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Journal articles on the topic 'Stochastic Optimization'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

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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2

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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3

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

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4

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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5

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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6

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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7

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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8

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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9

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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10

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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11

Bilbro, G. L. "Fast stochastic global optimization." IEEE Transactions on Systems, Man, and Cybernetics 24, no. 4 (1994): 684–89. http://dx.doi.org/10.1109/21.286389.

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12

Besbes, Omar, Yonatan Gur, and Assaf Zeevi. "Non-Stationary Stochastic Optimization." Operations Research 63, no. 5 (2015): 1227–44. http://dx.doi.org/10.1287/opre.2015.1408.

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13

Robini, Marc C., and Isabelle E. Magnin. "Optimization by Stochastic Continuation." SIAM Journal on Imaging Sciences 3, no. 4 (2010): 1096–121. http://dx.doi.org/10.1137/090756181.

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14

Wilson, Craig, Venugopal V. Veeravalli, and Angelia Nedic. "Adaptive Sequential Stochastic Optimization." IEEE Transactions on Automatic Control 64, no. 2 (2019): 496–509. http://dx.doi.org/10.1109/tac.2018.2816168.

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15

Dippon, J�rgen. "Accelerated randomized stochastic optimization." Annals of Statistics 31, no. 4 (2003): 1260–81. http://dx.doi.org/10.1214/aos/1059655913.

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16

Koç, Ali, and David P. Morton. "Prioritization via Stochastic Optimization." Management Science 61, no. 3 (2015): 586–603. http://dx.doi.org/10.1287/mnsc.2013.1865.

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17

Bernhardsson, B. "Robust Stochastic Performance Optimization." IFAC Proceedings Volumes 26, no. 2 (1993): 279–82. http://dx.doi.org/10.1016/s1474-6670(17)48944-0.

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18

Fang, Hai-Tao, and Han-Fu Chen. "Global recursive stochastic optimization." IFAC Proceedings Volumes 32, no. 2 (1999): 5029–34. http://dx.doi.org/10.1016/s1474-6670(17)56856-1.

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19

Mnif, Mohammed, and Huyên Pham. "Stochastic optimization under constraints." Stochastic Processes and their Applications 93, no. 1 (2001): 149–80. http://dx.doi.org/10.1016/s0304-4149(00)00089-2.

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20

Chen, Chun-Hung, Leyuan Shi, and Loo Hay Lee. "Stochastic systems simulation optimization." Frontiers of Electrical and Electronic Engineering in China 6, no. 3 (2011): 468–80. http://dx.doi.org/10.1007/s11460-011-0168-5.

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21

Luhandjula, M. K., and M. M. Gupta. "On fuzzy stochastic optimization." Fuzzy Sets and Systems 81, no. 1 (1996): 47–55. http://dx.doi.org/10.1016/0165-0114(95)00240-5.

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22

Yoshida, Hiroaki, Katsuhito Yamaguchi, and Yoshio Ishikawa. "Stochastic Process Optimization Technique." Applied Mathematics 05, no. 19 (2014): 3079–90. http://dx.doi.org/10.4236/am.2014.519293.

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23

Fouskakis, Dimitris, and David Draper. "Stochastic Optimization: a Review." International Statistical Review 70, no. 3 (2002): 315–49. http://dx.doi.org/10.1111/j.1751-5823.2002.tb00174.x.

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24

Marti, Kurt. "Stochastic optimization of regulators." Computers & Structures 180 (February 2017): 40–51. http://dx.doi.org/10.1016/j.compstruc.2016.04.003.

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25

Fukao, Takeshi, and Zhao Yue. "Stochastic distributed optimization algorithm." Electronics and Communications in Japan (Part I: Communications) 71, no. 5 (1988): 29–40. http://dx.doi.org/10.1002/ecja.4410710504.

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26

Chen, Tianyi, Yuejiao Sun, and Wotao Yin. "Solving Stochastic Compositional Optimization is Nearly as Easy as Solving Stochastic Optimization." IEEE Transactions on Signal Processing 69 (2021): 4937–48. http://dx.doi.org/10.1109/tsp.2021.3092377.

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27

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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28

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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29

Yuan, Gonglin, Yingjie Zhou, Liping Wang, and Qingyuan Yang. "Stochastic Bigger Subspace Algorithms for Nonconvex Stochastic Optimization." IEEE Access 9 (2021): 119818–29. http://dx.doi.org/10.1109/access.2021.3108418.

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30

Wang, Xiao, Shiqian Ma, Donald Goldfarb, and Wei Liu. "Stochastic Quasi-Newton Methods for Nonconvex Stochastic Optimization." SIAM Journal on Optimization 27, no. 2 (2017): 927–56. http://dx.doi.org/10.1137/15m1053141.

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31

Dentcheva, Darinka, and Andrzej Ruszczyński. "Stochastic Dynamic Optimization with Discounted Stochastic Dominance Constraints." SIAM Journal on Control and Optimization 47, no. 5 (2008): 2540–56. http://dx.doi.org/10.1137/070679569.

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32

Chimal-Eguia, Juan C., Julio C. Rangel-Reyes, and Ricardo T. Paez-Hernandez. "Improving Convergence in Therapy Scheduling Optimization: A Simulation Study." Mathematics 8, no. 12 (2020): 2114. http://dx.doi.org/10.3390/math8122114.

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The infusion times and drug quantities are two primary variables to optimize when designing a therapeutic schedule. In this work, we test and analyze several extensions to the gradient descent equations in an optimal control algorithm conceived for therapy scheduling optimization. The goal is to provide insights into the best strategies to follow in terms of convergence speed when implementing our method in models for dendritic cell immunotherapy. The method gives a pulsed-like control that models a series of bolus injections and aims to minimize a cost a function, which minimizes tumor size a

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33

LIUM, ARNT-GUNNAR, TEODOR GABRIEL CRAINIC, and STEIN W. WALLACE. "CORRELATIONS IN STOCHASTIC PROGRAMMING: A CASE FROM STOCHASTIC SERVICE NETWORK DESIGN." Asia-Pacific Journal of Operational Research 24, no. 02 (2007): 161–79. http://dx.doi.org/10.1142/s0217595907001206.

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Deterministic models, even if used repeatedly, will not capture the essence of planning in an uncertain world. Flexibility and robustness can only be properly valued in models that use stochastics explicitly, such as stochastic optimization models. However, it may also be very important to capture how the random phenomena are related to one another. In this article we show how the solution to a stochastic service network design model depends heavily on the correlation structure among the random demands. The major goal of this paper is to discuss why this happens, and to provide insights into t

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34

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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35

Wang, Bin, and Tao Yang. "Stochastic Optimization of Empty Container Repositioning of Sea Carriage." Advanced Materials Research 340 (September 2011): 324–30. http://dx.doi.org/10.4028/www.scientific.net/amr.340.324.

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To improve the efficiency of empty container repositioning for a shipping company, a stochastic optimization model of empty container repositioning of sea carriage was established by chance-constrained programming. The objective function was to minimize the cost of empty container repositioning including shipping, rening and shortage cost. In the model, shipping cost was decided by the number of ship used for empty container repositioning. The constraints of the model included meeting the need of empty containers, limit to the number of empty containers provided and the capacity of shipping. T

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36

Alaeddini, Atiye, and Daniel J. Klein. "Parallel Simultaneous Perturbation Optimization." Asia-Pacific Journal of Operational Research 36, no. 03 (2019): 1950009. http://dx.doi.org/10.1142/s021759591950009x.

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Stochastic computer simulations enable users to gain new insights into complex physical systems. Optimization is a common problem in this context: users seek to find model inputs that maximize the expected value of an objective function. The objective function, however, is time-intensive to evaluate, and cannot be directly measured. Instead, the stochastic nature of the model means that individual realizations are corrupted by noise. More formally, we consider the problem of optimizing the expected value of an expensive black-box function with continuously-differentiable mean, from which obser

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37

Chen, Zhi, Melvyn Sim, and Peng Xiong. "Robust Stochastic Optimization Made Easy with RSOME." Management Science 66, no. 8 (2020): 3329–39. http://dx.doi.org/10.1287/mnsc.2020.3603.

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We present a new distributionally robust optimization model called robust stochastic optimization (RSO), which unifies both scenario-tree-based stochastic linear optimization and distributionally robust optimization in a practicable framework that can be solved using the state-of-the-art commercial optimization solvers. We also develop a new algebraic modeling package, Robust Stochastic Optimization Made Easy (RSOME), to facilitate the implementation of RSO models. The model of uncertainty incorporates both discrete and continuous random variables, typically assumed in scenario-tree-based stoc

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38

Yu, Jie, Qizhi Feng, Yang Li, and Jinde Cao. "Stochastic Optimal Dispatch of Virtual Power Plant considering Correlation of Distributed Generations." Mathematical Problems in Engineering 2015 (2015): 1–8. http://dx.doi.org/10.1155/2015/135673.

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Virtual power plant (VPP) is an aggregation of multiple distributed generations, energy storage, and controllable loads. Affected by natural conditions, the uncontrollable distributed generations within VPP, such as wind and photovoltaic generations, are extremely random and relative. Considering the randomness and its correlation of uncontrollable distributed generations, this paper constructs the chance constraints stochastic optimal dispatch of VPP including stochastic variables and its random correlation. The probability distributions of independent wind and photovoltaic generations are de

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39

Oakley, David R., and Robert H. Sues. "Stochastic Optimization Using the Stochastic Preconditioned Conjugate Gradient Method." AIAA Journal 34, no. 9 (1996): 1969–71. http://dx.doi.org/10.2514/3.60034.

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40

Ye, Chencheng, and Ying Cui. "Stochastic Successive Convex Approximation for General Stochastic Optimization Problems." IEEE Wireless Communications Letters 9, no. 6 (2020): 755–59. http://dx.doi.org/10.1109/lwc.2019.2963032.

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41

Ermol'ev, Yu M., and V. I. Norkin. "Stochastic generalized gradient method for nonconvex nonsmooth stochastic optimization." Cybernetics and Systems Analysis 34, no. 2 (1998): 196–215. http://dx.doi.org/10.1007/bf02742069.

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42

Promsinchai, Porntip, Ali Farajzadeh, and Narin Petrot. "Stochastic Heavy-Ball Method for Constrained Stochastic Optimization Problems." Acta Mathematica Vietnamica 45, no. 2 (2020): 501–14. http://dx.doi.org/10.1007/s40306-019-00357-y.

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43

Ariyawansa, K. A., and Yuntao Zhu. "Stochastic semidefinite programming: a new paradigm for stochastic optimization." 4OR 4, no. 3 (2006): 239–53. http://dx.doi.org/10.1007/s10288-006-0016-2.

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44

Azanaw, Mr Girmay Mengesha. "Advanced Computational Methods for Simulating and Optimizing Stochastic Fracture: A Systematic Literature Review." International Journal of Emerging Science and Engineering 12, no. 10 (2024): 1–6. http://dx.doi.org/10.35940/ijese.h2579.12100924.

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Stochastic fracture processes, pervasive in diverse natural and engineered systems, pose intricate challenges for accurate simulation and optimization. This systematic literature review surveys the landscape of advanced computational methodologies to unravel and optimize stochastic fracture phenomena. Grounded in multidisciplinary perspectives spanning engineering, physics, and applied mathematics, the review navigates through the intricacies of simulation techniques and optimizations methods. From Finite Element Method (FEM) to Molecular Dynamics (MD) simulations, the review delineates the ev

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45

Arasteh, Abdollah. "Considering Stochastic and Combinatorial Optimization." Iranian Journal of Operations Research 7, no. 1 (2016): 69–84. http://dx.doi.org/10.29252/iors.7.1.69.

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46

Ren, Zhaolin, Zhengyuan Zhou, Linhai Qiu, Ajay Deshpande, and Jayant Kalagnanam. "Delay-Adaptive Distributed Stochastic Optimization." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (2020): 5503–10. http://dx.doi.org/10.1609/aaai.v34i04.6001.

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In large-scale optimization problems, distributed asynchronous stochastic gradient descent (DASGD) is a commonly used algorithm. In most applications, there are often a large number of computing nodes asynchronously computing gradient information. As such, the gradient information received at a given iteration is often stale. In the presence of such delays, which can be unbounded, the convergence of DASGD is uncertain. The contribution of this paper is twofold. First, we propose a delay-adaptive variant of DASGD where we adjust each iteration's step-size based on the size of the delay, and pro

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47

Klerkx, Rik, and Antoon Pelsser. "Narrative-based robust stochastic optimization." Journal of Economic Behavior & Organization 196 (April 2022): 266–77. http://dx.doi.org/10.1016/j.jebo.2022.02.007.

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48

Chen, Huifen, and Yendeng Huang. "STOCHASTIC OPTIMIZATION FOR SYSTEM DESIGN." Journal of the Chinese Institute of Industrial Engineers 23, no. 5 (2006): 357–70. http://dx.doi.org/10.1080/10170660609509332.

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49

Wang, Bin, Lotfollah Najjar, Neal N. Xiong, and Rung Ching Chen. "Stochastic Optimization: Theory and Applications." Journal of Applied Mathematics 2013 (2013): 1–2. http://dx.doi.org/10.1155/2013/949131.

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50

Duchi, John C., Peter L. Bartlett, and Martin J. Wainwright. "Randomized Smoothing for Stochastic Optimization." SIAM Journal on Optimization 22, no. 2 (2012): 674–701. http://dx.doi.org/10.1137/110831659.

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