition"
Jütte, Silke. "The Divide-and-Price Decomposition Algorithm." In Large-Scale Crew Scheduling, 57–89. Wiesbaden: Springer Fachmedien Wiesbaden, 2012. http://dx.doi.org/10.1007/978-3-658-24360-9_4.
Full textHorst, Reiner, and Hoang Tuy. "Decomposition of Large Scale Problems." In Global Optimization, 371–433. Berlin, Heidelberg: Springer Berlin Heidelberg, 1990. http://dx.doi.org/10.1007/978-3-662-02598-7_8.
Full textHeurtaux, Frédéric, Fabrice Planchon, and Mladen Victor Wickerhauser. "Scale decomposition in Burgers' equation." In Wavelets, 505–23. Boca Raton: CRC Press, 2021. http://dx.doi.org/10.1201/9781003210450-17.
Full textHorst, Reiner, and Hoang Tuy. "Decomposition of Large Scale Problems." In Global Optimization, 371–433. Berlin, Heidelberg: Springer Berlin Heidelberg, 1993. http://dx.doi.org/10.1007/978-3-662-02947-3_8.
Full textHorst, Reiner, and Hoang Tuy. "Decomposition of Large Scale Problems." In Global Optimization, 381–446. Berlin, Heidelberg: Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/978-3-662-03199-5_8.
Full textMartin, Richard Kipp. "Projection: Benders’ Decomposition." In Large Scale Linear and Integer Optimization: A Unified Approach, 349–67. Boston, MA: Springer US, 1999. http://dx.doi.org/10.1007/978-1-4615-4975-8_10.
Full textSakawa, Masatoshi. "The Dantzig-Wolfe Decomposition Method." In Large Scale Interactive Fuzzy Multiobjective Programming, 41–52. Heidelberg: Physica-Verlag HD, 2000. http://dx.doi.org/10.1007/978-3-7908-1851-2_3.
Full textSakawa, Masatoshi. "Genetic Algorithms with Decomposition Procedures." In Large Scale Interactive Fuzzy Multiobjective Programming, 117–34. Heidelberg: Physica-Verlag HD, 2000. http://dx.doi.org/10.1007/978-3-7908-1851-2_7.
Full textLitvinchev, Igor, and Vladimir Tsurkov. "Iterative Aggregation-Decomposition in Optimization Problems." In Aggregation in Large-Scale Optimization, 61–161. Boston, MA: Springer US, 2003. http://dx.doi.org/10.1007/978-1-4419-9154-6_2.
Full textSen, Suvrajeet, Jason Mai, and Julia L. Higle. "Solution of Large Scale Stochastic Programs with Stochastic Decomposition Algorithms." In Large Scale Optimization, 388–410. Boston, MA: Springer US, 1994. http://dx.doi.org/10.1007/978-1-4613-3632-7_19.
Full textConference papers on the topic "Scale decomposition"
Alsam, Ali, and Hans Jakob Rivertz. "Fast scale space image decomposition." In 2015 International Conference on Systems, Signals and Image Processing (IWSSIP). IEEE, 2015. http://dx.doi.org/10.1109/iwssip.2015.7313926.
Full textde Almeida, Andre L. F., and Alain Y. Kibangou. "Distributed large-scale tensor decomposition." In ICASSP 2014 - 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2014. http://dx.doi.org/10.1109/icassp.2014.6853551.
Full textMakarov, Dmitry, and Vladimir Sobolev. "Decomposition of Multiple Time-Scale Systems." In 2021 14th International Conference Management of large-scale system development (MLSD). IEEE, 2021. http://dx.doi.org/10.1109/mlsd52249.2021.9600157.
Full textMendez, Miguel Alfonso, Mikhael Balabane, and Jean Marie Buchlin. "Multi-scale proper orthogonal decomposition (mPOD)." In INTERNATIONAL CONFERENCE OF NUMERICAL ANALYSIS AND APPLIED MATHEMATICS (ICNAAM 2017). Author(s), 2018. http://dx.doi.org/10.1063/1.5043720.
Full textRao, Raghuveer M., and Harold H. Szu. "Progress toward three-scale biorthogonal decomposition." In Aerospace/Defense Sensing and Controls, edited by Harold H. Szu. SPIE, 1996. http://dx.doi.org/10.1117/12.235982.
Full textBespalov, Dmitriy, Ali Shokoufandeh, William C. Regli, and Wei Sun. "Local Feature Extraction Using Scale-Space Decomposition." In ASME 2004 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2004. http://dx.doi.org/10.1115/detc2004-57702.
Full textZalozhnev, Alexey Yu. "Large-Scale Railway Network Design and Decomposition." In 2018 Eleventh International Conference "Management of large-scale system development" (MLSD 2018). IEEE, 2018. http://dx.doi.org/10.1109/mlsd.2018.8551788.
Full textCheng, Lechao, Chengyi Zhang, and Zicheng Liao. "Intrinsic Image Transformation via Scale Space Decomposition." In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2018. http://dx.doi.org/10.1109/cvpr.2018.00075.
Full textCan, Azime, Ervin Sejdic, and Luis F. Chaparro. "An asynchronous scale decomposition for biomedical signals." In 2011 IEEE Signal Processing in Medicine and Biology Symposium (SPMB). IEEE, 2011. http://dx.doi.org/10.1109/spmb.2011.6120107.
Full textLi, Xin‐Gong, and Tadeuss J. Ulrych. "Multi‐scale attribute analysis and trace decomposition." In SEG Technical Program Expanded Abstracts 1996. Society of Exploration Geophysicists, 1996. http://dx.doi.org/10.1190/1.1826439.
Full textReports on the topic "Scale decomposition"
Rohlicek, J. R., and A. S. Willsky. Structural Decomposition of Multiple Time Scale Markov Processes,. Fort Belvoir, VA: Defense Technical Information Center, October 1987. http://dx.doi.org/10.21236/ada189739.
Full textYao, Y. Decomposition of Large Scale Semantic Graphsvia an Efficient Communities Algorithm. Office of Scientific and Technical Information (OSTI), February 2008. http://dx.doi.org/10.2172/926011.
Full textDantzig, George B., and Gerd Infanger. Large-Scale Stochastic Linear Programs: Importance Sampling and Benders Decomposition. Fort Belvoir, VA: Defense Technical Information Center, March 1991. http://dx.doi.org/10.21236/ada234962.
Full textSen, Suvrajeet, and Yang Yuan. Decomposition Algorithms for Very Large Scale Stochastic Mixed-Integer Programs. Fort Belvoir, VA: Defense Technical Information Center, January 2007. http://dx.doi.org/10.21236/ada481382.
Full textNemoto, Jiro, and Mika Goto. Productivity, Efficiency, Scale Economies and Technical Change: A New Decomposition Analysis. Cambridge, MA: National Bureau of Economic Research, May 2005. http://dx.doi.org/10.3386/w11373.
Full textCena, R. J., C. B. Thorsness, T. T. Coburn, and B. E. Watkins. Second test of base hydrolysate decomposition in a 0.04 gallon per minute scale reactor. Office of Scientific and Technical Information (OSTI), October 1994. http://dx.doi.org/10.2172/105852.
Full textCena, R. J., C. B. Thorsness, T. Coburn, and B. E. Watkins. LLNL demonstration of base hydrolysate decomposition in a 0.035 gallon per minute scale reactor. Office of Scientific and Technical Information (OSTI), June 1994. http://dx.doi.org/10.2172/10170617.
Full textMukerji, Sudip. Turbulence computations with 3-D small-scale additive turbulent decomposition and data-fitting using chaotic map combinations. Office of Scientific and Technical Information (OSTI), January 1997. http://dx.doi.org/10.2172/666048.
Full textHussain, Fazle. Vortex Core Dynamics, Complex Helical Wave Decomposition, Organization of Fine-Scale Turbulence and Other Related Theoretical/Numerical Studies. Fort Belvoir, VA: Defense Technical Information Center, April 1995. http://dx.doi.org/10.21236/ada299198.
Full textLou, X. C., R. Rohlicek, P. G. Coxson, G. C. Verghese, and A. S. Willsky. Time Scale Decomposition: The Role of Scaling in Linear Systems and Transient States in Finite-State Markov Processes. Fort Belvoir, VA: Defense Technical Information Center, March 1985. http://dx.doi.org/10.21236/ada160185.
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