Bibliografie tematiche / Dynamic programming

Letteratura scientifica selezionata sul tema "Dynamic programming"

Autore: Grafiati
Pubblicato: 4 giugno 2021
Ultima modifica: 6 luglio 2024

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Articoli di riviste sul tema "Dynamic programming"

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O'Caoimh, C. C., e Moshe Sniedovich. "Dynamic Programming". Mathematical Gazette 77, n. 479 (luglio 1993): 284. http://dx.doi.org/10.2307/3619755.

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Smith, David K., e Moshe Sniedovich. "Dynamic Programming." Journal of the Operational Research Society 44, n. 5 (maggio 1993): 526. http://dx.doi.org/10.2307/2583920.

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Smith, David K. "Dynamic Programming". Journal of the Operational Research Society 44, n. 5 (maggio 1993): 526–27. http://dx.doi.org/10.1057/jors.1993.90.

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Valqui Vidal, RenéVictor. "Dynamic programming". European Journal of Operational Research 71, n. 1 (novembre 1993): 135–36. http://dx.doi.org/10.1016/0377-2217(93)90270-w.

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Kenea, Tadios Kiros. "Solving Shortest Route Using Dynamic Programming Problem". Indian Journal Of Science And Technology 15, n. 31 (21 agosto 2022): 1527–31. http://dx.doi.org/10.17485/ijst/v15i31.1342.

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Chow, Gregory C. "Dynamic optimization without dynamic programming". Economic Modelling 9, n. 1 (gennaio 1992): 3–9. http://dx.doi.org/10.1016/0264-9993(92)90002-j.

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Lageweg, B. J., J. K. Lenstra, A. H. G. RinnooyKan, L. Stougie e A. H. G. Rinnooy Kan. "STOCHASTIC INTEGER PROGRAMMING BY DYNAMIC PROGRAMMING". Statistica Neerlandica 39, n. 2 (giugno 1985): 97–113. http://dx.doi.org/10.1111/j.1467-9574.1985.tb01131.x.

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Iwamoto, S. "From Dynamic Programming to Bynamic Programming". Journal of Mathematical Analysis and Applications 177, n. 1 (luglio 1993): 56–74. http://dx.doi.org/10.1006/jmaa.1993.1243.

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Kaur, Kamaljeet, e Neeti Taneja. "Dynamic Programming: LCS". International Journal of Advanced Research in Computer Science and Software Engineering 7, n. 6 (30 giugno 2017): 272–77. http://dx.doi.org/10.23956/ijarcsse/v7i6/0131.

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Jdid, Maissam, e Rafif Alhabib. "Neutrosophical dynamic programming". International Journal of Neutrosophic Science 18, n. 3 (2022): 157–65. http://dx.doi.org/10.54216/ijns.1803013.

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Abstract (sommario):
The great development that science has witnessed in all fields has reduced the risks and losses resulting from undertaking any business or projects. Since the emergence of the science of operations research, many life issues have been addressed by relying on it, and by using its methods, we have been able to establish projects and businesses and use the available capabilities in an ideal manner. Which achieved great success in all areas and reduced the losses of all kinds, whether material or human, that we were exposed to because of carrying out these works or projects without prior study. We are now able to model, analyze and solve a wide range of problems that can be broken down into a set of partial problems using dynamic programming. Programming that is used to find the optimal solution in a multi-step situation that involves a set of related decisions. In this research, we study one of the operations research problems that are solved using dynamic programming. It is the problem of creating an expressway between two cities, using the neutrosophic logic. The logic that takes into account all the specific and non-specific data and takes into account all the circumstances that can face us during the implementation of the project. The goal of studying this issue is to determine the optimal total cost, which is related to the partial costs presented by the study prepared for this project. In order to avoid losses we will take the partial costs neutrosophic values of the form , where represents the minimum partial cost in stage and represents the upper limit of the partial cost in stage . Through the indeterminacy offered by neutrosophic logic, we are able to find the ideal solution that will bring us the lowest possible cost for constructing this expressway. It takes into account all the circumstances that may encounter us in our study, and we will present an applied example that illustrates the study.
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Tesi sul tema "Dynamic programming"

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Zhang, Yan. "Dynamic programming speedups /". View abstract or full-text, 2007. http://library.ust.hk/cgi/db/thesis.pl?CSED%202007%20ZHANGY.

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Weimann, Oren. "Accelerating dynamic programming". Thesis, Massachusetts Institute of Technology, 2009. http://hdl.handle.net/1721.1/53302.

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Abstract (sommario):
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2009.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 129-136).
Dynamic Programming (DP) is a fundamental problem-solving technique that has been widely used for solving a broad range of search and optimization problems. While DP can be invoked when more specialized methods fail, this generality often incurs a cost in efficiency. We explore a unifying toolkit for speeding up DP, and algorithms that use DP as subroutines. Our methods and results can be summarized as follows. - Acceleration via Compression. Compression is traditionally used to efficiently store data. We use compression in order to identify repeats in the table that imply a redundant computation. Utilizing these repeats requires a new DP, and often different DPs for different compression schemes. We present the first provable speedup of the celebrated Viterbi algorithm (1967) that is used for the decoding and training of Hidden Markov Models (HMMs). Our speedup relies on the compression of the HMM's observable sequence. - Totally Monotone Matrices. It is well known that a wide variety of DPs can be reduced to the problem of finding row minima in totally monotone matrices. We introduce this scheme in the context of planar graph problems. In particular, we show that planar graph problems such as shortest paths, feasible flow, bipartite perfect matching, and replacement paths can be accelerated by DPs that exploit a total-monotonicity property of the shortest paths. - Combining Compression and Total Monotonicity. We introduce a method for accelerating string edit distance computation by combining compression and totally monotone matrices.
(cont.) In the heart of this method are algorithms for computing the edit distance between two straight-line programs. These enable us to exploits the compressibility of strings, even if each string is compressed using a different compression scheme. - Partial Tables. In typical DP settings, a table is filled in its entirety, where each cell corresponds to some subproblem. In some cases, by changing the DP, it is possible to compute asymptotically less cells of the table. We show that [theta](n³) subproblems are both necessary and sufficient for computing the similarity between two trees. This improves all known solutions and brings the idea of partial tables to its full extent. - Fractional Subproblems. In some DPs, the solution to a subproblem is a data structure rather than a single value. The entire data structure of a subproblem is then processed and used to construct the data structure of larger subproblems. We suggest a method for reusing parts of a subproblem's data structure. In some cases, such fractional parts remain unchanged when constructing the data structure of larger subproblems. In these cases, it is possible to copy this part of the data structure to the larger subproblem using only a constant number of pointer changes. We show how this idea can be used for finding the optimal tree searching strategy in linear time. This is a generalization of the well known binary search technique from arrays to trees.
by Oren Weimann.
Ph.D.
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Wong, K. H. "Dynamic programming in pattern recognition". Thesis, University of Cambridge, 1986. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.383059.

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Moor, Oege de. "Categories, relations and dynamic programming". Thesis, University of Oxford, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.305600.

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Hinchliffe, Mark. "Dynamic modelling using genetic programming". Thesis, University of Newcastle Upon Tyne, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.391407.

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Gallia, Jason. "Protein identification by dynamic programming". Diss., Online access via UMI:, 2009.

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Batra, Jatin. "Dynamic programming for scheduling problems". Thesis, IIT Delhi, 2019. http://eprint.iitd.ac.in:80//handle/2074/8050.

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Evers, Dirk J. "RNA folding via algebraic dynamic programming". [S.l. : s.n.], 2003. http://deposit.ddb.de/cgi-bin/dokserv?idn=968564844.

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Archer, Grant R. "Seismic velocity analysis using dynamic programming /". Title page, contents and abstract only, 1987. http://web4.library.adelaide.edu.au/theses/09S.B/09s.ba671.pdf.

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Sung, Joo-Ho. "Dynamic programming approaches to pension funding". Thesis, City University London, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.361860.

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Libri sul tema "Dynamic programming"

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Kennedy, John O. S. Dynamic Programming. Dordrecht: Springer Netherlands, 1986. http://dx.doi.org/10.1007/978-94-009-4191-5.

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Art, Lew, e Holger Mauch. Dynamic Programming. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-37014-7.

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Sniedovich, Moshe. Dynamic programming. New York, N.Y: M. Dekker, 1992.

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Powell, Warren B. Approximate Dynamic Programming. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2011. http://dx.doi.org/10.1002/9781118029176.

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N, Tsitsiklis John, a cura di. Neuro-dynamic programming. Belmont, Mass: Athena Scientific, 1996.

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Sun, Jiayue, Shun Xu, Yang Liu e Huaguang Zhang. Adaptive Dynamic Programming. Singapore: Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-99-5929-7.

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Jiang, Yu, e Zhong-Ping Jiang. Robust Adaptive Dynamic Programming. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2017. http://dx.doi.org/10.1002/9781119132677.

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1947-, Dana Rose-Anne, a cura di. Dynamic programming in economics. Boston: Kluwer Academic Publishers, 2003.

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Ulmer, Marlin Wolf. Approximate Dynamic Programming for Dynamic Vehicle Routing. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-55511-9.

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D, Robinett Rush, a cura di. Applied dynamic programming for optimization of dynamical systems. Philadelphia: Society for Industrial and Applied Mathematics, 2005.

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Capitoli di libri sul tema "Dynamic programming"

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Poler, Raúl, Josefa Mula e Manuel Díaz-Madroñero. "Dynamic Programming". In Operations Research Problems, 325–74. London: Springer London, 2013. http://dx.doi.org/10.1007/978-1-4471-5577-5_9.

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Zabczyk, Jerzy. "Dynamic programming". In Systems & Control: Foundations & Applications, 137–51. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-44778-6_9.

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Čepin, Marko. "Dynamic Programming". In Assessment of Power System Reliability, 253–55. London: Springer London, 2011. http://dx.doi.org/10.1007/978-0-85729-688-7_17.

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Blanchini, Franco, e Stefano Miani. "Dynamic programming". In Set-Theoretic Methods in Control, 149–89. Boston, MA: Birkhäuser Boston, 2008. http://dx.doi.org/10.1007/978-0-8176-4606-6_5.

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Onori, Simona, Lorenzo Serrao e Giorgio Rizzoni. "Dynamic Programming". In SpringerBriefs in Electrical and Computer Engineering, 41–49. London: Springer London, 2015. http://dx.doi.org/10.1007/978-1-4471-6781-5_4.

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Böhme, Thomas J., e Benjamin Frank. "Dynamic Programming". In Advances in Industrial Control, 199–214. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-51317-1_6.

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Borne, Pierre, Dumitru Popescu, Florin Gh Filip, Dan Stefanoiu e Bernard Dubuisson. "Dynamic Programming". In Optimization in Engineering Sciences, 101–14. Hoboken, NJ USA: John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118577899.ch3.

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Faísca, Nuno P., Konstatinos I. Kouramas, Berç Rustem e Efstratios N. Pistikopoulos. "Dynamic Programming". In Process Systems Engineering, 151–72. Weinheim, Germany: Wiley-VCH Verlag GmbH & Co. KGaA, 2014. http://dx.doi.org/10.1002/9783527631209.ch7.

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Faísca, Nuno P., Konstatinos I. Kouramas, Berç Rustem e Efstratios N. Pistikopoulos. "Dynamic Programming". In Multi-Parametric Programming, 151–72. Weinheim, Germany: Wiley-VCH Verlag GmbH & Co. KGaA, 2011. http://dx.doi.org/10.1002/9783527631216.ch7.

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Stueben, Michael. "Dynamic Programming". In Good Habits for Great Coding, 253–305. Berkeley, CA: Apress, 2018. http://dx.doi.org/10.1007/978-1-4842-3459-4_21.

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Atti di convegni sul tema "Dynamic programming"

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Yamamoto, Eiko, Mikio Yamamoto, Kyoji Umemura e Kenneth W. Church. "Dynamic programming". In the fifth international workshop on. New York, New York, USA: ACM Press, 2000. http://dx.doi.org/10.1145/355214.355232.

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Yamaguchi, Akihiko, e Christopher G. Atkeson. "Differential dynamic programming with temporally decomposed dynamics". In 2015 IEEE-RAS 15th International Conference on Humanoid Robots (Humanoids). IEEE, 2015. http://dx.doi.org/10.1109/humanoids.2015.7363430.

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Kolter, J. Zico, Adam Coates, Andrew Y. Ng, Yi Gu e Charles DuHadway. "Space-indexed dynamic programming". In the 25th international conference. New York, New York, USA: ACM Press, 2008. http://dx.doi.org/10.1145/1390156.1390218.

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Rajamaki, Joose, Kourosh Naderi, Ville Kyrki e Perttu Hamalainen. "Sampled differential dynamic programming". In 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2016. http://dx.doi.org/10.1109/iros.2016.7759229.

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Eppstein, D., Z. Galil e R. Giancarlo. "Speeding up dynamic programming". In [Proceedings 1988] 29th Annual Symposium on Foundations of Computer Science. IEEE, 1988. http://dx.doi.org/10.1109/sfcs.1988.21965.

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Plancher, Brian, Zachary Manchester e Scott Kuindersma. "Constrained unscented dynamic programming". In 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2017. http://dx.doi.org/10.1109/iros.2017.8206457.

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Chowdhury, Rezaul Alam, e Vijaya Ramachandran. "Cache-oblivious dynamic programming". In the seventeenth annual ACM-SIAM symposium. New York, New York, USA: ACM Press, 2006. http://dx.doi.org/10.1145/1109557.1109622.

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Holmes, Ian, e Richard Durbin. "Dynamic programming alignment accuracy". In the second annual international conference. New York, New York, USA: ACM Press, 1998. http://dx.doi.org/10.1145/279069.279102.

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Todorov, Emanuel, e Yuval Tassa. "Iterative local dynamic programming". In 2009 IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning (ADPRL). IEEE, 2009. http://dx.doi.org/10.1109/adprl.2009.4927530.

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Oshin, Alex, Matthew D. Houghton, Michael J. Acheson, Irene M. Gregory e Evangelos Theodorou. "Parameterized Differential Dynamic Programming". In Robotics: Science and Systems 2022. Robotics: Science and Systems Foundation, 2022. http://dx.doi.org/10.15607/rss.2022.xviii.046.

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Rapporti di organizzazioni sul tema "Dynamic programming"

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Cai, Yongyang, Kenneth Judd, Thomas Lontzek, Valentina Michelangeli e Che-Lin Su. Nonlinear Programming Method for Dynamic Programming. Cambridge, MA: National Bureau of Economic Research, maggio 2013. http://dx.doi.org/10.3386/w19034.

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Cai, Yongyang, e Kenneth Judd. Dynamic Programming with Hermite Approximation. Cambridge, MA: National Bureau of Economic Research, novembre 2012. http://dx.doi.org/10.3386/w18540.

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Gabaix, Xavier. Behavioral Macroeconomics Via Sparse Dynamic Programming. Cambridge, MA: National Bureau of Economic Research, gennaio 2016. http://dx.doi.org/10.3386/w21848.

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Klotz, Edward S. Dynamic Pricing Criteria in Linear Programming. Fort Belvoir, VA: Defense Technical Information Center, luglio 1988. http://dx.doi.org/10.21236/ada198945.

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Bergen, Benjamin Karl. Legion Programming System: Dynamic Control Replication. Office of Scientific and Technical Information (OSTI), aprile 2020. http://dx.doi.org/10.2172/1615657.

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Gabaix, Xavier. Boundedly Rational Dynamic Programming: Some Preliminary Results. Cambridge, MA: National Bureau of Economic Research, gennaio 2012. http://dx.doi.org/10.3386/w17783.

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Rust, John. A Dynamic Programming Model of Retirement Behavior. Cambridge, MA: National Bureau of Economic Research, dicembre 1987. http://dx.doi.org/10.3386/w2470.

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Kahou, Mahdi Ebrahimi, Jesús Fernández-Villaverde, Jesse Perla e Arnav Sood. Exploiting Symmetry in High-Dimensional Dynamic Programming. Cambridge, MA: National Bureau of Economic Research, luglio 2021. http://dx.doi.org/10.3386/w28981.

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Demmel, James. DEGAS: Dynamic Exascale Global Address Space Programming Environments. Office of Scientific and Technical Information (OSTI), febbraio 2018. http://dx.doi.org/10.2172/1423458.

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Cai, Yongyang, Kenneth Judd, Greg Thain e Stephen Wright. Solving Dynamic Programming Problems on a Computational Grid. Cambridge, MA: National Bureau of Economic Research, gennaio 2013. http://dx.doi.org/10.3386/w18714.

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