Bibliografías temáticas / Stochastic program

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Literatura académica sobre el tema "Stochastic program"

Autor: Grafiati
Publicado: 4 de junio de 2021
Última modificación: 16 de febrero de 2022

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Artículos de revistas sobre el tema "Stochastic program"

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Schkufza, Eric, Rahul Sharma, and Alex Aiken. "Stochastic program optimization." Communications of the ACM 59, no. 2 (2016): 114–22. http://dx.doi.org/10.1145/2863701.

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Bortolussi, L., and A. Policriti. "(Hybrid) automata and (stochastic) programs * The hybrid automata lattice of a stochastic program." Journal of Logic and Computation 23, no. 4 (2011): 761–98. http://dx.doi.org/10.1093/logcom/exr045.

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Alizadeh, Hosein, and S. Jamshid Mousavi. "Stochastic order-based optimal design of a surface reservoir–irrigation district system." Journal of Hydroinformatics 15, no. 2 (2012): 591–606. http://dx.doi.org/10.2166/hydro.2012.223.

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This paper explores the effects of streamflow uncertainty and the type of stochastic order, which is used for comparing stochastic variables, on optimal design of a reservoir multi-crop irrigation district system. Four nonlinear mathematical programs with an economic objective function including deterministic, stochastic-EXP with expected value order, stochastic-SD with stochastic dominance (SD) order, and stochastic-EGCL with expected gain-confidence limit (EGCL) order were developed. Afterwards, the approaches of successive linear programming (SLP) and PSO–MC, which combines particle swarm o
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Jiang, Ruiwei, and Yongpei Guan. "Data-driven chance constrained stochastic program." Mathematical Programming 158, no. 1-2 (2015): 291–327. http://dx.doi.org/10.1007/s10107-015-0929-7.

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Easton, Fred F., and Donald F. Rossin. "A Stochastic Goal Program for Employee Scheduling." Decision Sciences 27, no. 3 (1996): 541–68. http://dx.doi.org/10.1111/j.1540-5915.1996.tb01825.x.

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Gulwani, Sumit. "Technical Perspective: Program synthesis using stochastic techniques." Communications of the ACM 59, no. 2 (2016): 113. http://dx.doi.org/10.1145/2863699.

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Gen, Mitsuo, Baoding Liu, and Kenichi Ida. "Evolution program for deterministic and stochastic optimizations." European Journal of Operational Research 94, no. 3 (1996): 618–25. http://dx.doi.org/10.1016/0377-2217(95)00138-7.

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Park, M., K. Krishnamoorthy, S. Darbha, P. Chandler, and M. Pachter. "State partitioning based linear program for stochastic dynamic programs: An invariance property." Operations Research Letters 40, no. 6 (2012): 487–91. http://dx.doi.org/10.1016/j.orl.2012.08.006.

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Davarnia, Danial, Burak Kocuk, and Gérard Cornuéjols. "Computational Aspects of Bayesian Solution Estimators in Stochastic Optimization." INFORMS Journal on Optimization 2, no. 4 (2020): 256–72. http://dx.doi.org/10.1287/ijoo.2019.0035.

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We study a class of stochastic programs in which some of the elements in the objective function are random and their probability distribution has unknown parameters. The goal is to find a good estimate for the optimal solution of the stochastic program using data sampled from the distribution of the random elements. We investigate two common optimization criteria for evaluating the quality of a solution estimator—one based on the difference in objective values and the other based on the Euclidean distance between solutions. We use risk as the expected value of such criteria over the sample spa
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Bosch, Paul. "A Numerical Method for Two-Stage Stochastic Programs under Uncertainty." Mathematical Problems in Engineering 2011 (2011): 1–13. http://dx.doi.org/10.1155/2011/840137.

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Motivated by problems coming from planning and operational management in power generation companies, this work extends the traditional two-stage linear stochastic program by adding probabilistic constraints in the second stage. In this work we describe, under special assumptions, how the two-stage stochastic programs with mixed probabilities can be treated computationally. We obtain a convex conservative approximations of the chance constraints defined in second stage of our model and use Monte Carlo simulation techniques for approximating the expectation function in the first stage by the ave
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Tesis sobre el tema "Stochastic program"

1

Holbrow, Charles J. "Hypercompression : stochastic musical processing." Thesis, Massachusetts Institute of Technology, 2015. http://hdl.handle.net/1721.1/101838.

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Thesis: S.M., Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2015.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages [66]-70).<br>The theory of stochastic music proposes that we think of music as a vertical integration of mathematics, the physics of sound, psychoacoustics, and traditional music theory In Hypercompression, Stochastic Musical Processing we explore the design and implementation of three innovative musical projects that build on a deep vertical integration of science and technolog
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Zhan, Minshu. "Program auto-tuning through population-based stochastic optimization." Thesis, Massachusetts Institute of Technology, 2016. http://hdl.handle.net/1721.1/106451.

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Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2016.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 41-42).<br>Program optimization often can and need to be decoupled from the programs innate logic. In various domains, program autotuners have been developed which can automatically search for optimal program configurations using established optimization methods. OpenTuner is a general-purpose autotuner which provides a flexible search domain representation and robust optimization techniqu
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Dong, Wen S. M. Massachusetts Institute of Technology. "Influence modeling of complex stochastic processes." Thesis, Massachusetts Institute of Technology, 2006. http://hdl.handle.net/1721.1/37386.

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Thesis (S.M.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2006.<br>Includes bibliographical references (leaves 75-76).<br>A complex stochastic process involving human behaviors or human group behaviors is computationally hard to model with a hidden Markov process. This is because the state space of such behaviors is often a Cartesian product of a large number of constituent probability spaces, and is exponentially large. A sample for those stochastic processes is normally composed of a large collection of heterogeneous consti
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Ivanov, Yuri A. 1967. "Application of stochastic grammars to understanding action." Thesis, Massachusetts Institute of Technology, 1998. http://hdl.handle.net/1721.1/61534.

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Lobovsky, Maxim B. "RattleSnake : stochastic folding for chain programmable matter." Thesis, Massachusetts Institute of Technology, 2011. http://hdl.handle.net/1721.1/69804.

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Thesis (S.M.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2011.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (p. 57-59).<br>The vision of programmable matter is to create a blob of material that can transform itself into an arbitrary form. One promising approach for achieving programmable matter is to construct a chain of identical nodes that can fold into arbitrary threedimensional shapes. Previous active electromechanical systems have demonstrated this concept but are currently costly, com
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Sahu, Reetik Kumar. "Optimal reservoir operation using stochastic model predictive control." Thesis, Massachusetts Institute of Technology, 2016. http://hdl.handle.net/1721.1/104561.

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Thesis: S.M., Massachusetts Institute of Technology, Computation for Design and Optimization Program, 2016.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 61-65).<br>Dynamical systems are subjected to various random external forcings that complicate theie control. In order to achieve optimal performance, these systems need to continually adapt to external disturbances in real time. This capability is provided by feedback based control strategies that derive an optimal control from the current state of the system. Model Predictive Control(MPC) is one such
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Wang, Yanbo S. M. Massachusetts Institute of Technology. "Empirical comparison of robust, data driven and stochastic optimization." Thesis, Massachusetts Institute of Technology, 2008. http://hdl.handle.net/1721.1/45286.

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Includes bibliographical references (leaf 49).<br>Thesis (S.M.)--Massachusetts Institute of Technology, Computation for Design and Optimization Program, 2008.<br>In this thesis, we compare computationally four methods for solving optimization problems under uncertainty: * Robust Optimization (RO) * Adaptive Robust Optimization (ARO) * Data Driven Optimization (DDO) * stochastic Programming (SP) We have implemented several computation experiments to demonstrate the different performance of these methods. We conclude that ARO outperform RO, which has a comparable performance with DDO. SP has a c
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Wang, Wei. "Sample Average Approximation of Risk-Averse Stochastic Programs." Diss., Georgia Institute of Technology, 2007. http://hdl.handle.net/1853/19784.

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Sample average approximation (SAA) is a well-known solution methodology for traditional stochastic programs which are risk neutral in the sense that they consider optimization of expectation functionals. In this thesis we establish sample average approximation methods for two classes of non-traditional stochastic programs. The first class is that of stochastic min-max programs, i.e., min-max problems with expected value objectives, and the second class is that of expected value constrained stochastic programs. We specialize these SAA methods for risk-averse stochastic problems with a bi-criter
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Smith, Michael James Andrew. "Stochastic abstraction of programs : towards performance-driven development." Thesis, University of Edinburgh, 2010. http://hdl.handle.net/1842/4778.

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Distributed computer systems are becoming increasingly prevalent, thanks to modern technology, and this leads to significant challenges for the software developers of these systems. In particular, in order to provide a certain service level agreement with users, the performance characteristics of the system are critical. However, developers today typically consider performance only in the later stages of development, when it may be too late to make major changes to the design. In this thesis, we propose a performance driven approach to development — based around tool support that allows develo
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See, Wee Chiang. "Carbon permit prices in the European emissions trading system : a stochastic analysis." Thesis, Massachusetts Institute of Technology, 2005. http://hdl.handle.net/1721.1/32293.

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Thesis (S.M.)--Massachusetts Institute of Technology, Engineering Systems Division, Technology and Policy Program, 2005.<br>Includes bibliographical references (leaves 79-80).<br>The Emission Trading Scheme (ETS) is a cornerstone for European efforts to reduce greenhouse gas emissions, and in its test phase will operate from 2005-2007. It is a cap-and-trade system where an aggregate cap on emissions is set by the respective government agencies to define the total number of emissions allowances. Each allowance gives the owner the right to emit one unit (usually one ton) of emissions. Covered es
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Libros sobre el tema "Stochastic program"

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Thomas, A. W. Computer program for stochastic utilization of the USLE. Southern Piedmont Conservation Research Center, Agricultural Research Service, USDA, 1989.

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Thomas, A. W. A computer program for transforming stochastic data and evaluating probability distributions. Southern Piedmont Conservation Research Center, Agricultural Research Service, USDA, 1986.

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Gallis, Christos. Stochastic computer simulation of forest biomass logistics in Greece. Dept. of Forest Resource Management, University of Helsinki, 1997.

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Grigoriu, Mircea. Applied non-Gaussian processes: Examples, theory, simulation, linear random vibration, and MATLAB solutions. PTR Prentice Hall, 1995.

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Butler, Ricky W. The SURE reliability analysis program. National Aeronautics and Space Administration, Scientific and Technical Information Branch, 1986.

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Liang, Jingjing. WestProPlus: A stochastic spreadsheet program for the management of all-aged Douglas-fir-- hemlock forests in the Pacific Northwest. U.S. Dept. of Agriculture, Forest Service, Pacific Northwest Research Station, 2006.

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Ludwig, Monika. Asymptotic Geometric Analysis: Proceedings of the Fall 2010 Fields Institute Thematic Program. Springer New York, 2013.

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Kuhn, Daniel. Generalized bounds for convex multistage stochastic programs. Springer, 2005.

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Beckmann, M., H. P. Künzi, G. Fandel, et al., eds. Generalized Bounds for Convex Multistage Stochastic Programs. Springer-Verlag, 2005. http://dx.doi.org/10.1007/b138260.

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Gould, Harvey. An introduction to computer simulation methods: Applicationsto physical systems. Addison-Wesley, 1987.

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Capítulos de libros sobre el tema "Stochastic program"

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Charamza, Pavel. "Integrated stochastic approximation program system." In Lecture Notes in Economics and Mathematical Systems. Springer Berlin Heidelberg, 1992. http://dx.doi.org/10.1007/978-3-642-48914-3_6.

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Sałustowicz, Rafał, and Jürgen Schmidhuber. "Probabilistic Incremental Program Evolution: Stochastic search through program space." In Machine Learning: ECML-97. Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/3-540-62858-4_86.

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Straßer, Jeffry. "Setting up a Multistage Stochastic Program." In Integrated Risk Management of Non-Maturing Accounts. Springer Fachmedien Wiesbaden, 2014. http://dx.doi.org/10.1007/978-3-658-04903-4_4.

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Sladký, Karel. "On a Multistage Stochastic Linear Program." In Asymptotic Statistics. Physica-Verlag HD, 1994. http://dx.doi.org/10.1007/978-3-642-57984-4_39.

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Arnold, Ludwig. "Hasselmann’s program revisited: the analysis of stochasticity in deterministic climate models." In Stochastic Climate Models. Birkhäuser Basel, 2001. http://dx.doi.org/10.1007/978-3-0348-8287-3_5.

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Enevoldsen, Søren, Mathias Claus Jensen, Kim Guldstrand Larsen, Anders Mariegaard, and Jiří Srba. "Verification of Multiplayer Stochastic Games via Abstract Dependency Graphs." In Logic-Based Program Synthesis and Transformation. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-68446-4_13.

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Taskiran, Cuneyt M., Ilya Pollak, Charles A. Bouman, and Edward J. Delp. "Stochastic Models of Video Structure for Program Genre Detection." In Visual Content Processing and Representation. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-39798-4_13.

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Zambrini, Jean-Claude. "The Research Program of Stochastic Deformation (with a View Toward Geometric Mechanics)." In Stochastic Analysis: A Series of Lectures. Springer Basel, 2015. http://dx.doi.org/10.1007/978-3-0348-0909-2_13.

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Birge, John R., Stephen M. Pollock, and Liqun Qi. "A Quadratic Recourse Function for the Two-Stage Stochastic Program." In Applied Optimization. Springer US, 2000. http://dx.doi.org/10.1007/978-1-4613-0301-5_7.

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Krasovskii, A. N., and N. N. Krasovskii. "Stochastic program synthesis of pure strategies for a positional functional." In Control Under Lack of Information. Birkhäuser Boston, 1995. http://dx.doi.org/10.1007/978-1-4612-2568-3_2.

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Actas de conferencias sobre el tema "Stochastic program"

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Debeye, H. W. J., E. Sabbah, and P. M. van der Made. "Stochastic inversion." In SEG Technical Program Expanded Abstracts 1996. Society of Exploration Geophysicists, 1996. http://dx.doi.org/10.1190/1.1826315.

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Jenkins, Gale F., and Gary M. Brown. "Stochastic inversion." In SEG Technical Program Expanded Abstracts 2000. Society of Exploration Geophysicists, 2000. http://dx.doi.org/10.1190/1.1815911.

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Liu, Han, Chengnian Sun, Zhendong Su, Yu Jiang, Ming Gu, and Jiaguang Sun. "Stochastic Optimization of Program Obfuscation." In 2017 IEEE/ACM 39th International Conference on Software Engineering (ICSE). IEEE, 2017. http://dx.doi.org/10.1109/icse.2017.28.

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Xin, Qi, Myeongsoo Kim, Qirun Zhang, and Alessandro Orso. "Program debloating via stochastic optimization." In ICSE '20: 42nd International Conference on Software Engineering. ACM, 2020. http://dx.doi.org/10.1145/3377816.3381739.

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Canning, Anat, Dominique Mouliere, Yuval Weiss, et al. "Modified Stochastic Inversion (MSI)." In SEG Technical Program Expanded Abstracts 2020. Society of Exploration Geophysicists, 2020. http://dx.doi.org/10.1190/segam2020-3424954.1.

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van der Burg, Dennis, Arie Verdel, and Kees Wapenaar. "Ray‐based stochastic inversion." In SEG Technical Program Expanded Abstracts 2004. Society of Exploration Geophysicists, 2004. http://dx.doi.org/10.1190/1.1851146.

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White, Luther, and John P. Castagna. "Stochastic fluid modulus inversion." In SEG Technical Program Expanded Abstracts 2001. Society of Exploration Geophysicists, 2001. http://dx.doi.org/10.1190/1.1816448.

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Nowak, Ethan, Matthias Imhof, Virginia Tech, and William Kempner. "Object‐based stochastic facies inversion." In SEG Technical Program Expanded Abstracts 2003. Society of Exploration Geophysicists, 2003. http://dx.doi.org/10.1190/1.1817644.

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Swan, Jerry, Krzysztof Krawiec, and Zoltan A. Kocsis. "Stochastic program synthesis via recursion schemes." In GECCO '19: Genetic and Evolutionary Computation Conference. ACM, 2019. http://dx.doi.org/10.1145/3319619.3326758.

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Shrestha, Rajendra K., and Manfred Boeckmann. "Stochastic seismic inversion for reservoir modeling." In SEG Technical Program Expanded Abstracts 2002. Society of Exploration Geophysicists, 2002. http://dx.doi.org/10.1190/1.1817409.

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Informes sobre el tema "Stochastic program"

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Rhode, William S. University - Instrumentation-Research-Program: Computer-Aided Study of Stochastic Processes in the Nervous System. Defense Technical Information Center, 1987. http://dx.doi.org/10.21236/ada193010.

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Andrus, Jason P., Chad Pope, Mary Toston, and Andrew Maas. NSR&D Program Fiscal Year 2015 Funded Research Stochastic Modeling of Radioactive Material Releases Final Report. Office of Scientific and Technical Information (OSTI), 2016. http://dx.doi.org/10.2172/1364099.

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Andrus, Jason P., Chad Pope, Mary Toston, and Andrew Maas. NSR&D Program Fiscal Year 2015 Funded Research Stochastic Modeling of Radioactive Material Releases Final Report. Office of Scientific and Technical Information (OSTI), 2016. http://dx.doi.org/10.2172/1351530.

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Osadetz, K. G., Z. Chen, and H. Gao. SuperSD, Version 1.0: a pool-based stochastic simulation program for modelling the spatial distribution of undiscovered petroleum resources. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2003. http://dx.doi.org/10.4095/214036.

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Liang, Jingjing, Joseph Buongiorno, and Robert A. Monserud. WestProPlus: a stochastic spreadsheet program for the management of all-aged Douglas-fir–hemlock forests in the Pacific Northwest. U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station, 2006. http://dx.doi.org/10.2737/pnw-gtr-674.

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Platzer, Andre. Stochastic Differential Dynamic Logic for Stochastic Hybrid Programs. Defense Technical Information Center, 2011. http://dx.doi.org/10.21236/ada543485.

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Dantzig, George B., and Gerd Infanger. Multi-Stage Stochastic Linear Programs for Portfolio Optimization. Defense Technical Information Center, 1991. http://dx.doi.org/10.21236/ada242308.

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Dantzig, George B., and Gerd Infanger. Multi-Stage Stochastic Linear Programs for Portfolio Optimization. Defense Technical Information Center, 1991. http://dx.doi.org/10.21236/ada242510.

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Dantzig, George B., James K. Ho, and Gerd Infanger. Solving Stochastic Linear Programs on a Hypercube Multicomputer. Defense Technical Information Center, 1991. http://dx.doi.org/10.21236/ada240443.

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Dantzig, George B., James K. Ho, and Gerd Infanger. Solving Stochastic Linear Programs on a Hypercube Multicomputer. Defense Technical Information Center, 1991. http://dx.doi.org/10.21236/ada240630.

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