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Relevant bibliographies by topics / Stochastical optimization

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Academic literature on the topic 'Stochastical optimization'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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Journal articles on the topic "Stochastical optimization"

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Ermakov, Sergei M., and Vyacheslav B. Melas. "Stochastical computation methods and experimental designing." Vestnik of Saint Petersburg University. Mathematics. Mechanics. Astronomy 10, no. 2 (2023): 187–99. http://dx.doi.org/10.21638/spbu01.2023.201.

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This paper contains a brief review of the most important results obtained by the staff of the department of statistical modeling. Results include mathematical justification of computer simulation of randomness, stochastic methods of solving equations, stochastic optimization, study of stochastic stability and parallelism of Monte-Carlo algorithms. In the area of experiment planning, special attention is paid to regression experiment under nonlinear parameterization. The list of references mainly includes monographs written by members of the department. The exceptions are some articles with res

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Hussen, Ayad Kasem. "SPEED ESTIMATION USING EXTENDED KALMAN FILTER TECHNIQUE." Tikrit Journal of Engineering Sciences 12, no. 1 (2005): 115–39. http://dx.doi.org/10.25130/tjes.12.1.09.

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This paper presents a state estimation technique for speed sensorless field oriented control of induction motors. The theoretical basis of each algorithm is explained in detail and its performance is tested with simulations using MATLAB package VER.6.3.A stochastical nonlinear state estimator, Extended Kalman Filter (EKF) is presented. The motor model designed for EKF application involves rotor speed, dq-axis stator currents. Thus, using this observer the rotor speed and rotor fluxes are estimated simultaneously. Different from the widely accepted use of EKF, in which it is optimized for eithe

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Hussen, Ayad Kasem. "Speed Estimation Using Extended Kalman Filter Technique." Tikrit Journal of Engineering Sciences 12, no. 3 (2005): 115–39. http://dx.doi.org/10.25130/tjes.12.3.06.

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This paper presents a state estimation technique for speed senseless field oriented control of induction motors. The theoretical basis of each algorithm is explained in detail and its performance is tested with simulations using MATLAB package VER.6.3. A stochastical nonlinear state estimator, Extended Kalman Filter (EKF) is presented. The motor model designed for EKF application involves rotor speed, dq-axis stator currents. Thus, using this observer the rotor speed and rotor fluxes are estimated simultaneously. Different from the widely accepted use of EKF, in which it is optimized for eithe

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Lapinskaitė, Indrė, and Aleksandras Rutkauskas. "The Optimization of Marketing Costs' Structure as a Prerequisite for Business Sustainable Development." Business: Theory and Practice 14, no. (1) (2013): 74–82. https://doi.org/10.3846/btp.2013.09.

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The idea of quantitative measurement of sustainability is revealed in this article. This idea is based on outlining the possibility of the event or the process, taking into account both possibility of efficiency and reliability of effectiveness. For the selection of appropriate compositions of efficiency and reliability for a particular subject an adequate idea of the utility function was used. Markovitz random field makes it possible to optimize the allocation of existing resources for the marketing costs between the components of marketing structure. Technically, the task is formulated as fo

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Riaz, Muhammad, Sadiq Ahmad, Irshad Hussain, Muhammad Naeem, and Lucian Mihet-Popa. "Probabilistic Optimization Techniques in Smart Power System." Energies 15, no. 3 (2022): 825. http://dx.doi.org/10.3390/en15030825.

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Uncertainties are the most significant challenges in the smart power system, necessitating the use of precise techniques to deal with them properly. Such problems could be effectively solved using a probabilistic optimization strategy. It is further divided into stochastic, robust, distributionally robust, and chance-constrained optimizations. The topics of probabilistic optimization in smart power systems are covered in this review paper. In order to account for uncertainty in optimization processes, stochastic optimization is essential. Robust optimization is the most advanced approach to op

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Azodi, Peyman, Peyman Setoodeh, Alireza Khayatian, and Elham Jamalinia. "Stochastic boundedness of state trajectories of stable LTI systems in the presence of non-vanishing stochastic perturbation." IMA Journal of Mathematical Control and Information 37, no. 3 (2019): 718–29. http://dx.doi.org/10.1093/imamci/dnz023.

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Abstract This paper studies stochastic boundedness of trajectories of a non-vanishing stochastically perturbed stable linear time-invariant system. First, two definitions on stochastic boundedness are presented, then, the boundedness is analyzed via Lyapunov theory. A theorem is proposed, which shows that under a condition on the Lipchitz constant of the perturbation kernel, the trajectories remain stochastically bounded, and the bounds are calculated. Also, the limiting behaviour of the trajectories is studied. At the end, an illustrative example is presented, which shows the effectiveness of

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Sihotang, Hengki Tamando, Syahril Efendi, Muhammad Zarlis, and Herman Mawengkang. "Data driven approach for stochastic data envelopment analysis." Bulletin of Electrical Engineering and Informatics 11, no. 3 (2022): 1497–504. http://dx.doi.org/10.11591/eei.v11i3.3660.

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Decision making based on data driven deals with a large amount of data will evaluate the process's effectiveness. Evaluate effectiveness in this paper is measure of performance efficiency of data envelopment analysis (DEA) method in this study is the approach with uncertainty problems. This study proposed a new method called the robust stochastic DEA (RSDEA) to approach performance efficiency in tackling uncertainty problems (i.e., stochastic and robust optimization). The RSDEA method develops to combine the stochastics DEA (SDEA) formulation method and Robust Optimization. The numerical examp

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Hengki, Tamando Sihotang, Efendi Syahril, Zarlis Muhammad, and Mawengkang Herman. "Data driven approach for stochastic data envelopment analysis." Bulletin of Electrical Engineering and Informatics 11, no. 3 (2022): 1497~1504. https://doi.org/10.11591/eei.v11i3.3660.

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Decision making based on data driven deals with a large amount of data will evaluate the process's effectiveness. Evaluate effectiveness in this paper is measure of performance efficiency of data envelopment analysis (DEA) method in this study is the approach with uncertainty problems. This study proposed a new method called the robust stochastic DEA (RSDEA) to approach performance efficiency in tackling uncertainty problems (i.e., stochastic and robust optimization). The RSDEA method develops to combine the stochastics DEA (SDEA) formulation method and Robust Optimization. The numerical e

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Ashour, Marwan Abdul Hameed, Alyaa Abdulameer Ahmed, and Ammar Sh Ahmed. "Optimization algorithms for transportation problems with stochastic demand." Periodicals of Engineering and Natural Sciences (PEN) 10, no. 3 (2022): 172–79. https://doi.org/10.21533/pen.v10.i3.654.

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The purpose of this paper is to solve the stochastic demand for the unbalanced transport problem using heuristic algorithms to obtain the optimum solution, by minimizing the costs of transporting the gasoline product for the Oil Products Distribution Company of the Iraqi Ministry of Oil. The most important conclusions that were reached are the results prove the possibility of solving the random transportation problem when the demand is uncertain by the stochastic programming model. The most obvious finding to emerge from this work is that the genetic algorithm was able to address the problems

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K. Sivaselvan, K. Sivaselvan, and C. Vijayalakshmi C. Vijayalakshmi. "Stochastic Control Optimization Technique on Multi-Server Markovian Queueing System." Indian Journal of Applied Research 3, no. 7 (2011): 375–78. http://dx.doi.org/10.15373/2249555x/july2013/115.

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Dissertations / Theses on the topic "Stochastical optimization"

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Aull, Mark J. "Airborne Wind Energy System Analysis and Design Optimization." University of Cincinnati / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1592168644639446.

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Kůdela, Jakub. "Advanced Decomposition Methods in Stochastic Convex Optimization." Doctoral thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2019. http://www.nusl.cz/ntk/nusl-403864.

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Při práci s úlohami stochastického programování se často setkáváme s optimalizačními problémy, které jsou příliš rozsáhlé na to, aby byly zpracovány pomocí rutinních metod matematického programování. Nicméně, v některých případech mají tyto problémy vhodnou strukturu, umožňující použití specializovaných dekompozičních metod, které lze použít při řešení rozsáhlých optimalizačních problémů. Tato práce se zabývá dvěma třídami úloh stochastického programování, které mají speciální strukturu, a to dvoustupňovými stochastickými úlohami a úlohami s pravděpodobnostním omezením, a pokročilými dekompozi

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Cheng, Jianqiang. "Stochastic Combinatorial Optimization." Thesis, Paris 11, 2013. http://www.theses.fr/2013PA112261.

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Dans cette thèse, nous étudions trois types de problèmes stochastiques : les problèmes avec contraintes probabilistes, les problèmes distributionnellement robustes et les problèmes avec recours. Les difficultés des problèmes stochastiques sont essentiellement liées aux problèmes de convexité du domaine des solutions, et du calcul de l’espérance mathématique ou des probabilités qui nécessitent le calcul complexe d’intégrales multiples. A cause de ces difficultés majeures, nous avons résolu les problèmes étudiées à l’aide d’approximations efficaces.Nous avons étudié deux types de problèmes stoch

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Fei, Lin. "On a stochastic optimization technique : stochastic probing /." The Ohio State University, 1992. http://rave.ohiolink.edu/etdc/view?acc_num=osu1487777901661535.

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Birbil, Sevket Ilker. "Stochastic Global Optimization Techniques." NCSU, 2002. http://www.lib.ncsu.edu/theses/available/etd-20020403-171452.

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<p>In this research, a novel population-based global optimization method has been studied. The method is called Electromagnetism-like Mechanism or in short EM. The proposed method mimicks the behavior of electrically charged particles. In other words, a set of points is sampled from the feasible region and these points imitate the role of the charged particles in basic electromagnetism. The underlying idea of the method is directing sample points toward local optimizers, which point out attractive regions of the feasible space.The proposed method has been applied to different test problems fro

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Parpas, Panayiotis. "Algorithms for stochastic optimization." Thesis, Imperial College London, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.434980.

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Pohl, Jan. "Algoritmus s pravděpodobnostním směrovým vektorem." Doctoral thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2015. http://www.nusl.cz/ntk/nusl-233694.

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This disertation presents optimization algorithm with probability direction vector. This algorithm, in its basic form, belongs to category of stochastic optimization algorithms. It uses statistically effected perturbation of individual through state space. This work also represents modification of basic idea to the form of swarm optimization algoritm. This approach contains form of stochastic cooperation. This is one of the new ideas of this algorithm. Population of individuals cooperates only through modification of probability direction vector and not directly. Statistical tests are used to

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Xiong, Xiaoping. "Stochastic optimization algorithms and convergence /." College Park, Md. : University of Maryland, 2005. http://hdl.handle.net/1903/2360.

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Thesis (Ph. D.) -- University of Maryland, College Park, 2005.<br>Thesis research directed by: Business and Management. Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.

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Al-Mharmah, Hisham. "Global optimization of stochastic functions." Diss., Georgia Institute of Technology, 1993. http://hdl.handle.net/1853/25665.

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李國誠 and Kwok-shing Lee. "Convergences of stochastic optimization algorithms." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1999. http://hub.hku.hk/bib/B3025632X.

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Books on the topic "Stochastical optimization"

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Marti, Kurt, ed. Stochastic Optimization. Springer Berlin Heidelberg, 1992. http://dx.doi.org/10.1007/978-3-642-88267-8.

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Arkin, Vadim I., A. Shiraev, and R. Wets, eds. Stochastic Optimization. Springer Berlin Heidelberg, 1986. http://dx.doi.org/10.1007/bfb0007076.

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P, Uri︠a︡sʹev S., and Pardalos P. M. 1954-, eds. Stochastic optimization. Kluwer Academic Publishers, 2001.

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Marti, Kurt, Yuri Ermoliev, and Georg Pflug, eds. Dynamic Stochastic Optimization. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-642-55884-9.

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Pflug, Georg Ch, and Alois Pichler. Multistage Stochastic Optimization. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-08843-3.

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Marti, Kurt. Stochastic Optimization Methods. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-79458-5.

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Wang, Shuming, and Junzo Watada. Fuzzy Stochastic Optimization. Springer US, 2012. http://dx.doi.org/10.1007/978-1-4419-9560-5.

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Marti, Kurt. Stochastic Optimization Methods. Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-46214-0.

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service), SpringerLink (Online, ed. Stochastic Optimization Methods. Springer-Verlag Berlin Heidelberg, 2008.

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Kurt, Marti. Stochastic optimization methods. Springer, 2004.

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Book chapters on the topic "Stochastical optimization"

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Cavazzuti, Marco. "Stochastic Optimization." In Optimization Methods. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-31187-1_5.

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Gerke, Horst H., Youcef Kelanemer, Ulrich Hornung, Marián Slodička, and Stephan Schumacher. "Stochastic Optimization." In Optimal Control of Soil Venting: Mathematical Modeling and Applications. Birkhäuser Basel, 1999. http://dx.doi.org/10.1007/978-3-0348-8732-8_9.

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Hänggi, Martin, and George S. Moschytz. "Stochastic Optimization." In Cellular Neural Networks. Springer US, 2000. http://dx.doi.org/10.1007/978-1-4757-3220-7_6.

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Stefanoiu, Dan, Pierre Borne, Dumitru Popescu, Florin Gh Filip, and Abdelkader El Kamel. "Stochastic Optimization." In Optimization in Engineering Sciences: Approximate and Metaheuristic Methods. John Wiley & Sons, Inc., 2014. http://dx.doi.org/10.1002/9781118648766.ch3.

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Spall, James C. "Stochastic Optimization." In Handbook of Computational Statistics. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21551-3_7.

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Segovia-Hernández, Juan Gabriel, and Fernando Israel Gómez-Castro. "Stochastic Optimization." In Stochastic Process Optimization using Aspen Plus®. CRC Press, 2017. http://dx.doi.org/10.1201/9781315155739-3.

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Yamakawa, Makoto, and Makoto Ohsaki. "Stochastic Optimization." In Stochastic Structural Optimization. CRC Press, 2023. http://dx.doi.org/10.1201/9781003153160-2.

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Stickler, Benjamin A., and Ewald Schachinger. "Stochastic Optimization." In Basic Concepts in Computational Physics. Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-02435-6_20.

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Stickler, Benjamin A., and Ewald Schachinger. "Stochastic Optimization." In Basic Concepts in Computational Physics. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-27265-8_20.

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Niño-Mora, José. "Stochastic Scheduling." In Encyclopedia of Optimization. Springer Nature Switzerland, 2024. https://doi.org/10.1007/978-3-030-54621-2_665-1.

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Conference papers on the topic "Stochastical optimization"

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Benjouad, Abdelghani, and Mohammed Kaicer. "Efficient Simulations for Pricing Barrier Options under Stochastic Volatility Model." In 2024 10th International Conference on Optimization and Applications (ICOA). IEEE, 2024. http://dx.doi.org/10.1109/icoa62581.2024.10753751.

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Schaffrath, Robert, Eberhard Nicke, Nicolai Forsthofer, Oliver Kunc, and Christian Voß. "Gradient-Free Aerodynamic Optimization With Structural Constraints and Surge Line Control for Radial Compressor Stage." In ASME Turbo Expo 2023: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2023. http://dx.doi.org/10.1115/gt2023-101593.

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Abstract The concept and design of High Temperature Heat Pumps (HTHP) including their components for specific temperature needs is a time consuming and interdisciplinary task. Especially, the design of compressor geometries has a big impact on the overall performance and the initial costs of the system. For this reasoning, in this work an automated aerodynamic gradient-free optimization including structural constraints for the geometry of a radial compressor impeller blade as well as diffusor vane geometry for water steam, that is applied in a reverse Rankine cycle based HTHP, is presented. Ob

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Wu, Xiaojian, Yexiang Xue, Bart Selman, and Carla P. Gomes. "XOR-Sampling for Network Design with Correlated Stochastic Events." In Twenty-Sixth International Joint Conference on Artificial Intelligence. International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/647.

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Many network optimization problems can be formulated as stochastic network design problems in which edges are present or absent stochastically. Furthermore, protective actions can guarantee that edges will remain present. We consider the problem of finding the optimal protection strategy under a budget limit in order to maximize some connectivity measurements of the network. Previous approaches rely on the assumption that edges are independent. In this paper, we consider a more realistic setting where multiple edges are not independent due to natural disasters or regional events that make the

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Kim, Sun-Je, Hanbyul Chang, and Yoonchan Jeong. "Optimization of plasmonic tapering nanostructures for strong and versatile angle-selective flat optics." In Novel Optical Materials and Applications. Optica Publishing Group, 2022. http://dx.doi.org/10.1364/noma.2022.notu1e.1.

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We introduce our achievements on angle-selective flat optics using the nanoplasmonic tapering metasurfaces. Our study includes theoretical investigation on bimodal plasmonic waveguide and efficient multi-objective performance optimization using the stochastic optimizations with the Adam algorithm.

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Hendricks, Terry J., and Naveen K. Karri. "Probabilistic Design and Analysis for Robust Design of Advanced Thermoelectric Conversion Systems." In ASME 2007 Energy Sustainability Conference. ASMEDC, 2007. http://dx.doi.org/10.1115/es2007-36085.

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Recent research investigated the impacts of single- and multi-variable stochasticity on optimum thermoelectric (TE) system design for automotive and industrial energy recovery because many critical design and environmental parameters used in design optimization can be randomly variable. Analysis tools and techniques have been developed to investigate a variety of stochastic behaviors in critical input parameters, including Gaussian, Log-Normal, Weibull, Gamma, or any type of user-defined probability distribution. Recent accomplishments discussed herein show that: 1) Gaussian input probability

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Spence, Seymour M. J., and Massimiliano Gioffrè. "Time Variant Reliability Optimization of Tall Buildings." In 6th International Conference on Computational Stochastic Mechanics. Research Publishing Services, 2011. http://dx.doi.org/10.3850/978-981-08-7619-7_p057.

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Marczyk, Jacek. "Stochastic multidisciplinary improvement - Beyond optimization." In 8th Symposium on Multidisciplinary Analysis and Optimization. American Institute of Aeronautics and Astronautics, 2000. http://dx.doi.org/10.2514/6.2000-4929.

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Wilson, Craig, Venugopal Veeravalli, and Angelia Nedic. "Dynamic stochastic optimization." In 2014 IEEE 53rd Annual Conference on Decision and Control (CDC). IEEE, 2014. http://dx.doi.org/10.1109/cdc.2014.7039377.

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Huu, Thong Nguyen, and Hao Tran Van. "Search via Probability Algorithm for Engineering Optimization Problems." In Recent Advances in Stochastic Modeling and Data Analysis. WORLD SCIENTIFIC, 2007. http://dx.doi.org/10.1142/9789812709691_0054.

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Scharpenberg, Moriz, and Maria Lukacova-Medvidova. "Stochastic Considerations for Dynamic Systems." In 12th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference. American Institute of Aeronautics and Astronautics, 2008. http://dx.doi.org/10.2514/6.2008-6054.

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Reports on the topic "Stochastical optimization"

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Pasupuleti, Murali Krishna. Stochastic Computation for AI: Bayesian Inference, Uncertainty, and Optimization. National Education Services, 2025. https://doi.org/10.62311/nesx/rriv325.

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Abstract: Stochastic computation is a fundamental approach in artificial intelligence (AI) that enables probabilistic reasoning, uncertainty quantification, and robust decision-making in complex environments. This research explores the theoretical foundations, computational techniques, and real-world applications of stochastic methods, focusing on Bayesian inference, Monte Carlo methods, stochastic optimization, and uncertainty-aware AI models. Key topics include probabilistic graphical models, Markov Chain Monte Carlo (MCMC), variational inference, stochastic gradient descent (SGD), and Bayes

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Prokopyev, Oleg. Stochastic Pseudo-Boolean Optimization. Defense Technical Information Center, 2011. http://dx.doi.org/10.21236/ada564073.

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Birge, John. Stochastic Optimization of Complex Systems. Office of Scientific and Technical Information (OSTI), 2014. http://dx.doi.org/10.2172/1124082.

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Johnson, Michael M., Ann S. Yoshimura, Patricia Diane Hough, and Heidi R. Ammerlahn. Nonlinear optimization for stochastic simulations. Office of Scientific and Technical Information (OSTI), 2003. http://dx.doi.org/10.2172/918225.

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Bidier, S., U. Khristenko, A. Kodakkal, C. Soriano, and R. Rossi. D7.4 Final report on Stochastic Optimization results. Scipedia, 2022. http://dx.doi.org/10.23967/exaqute.2022.3.02.

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This deliverable report focuses on the final stochastic optimization results obtained within the EXAscale Quantification of Uncertainties for Technology and Science Simulation (ExaQUte) project. Details on a novel wind inlet generator that is able to incorporate local wind-field data through a deep-learned rapid distortion model and generates the turbulent wind data during run-time is presented in section 2. Section 3 presents the results of the overall stochastic optimization procedure applied to a twisted tapered tower with multiple design parameters within an uncertain synthetic wind field.

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Glynn, Peter W. Optimization of Stochastic Systems via Simulation. Defense Technical Information Center, 1989. http://dx.doi.org/10.21236/ada214011.

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Cassandras, Christos G. Real-Time Optimization in Complex Stochastic Environments. Defense Technical Information Center, 2012. http://dx.doi.org/10.21236/ada564171.

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Wu, Xingxing, Zhong-Ping Jiang, Daniel W. Repperger, and Yi Guo. Enhancement of Stochastic Resonance Using Optimization Theory. Defense Technical Information Center, 2006. http://dx.doi.org/10.21236/ada460357.

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Aravena, I., D. Rajan, G. Patsakis, S. Oren, and J. Rios. Stochastic Optimization for Grid ResilienceFY18 Final Technical Report. Office of Scientific and Technical Information (OSTI), 2019. http://dx.doi.org/10.2172/1635780.

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Chiang, Mung. A Wireless Network Testbed for Stochastic Network Optimization. Defense Technical Information Center, 2010. http://dx.doi.org/10.21236/ada535199.

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