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Journal articles on the topic 'Online Optimization'

Author: Grafiati

Published: 4 June 2021

Last updated: 26 July 2025

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1

Shalev-Shwartz, Shai. "Online Learning and Online Convex Optimization." Foundations and Trends® in Machine Learning 4, no. 2 (2011): 107–94. http://dx.doi.org/10.1561/2200000018.

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2

Delinchant, Benoit, Frédéric Wurtz, João Vasconcelos, and Jean-Louis Coulomb. "Framework for the optimization of online computable models." COMPEL: The International Journal for Computation and Mathematics in Electrical and Electronic Engineering 33, no. 3 (2014): 745–58. http://dx.doi.org/10.1108/compel-10-2012-0211.

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Purpose – The purpose of this paper is to make easily accessible models to test and compare the optimization algorithms we develop. Design/methodology/approach – For this, the paper proposes an optimization framework based on software component, web service, and plugin to exploit these models in different environments. Findings – The paper illustrates the discussion with optimizations in Matlab™ and R (www.r-project.org) of a transformer described and exploitable from the internet. Originality/value – The originality is to make easy implementation of simulation model and optimization algorithm

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3

Si Salem, Tareq, Gözde Özcan, Iasonas Nikolaou, Evimaria Terzi, and Stratis Ioannidis. "Online Submodular Maximization via Online Convex Optimization." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 13 (2024): 15038–46. http://dx.doi.org/10.1609/aaai.v38i13.29425.

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We study monotone submodular maximization under general matroid constraints in the online setting. We prove that online optimization of a large class of submodular functions, namely, threshold potential functions, reduces to online convex optimization (OCO). This is precisely because functions in this class admit a concave relaxation; as a result, OCO policies, coupled with an appropriate rounding scheme, can be used to achieve sublinear regret in the combinatorial setting. We also show that our reduction extends to many different versions of the online learning problem, including the dynamic

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4

Yuan, Deming, Alexandre Proutiere, and Guodong Shi. "Multi-agent Online Optimization." Foundations and Trends® in Optimization 7, no. 2-3 (2024): 81–263. https://doi.org/10.1561/2400000037.

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5

Lesage-Landry, Antoine, Iman Shames, and Joshua A. Taylor. "Predictive online convex optimization." Automatica 113 (March 2020): 108771. http://dx.doi.org/10.1016/j.automatica.2019.108771.

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6

Lesage-Landry, Antoine, and Julien Pallage. "Online dynamic submodular optimization." Automatica 167 (September 2024): 111758. http://dx.doi.org/10.1016/j.automatica.2024.111758.

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7

Goel, Gautam, and Adam Wierman. "An Online Algorithm for Smoothed Online Convex Optimization." ACM SIGMETRICS Performance Evaluation Review 47, no. 2 (2019): 6–8. http://dx.doi.org/10.1145/3374888.3374892.

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8

Goel, Gautam. "Smoothed Online Convex Optimization via Online Balanced Descent." ACM SIGMETRICS Performance Evaluation Review 46, no. 2 (2019): 42–44. http://dx.doi.org/10.1145/3305218.3305234.

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9

Yao Song, Yao Song, Limin Xiao Yao Song, Liang Wang Limin Xiao, Wei Wei Liang Wang, and Jinquan Wang Wei Wei. "Joint Online Optimization of Task Rescheduling and Data Redistribution." 網際網路技術學刊 24, no. 1 (2023): 011–22. http://dx.doi.org/10.53106/160792642023012401002.

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<p>Wide-area distributed computing environment is the main platform for storing large amounts of data and conducting wide-area computing. Tasks and data are jointly scheduled among multiple computing platforms to improve system efficiency. However, large network latency and limited bandwidth in wide-area networks may cause a large delay in scheduling information and data migration, which brings low task execution efficiency and a long time waiting for data. Traditional works mainly focus on allocating tasks based on data locality or distributing data replications, but optimizing task all

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10

Hazan, Elad. "Introduction to Online Convex Optimization." Foundations and Trends® in Optimization 2, no. 3-4 (2016): 157–325. http://dx.doi.org/10.1561/2400000013.

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11

Lin, Minghong, Adam Wierman, Alan Roytman, Adam Meyerson, and Lachlan L. H. Andrew. "Online optimization with switching cost." ACM SIGMETRICS Performance Evaluation Review 40, no. 3 (2012): 98–100. http://dx.doi.org/10.1145/2425248.2425275.

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12

Chen, Niangjun, Joshua Comden, Zhenhua Liu, Anshul Gandhi, and Adam Wierman. "Using Predictions in Online Optimization." ACM SIGMETRICS Performance Evaluation Review 44, no. 1 (2016): 193–206. http://dx.doi.org/10.1145/2964791.2901464.

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13

Chen, Niangjun, Anish Agarwal, Adam Wierman, Siddharth Barman, and Lachlan L. H. Andrew. "Online Convex Optimization Using Predictions." ACM SIGMETRICS Performance Evaluation Review 43, no. 1 (2015): 191–204. http://dx.doi.org/10.1145/2796314.2745854.

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14

Audibert, Jean-Yves, Sébastien Bubeck, and Gábor Lugosi. "Regret in Online Combinatorial Optimization." Mathematics of Operations Research 39, no. 1 (2014): 31–45. http://dx.doi.org/10.1287/moor.2013.0598.

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15

Hestermeyer, Thorsten, Eckehard Münch, and Erika Schäfer. "Model-Based Online Parameter Optimization." IFAC Proceedings Volumes 37, no. 14 (2004): 193–98. http://dx.doi.org/10.1016/s1474-6670(17)31103-5.

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16

Donmez, Mehmet A., Maxim Raginsky, and Andrew C. Singer. "Online Optimization Under Adversarial Perturbations." IEEE Journal of Selected Topics in Signal Processing 10, no. 2 (2016): 256–69. http://dx.doi.org/10.1109/jstsp.2015.2496911.

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17

Mahdian, Mohammad, Hamid Nazerzadeh, and Amin Saberi. "Online Optimization with Uncertain Information." ACM Transactions on Algorithms 8, no. 1 (2012): 1–29. http://dx.doi.org/10.1145/2071379.2071381.

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18

Avitabile, T., C. Mathieu, and L. Parkinson. "Online constrained optimization with recourse." Information Processing Letters 113, no. 3 (2013): 81–86. http://dx.doi.org/10.1016/j.ipl.2012.09.011.

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19

Li, Xiuxian, Lihua Xie, and Na Li. "A survey on distributed online optimization and online games." Annual Reviews in Control 56 (2023): 100904. http://dx.doi.org/10.1016/j.arcontrol.2023.100904.

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20

Yuan, Deming, Abhishek Bhardwaj, Ian Petersen, Elizabeth L. Ratnam, and Guodong Shi. "Towards online optimization for power grids." ACM SIGEnergy Energy Informatics Review 1, no. 1 (2021): 51–58. http://dx.doi.org/10.1145/3508467.3508472.

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In this note, we discuss potential advantages in extending distributed optimization frameworks to enhance support for power grid operators managing an influx of online sequential decisions. First, we review the state-of-the-art distributed optimization frameworks for electric power systems, and explain how distributed algorithms deliver scalable solutions. Next, we introduce key concepts and paradigms for online optimization, and present a distributed online optimization framework highlighting important performance characteristics. Finally, we discuss the connection and difference between offl

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21

Li, Xiuxian. "Recent advances on distributed online optimization." Control Theory and Technology 19, no. 1 (2021): 153–56. http://dx.doi.org/10.1007/s11768-021-00041-3.

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22

Lin, Qiulin, Yanfang Mo, Junyan Su, and Minghua Chen. "Competitive Online Optimization with Multiple Inventories." ACM SIGMETRICS Performance Evaluation Review 50, no. 1 (2022): 83–84. http://dx.doi.org/10.1145/3547353.3530969.

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We study an online inventory trading problem where a user seeks to maximize the aggregate revenue of trading multiple inventories over a time horizon. The trading constraints and concave revenue functions are revealed sequentially in time, and the user needs to make irrevocable decisions. The problem has wide applications in various engineering domains. Existing works employ the primal-dual framework to design online algorithms with sub-optimal, albeit near-optimal, competitive ratios (CR). We exploit the problem structure to develop a new divide-and-conquer approach to solve the online multi-

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23

Yamim, João Daniel Madureira, Carlos Cristiano Hasenclever Borges, and Raul Fonseca Neto. "Online Portfolio Optimization with Risk Control." Trends in Computational and Applied Mathematics 22, no. 3 (2021): 475–93. http://dx.doi.org/10.5540/tcam.2021.022.03.00475.

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Portfolio selection is undoubtedly one of the most challenging topics in the area of finance. Since Markowitz's initial contribution in 1952, portfolio allocation strategies have been intensely discussed in the literature. With the development of online optimization techniques, dynamic learning algorithms have proven to be an effective approach to building portfolios, although they do not assess the risk related to each investment decision.In this work, we compared the performance of the Online Gradient Descent (OGD) algorithm and a modification of the method, that takes into account risk metr

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24

He, Jianhao, Feidiao Yang, Jialin Zhang, and Lvzhou Li. "Quantum algorithm for online convex optimization." Quantum Science and Technology 7, no. 2 (2022): 025022. http://dx.doi.org/10.1088/2058-9565/ac5919.

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Abstract We explore whether quantum advantages can be found for the zeroth-order online convex optimization (OCO) problem, which is also known as bandit convex optimization with multi-point feedback. In this setting, given access to zeroth-order oracles (that is, the loss function is accessed as a black box that returns the function value for any queried input), a player attempts to minimize a sequence of adversarially generated convex loss functions. This procedure can be described as a T round iterative game between the player and the adversary. In this paper, we present quantum algorithms f

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25

Suenaga, Shinya, Yasuyuki Tada, and Hiroaki Seki. "Realization of Online Voltage Profile Optimization." IEEJ Transactions on Power and Energy 139, no. 4 (2019): 251–58. http://dx.doi.org/10.1541/ieejpes.139.251.

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26

Paschos, Georgios S., Apostolos Destounis, and George Iosifidis. "Online Convex Optimization for Caching Networks." IEEE/ACM Transactions on Networking 28, no. 2 (2020): 625–38. http://dx.doi.org/10.1109/tnet.2020.2968424.

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27

Shames, Iman, Daniel Selvaratnam, and Jonathan H. Manton. "Online Optimization Using Zeroth Order Oracles." IEEE Control Systems Letters 4, no. 1 (2020): 31–36. http://dx.doi.org/10.1109/lcsys.2019.2921593.

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28

Strokov, A. G., and I. L. Poz. "Convection flow optimization in online hemodiafiltration." Russian Journal of Transplantology and Artificial Organs 21, no. 4 (2020): 41–44. http://dx.doi.org/10.15825/1995-1191-2019-4-41-44.

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29

Tan, Bo, and R. Srikant. "Online Advertisement, Optimization and Stochastic Networks." IEEE Transactions on Automatic Control 57, no. 11 (2012): 2854–68. http://dx.doi.org/10.1109/tac.2012.2195810.

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30

Lilja, D. J., Lau Ying Kit, and B. Hamidzadeh. "Dynamic task scheduling using online optimization." IEEE Transactions on Parallel and Distributed Systems 11, no. 11 (2000): 1151–63. http://dx.doi.org/10.1109/71.888636.

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31

Ailon, Nir, Kohei Hatano, and Eiji Takimoto. "Bandit online optimization over the permutahedron." Theoretical Computer Science 650 (October 2016): 92–108. http://dx.doi.org/10.1016/j.tcs.2016.07.033.

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32

Awerbuch, Baruch, and Robert Kleinberg. "Online linear optimization and adaptive routing." Journal of Computer and System Sciences 74, no. 1 (2008): 97–114. http://dx.doi.org/10.1016/j.jcss.2007.04.016.

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33

Hall, Eric C., and Rebecca M. Willett. "Online Convex Optimization in Dynamic Environments." IEEE Journal of Selected Topics in Signal Processing 9, no. 4 (2015): 647–62. http://dx.doi.org/10.1109/jstsp.2015.2404790.

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34

Lin, Qiulin, Hanling Yi, John Pang, et al. "Competitive Online Optimization under Inventory Constraints." ACM SIGMETRICS Performance Evaluation Review 47, no. 1 (2019): 35–36. http://dx.doi.org/10.1145/3376930.3376953.

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35

Comden, Joshua, Sijie Yao, Niangjun Chen, Haipeng Xing, and Zhenhua Liu. "Online Optimization in Cloud Resource Provisioning." ACM SIGMETRICS Performance Evaluation Review 47, no. 1 (2019): 47–48. http://dx.doi.org/10.1145/3376930.3376961.

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36

Lin, Qiulin, Hanling Yi, John Pang, et al. "Competitive Online Optimization under Inventory Constraints." Proceedings of the ACM on Measurement and Analysis of Computing Systems 3, no. 1 (2019): 1–28. http://dx.doi.org/10.1145/3322205.3311081.

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37

Comden, Joshua, Sijie Yao, Niangjun Chen, Haipeng Xing, and Zhenhua Liu. "Online Optimization in Cloud Resource Provisioning." Proceedings of the ACM on Measurement and Analysis of Computing Systems 3, no. 1 (2019): 1–30. http://dx.doi.org/10.1145/3322205.3311087.

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38

Arnold, Matthew, Michael Hind, and Barbara G. Ryder. "Online feedback-directed optimization of Java." ACM SIGPLAN Notices 37, no. 11 (2002): 111–29. http://dx.doi.org/10.1145/583854.582432.

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39

Zarandioon, Saman, and Alexander Thomasian. "Optimization of online disk scheduling algorithms." ACM SIGMETRICS Performance Evaluation Review 33, no. 4 (2006): 42–46. http://dx.doi.org/10.1145/1138085.1138086.

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40

Shen, Jie, Huan Xu, and Ping Li. "Online optimization for max-norm regularization." Machine Learning 106, no. 3 (2017): 419–57. http://dx.doi.org/10.1007/s10994-017-5628-6.

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41

Van Hentenryck, Pascal, Russell Bent, and Eli Upfal. "Online stochastic optimization under time constraints." Annals of Operations Research 177, no. 1 (2009): 151–83. http://dx.doi.org/10.1007/s10479-009-0605-5.

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42

Rutten, Daan, Nicolas Christianson, Debankur Mukherjee, and Adam Wierman. "Smoothed Online Optimization with Unreliable Predictions." Proceedings of the ACM on Measurement and Analysis of Computing Systems 7, no. 1 (2023): 1–36. http://dx.doi.org/10.1145/3579442.

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We examine the problem of smoothed online optimization, where a decision maker must sequentially choose points in a normed vector space to minimize the sum of per-round, non-convex hitting costs and the costs of switching decisions between rounds. The decision maker has access to a black-box oracle, such as a machine learning model, that provides untrusted and potentially inaccurate predictions of the optimal decision in each round. The goal of the decision maker is to exploit the predictions if they are accurate, while guaranteeing performance that is not much worse than the hindsight optimal

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43

Rutten, Daan, Nicolas Christianson, Debankur Mukherjee, and Adam Wierman. "Smoothed Online Optimization with Unreliable Predictions." ACM SIGMETRICS Performance Evaluation Review 51, no. 1 (2023): 71–72. http://dx.doi.org/10.1145/3606376.3593570.

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We consider online optimization with switching costs in a normed vector space (X, ||·||) wherein, at each time t, a decision maker observes a non-convex hitting cost function ƒ : t X →[0, ∞] and must decide upon some xt∈X→, paying ƒt (xt) + || xt-xt-1||, where ||·|| characterizes the switching cost. Throughout, we assume that ƒt is globally α-polyhedral, i.e., ƒt has a unique minimizer υt ∈X, and, for all x ∈ X, ƒ t) (x) ≥ ƒt + α · ||x - υ t. Moreover, we assume that the decision maker has access to an untrusted prediction xt of the optimal decision during each round, such as the decision sugg

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44

Hu, Yuhan, Yawei Zhao, Lailong Luo, and Deke Guo. "Boosting for Distributed Online Convex Optimization." Tsinghua Science and Technology 28, no. 4 (2023): 811–21. http://dx.doi.org/10.26599/tst.2022.9010041.

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45

Waldner, Stephan, Patrick Biedermann, and Silvia Schwyn Thony. "Online re-optimization of optical filters on a production sputter tool." Chinese Optics Letters 11, S1 (2013): S10207. http://dx.doi.org/10.3788/col201311.s10207.

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46

Simmons, Michelle, and Tim Flannery. "Maximizing Online Visibility, the Importance of Search Engine Optimization on Google." International Journal of Research Publication and Reviews 4, no. 5 (2023): 1462–66. http://dx.doi.org/10.55248/gengpi.234.5.38049.

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47

M G, Saji, and Renju Kalarikkal. "Online Security and Optimization Powered by Fingerprint in Online Voting System." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 07, no. 03 (2023): 1–8. https://doi.org/10.55041/ijsrem18098.

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Election is a process in which voters choose their representatives and express their preferences for the way that they will be governed. Using the decade old voting system to collect votes is no longer considered efficient due to the various recurring errors. The advancement of information and telecommunications technologies allow for a fully automated online computerized election process. An electronic voting system defines rules for valid voting and gives an efficient method of counting votes, which are aggregated to yield a result. Moreover, electronic voting systems can improve voter ident

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48

Yao, Jianming, and Mengjie Gu. "Optimization Analysis of Supply Chain Resource Allocation in Customized Online Shopping Service Mode." Mathematical Problems in Engineering 2015 (2015): 1–11. http://dx.doi.org/10.1155/2015/519125.

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For an online-shopping company, whether it can provide its customers with customized service is the key to enhance its customers’ experience value and its own competence. A good customized service requires effective integration and reasonable allocation of the company’s supply chain resources running in the background. Based on the analysis of the allocation of supply chain resources in the customized online shopping service mode and its operational characteristics, this paper puts forward an optimization model for the resource allocation and builds an improved ant algorithm to solve it. Final

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49

Gheorghe, Simona, Mirona Popescu, and Anca Alexandra Purcărea. "A model of business intelligence and online marketing for commercial." Balkan Region Conference on Engineering and Business Education 2, no. 1 (2017): 267–74. http://dx.doi.org/10.1515/cplbu-2017-0035.

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Abstract Technology is expanding at a speed previously unsurpassed; therefore an emphasis is made on integration, optimization and increasing efficiency on different fields. Business intelligence is a new concept which became popular alongside online marketing, as both use external and internal data in order to make better decisions, process improvement and optimizations. The aim of this paper is to propose a solution for business intelligence in the commercial field based on a model of internal sales platform. Gathering data for all the structures integrated in this platform improves the opti

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50

Ho-Nguyen, Nam, and Fatma Kılınç-Karzan. "Exploiting problem structure in optimization under uncertainty via online convex optimization." Mathematical Programming 177, no. 1-2 (2018): 113–47. http://dx.doi.org/10.1007/s10107-018-1262-8.

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