Relevant bibliographies by topics / Stochastic Optimization

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Stochastic Optimization'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 July 2025

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Journal articles on the topic "Stochastic Optimization"

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Fukushima-Kimura, Bruno Hideki, Yoshinori Kamijima, Kazushi Kawamura, and Akira Sakai. "Stochastic Optimization." Transactions of the Institute of Systems, Control and Information Engineers 36, no. 1 (2023): 9–16. http://dx.doi.org/10.5687/iscie.36.9.

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Yan, Di, and H. Mukai. "Stochastic Discrete Optimization." SIAM Journal on Control and Optimization 30, no. 3 (1992): 594–612. http://dx.doi.org/10.1137/0330034.

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ARCISZEWSKI, TOMASZ. "STOCHASTIC FORM OPTIMIZATION." Engineering Optimization 13, no. 1 (1988): 17–33. http://dx.doi.org/10.1080/03052158808940944.

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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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Apolloni, B., C. Carvalho, and D. de Falco. "Quantum stochastic optimization." Stochastic Processes and their Applications 33, no. 2 (1989): 233–44. http://dx.doi.org/10.1016/0304-4149(89)90040-9.

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Nie, Jiawang, Liu Yang, and Suhan Zhong. "Stochastic polynomial optimization." Optimization Methods and Software 35, no. 2 (2019): 329–47. http://dx.doi.org/10.1080/10556788.2019.1649672.

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Curtis, Frank E., and Katya Scheinberg. "Adaptive Stochastic Optimization: A Framework for Analyzing Stochastic Optimization Algorithms." IEEE Signal Processing Magazine 37, no. 5 (2020): 32–42. http://dx.doi.org/10.1109/msp.2020.3003539.

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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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Kim, Minyoung, and Timothy Hospedales. "A Stochastic Approach to Bi-Level Optimization for Hyperparameter Optimization and Meta Learning." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 17 (2025): 17913–20. https://doi.org/10.1609/aaai.v39i17.33970.

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We tackle the general differentiable meta learning problem that is ubiquitous in modern deep learning, including hyperparameter optimization, loss function learning, few-shot learning and more. These problems are often formalized as Bi-Level Optimizations (BLO). We introduce a novel perspective by turning a given BLO problem into a stochastic optimization, where the inner loss function becomes a smooth probability distribution, and the outer loss becomes an expected loss over the inner distribution. To solve this stochastic optimization, we adopt Stochastic Gradient Langevin Dynamics (SGLD) MC
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Fouskakis, Dimitris, and David Draper. "Stochastic Optimization: A Review." International Statistical Review / Revue Internationale de Statistique 70, no. 3 (2002): 315. http://dx.doi.org/10.2307/1403861.

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Dissertations / Theses on the topic "Stochastic Optimization"

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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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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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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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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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Youssef, Nataly. "Stochastic analysis via robust optimization." Thesis, Massachusetts Institute of Technology, 2016. http://hdl.handle.net/1721.1/103246.

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Thesis: Ph. D., Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2016.<br>Cataloged from student-submitted PDF version of thesis.<br>Includes bibliographical references (pages 167-174).<br>To evaluate the performance and optimize systems under uncertainty, two main avenues have been suggested in the literature: stochastic analysis and optimization describing the uncertainty probabilistically and robust optimization describing the uncertainty deterministically. Instead, we propose a novel paradigm which leverages the conclusions of probability theor
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Theodosopoulos, Theodore. "Stochastic models for global optimization." Thesis, Massachusetts Institute of Technology, 1995. http://hdl.handle.net/1721.1/11404.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1995.<br>Includes bibliographical references (p. 61-63).<br>by Theodore Vassilios Theodosopoulos.<br>Ph.D.
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Books on the topic "Stochastic 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 "Stochastic 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 "Stochastic Optimization"

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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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Gürkan, Gü, A. Yonca Özge, and Stephen M. Robinson. "Solving stochastic optimization problems with stochastic constraints." In the 31st conference. ACM Press, 1999. http://dx.doi.org/10.1145/324138.324297.

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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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Glynn, Peter W. "Optimization of stochastic systems." In the 18th conference. ACM Press, 1986. http://dx.doi.org/10.1145/318242.318260.

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Agrawal, Shipra, Yichuan Ding, Amin Saberi, and Yinyu Ye. "Correlation Robust Stochastic Optimization." In Proceedings of the Twenty-First Annual ACM-SIAM Symposium on Discrete Algorithms. Society for Industrial and Applied Mathematics, 2010. http://dx.doi.org/10.1137/1.9781611973075.88.

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Bilbro, Griff L. "Fast stochastic global optimization." In SPIE's 1993 International Symposium on Optics, Imaging, and Instrumentation, edited by Su-Shing Chen. SPIE, 1993. http://dx.doi.org/10.1117/12.162050.

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Agarwal, Alekh, and John C. Duchi. "Distributed delayed stochastic optimization." In 2012 IEEE 51st Annual Conference on Decision and Control (CDC). IEEE, 2012. http://dx.doi.org/10.1109/cdc.2012.6426626.

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De Oliveira, Ítalo Romani, Steve Altus, Sergey Tiourine, Euclides C. Pinto Neto, Alexandre Leite, and Felipe C. F. De Azevedo. "Stochastic Flight Plan Optimization." In 2023 IEEE/AIAA 42nd Digital Avionics Systems Conference (DASC). IEEE, 2023. http://dx.doi.org/10.1109/dasc58513.2023.10311152.

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Eberhard, Peter, Werner Schiehlen, and Dieter Bestle. "Optimization of Stochastic Multibody Systems." In ASME 1995 Design Engineering Technical Conferences collocated with the ASME 1995 15th International Computers in Engineering Conference and the ASME 1995 9th Annual Engineering Database Symposium. American Society of Mechanical Engineers, 1995. http://dx.doi.org/10.1115/detc1995-0344.

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Abstract The multibody system approach is used for the investigation of mechanical systems with large motions, e.g. in vehicle dynamics and robotics. Important modules of a computer-aided tool proposed for analysis and optimization are filtering techniques and scalar optimization algorithms. Formfilters are used for the creation of stochastic exciations and criterion computations. Typical properties of deterministic and stochastic scalar optimization algorithms with application to dynamic systems are presented and a hybrid deterministic-stochastic approach is proposed which combines the advant
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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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Reports on the topic "Stochastic Optimization"

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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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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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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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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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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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