Relevant bibliographies by topics / Online Optimization

Contents

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

Academic literature on the topic 'Online Optimization'

Author: Grafiati
Published: 4 June 2021
Last updated: 26 July 2025

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Journal articles on the topic "Online Optimization"

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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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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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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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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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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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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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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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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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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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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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Dissertations / Theses on the topic "Online Optimization"

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Lu, Xin Ph D. Massachusetts Institute of Technology Operations Research Center. "Online optimization problems." Thesis, Massachusetts Institute of Technology, 2013. http://hdl.handle.net/1721.1/82724.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2013.<br>This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Cataloged from student-submitted PDF version of thesis.<br>Includes bibliographical references (pages 149-153).<br>In this thesis, we study online optimization problems in routing and allocation applications. Online problems are problems where information is revealed incrementally, and decisions must be made before all informat
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Sinha, Deeksha. "Optimization for online platforms." Thesis, Massachusetts Institute of Technology, 2021. https://hdl.handle.net/1721.1/130723.

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Thesis: Ph. D., Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, February, 2021<br>Cataloged from the official PDF of thesis.<br>Includes bibliographical references (pages 179-189).<br>In the last decade, there has been a surge in online platforms for providing a wide variety of services. These platforms face an array of challenges that can be mitigated with appropriate modeling and the use of optimization tools. In this thesis, we examine, model, and provide solutions to some of the key challenges. First, we focus on the problem of intelligent SMS
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Wagner, Michael R. (Michael Robert) 1978. "Online optimization in routing and scheduling." Thesis, Massachusetts Institute of Technology, 2006. http://hdl.handle.net/1721.1/36225.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2006.<br>Includes bibliographical references (leaves 169-176).<br>In this thesis we study online optimization problems in routing and scheduling. An online problem is one where the problem instance is revealed incrementally. Decisions can (and sometimes must) be made before all information is available. We design and analyze (polynomial-time) online algorithms for a variety of problems. We utilize worst-case competitive ratio (and relaxations thereof), asymptotic and Monte Carlo simu
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Talebi, Mazraeh Shahi Mohammad Sadegh. "Online Combinatorial Optimization under Bandit Feedback." Licentiate thesis, KTH, Reglerteknik, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-181321.

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Multi-Armed Bandits (MAB) constitute the most fundamental model for sequential decision making problems with an exploration vs. exploitation trade-off. In such problems, the decision maker selects an arm in each round and observes a realization of the corresponding unknown reward distribution. Each decision is based on past decisions and observed rewards. The objective is to maximize the expected cumulative reward over some time horizon by balancing exploitation (arms with higher observed rewards should be selectedoften) and exploration (all arms should be explored to learn their average rewar
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Feng, Siyuan. "Online Hierarchical Optimization for Humanoid Control." Research Showcase @ CMU, 2016. http://repository.cmu.edu/dissertations/822.

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This thesis presents an online approach for controlling humanoid robots using hierarchical optimization. While our primary focus is to develop a fast and robust walking controller that is able to follow desired foot steps, full body manipulation capability is also achieved. The proposed hierarchical system consists of three levels: a high level trajectory optimizer that generates nominal center of mass and swing foot trajectories, together with useful information such as a local value function approximation and a linear policy along the nominal trajectory; a middle level receding-horizon contr
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Korolko, Nikita (Nikita E. ). "A robust optimization approach to online problems." Thesis, Massachusetts Institute of Technology, 2017. http://hdl.handle.net/1721.1/112013.

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Thesis: Ph. D., Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2017.<br>This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Cataloged from student-submitted PDF version of thesis.<br>Includes bibliographical references (pages 149-155).<br>In this thesis, we consider online optimization problems that are characterized by incrementally revealed input data and sequential irrevocable decisions that must be made without complete knowledge of the future. W
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Gupta, Swati Ph D. Massachusetts Institute of Technology. "Combinatorial structures in online and convex optimization." Thesis, Massachusetts Institute of Technology, 2017. http://hdl.handle.net/1721.1/112014.

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Thesis: Ph. D., Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2017.<br>This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Cataloged from student-submitted PDF version of thesis.<br>Includes bibliographical references (pages 157-163).<br>Motivated by bottlenecks in algorithms across online and convex optimization, we consider three fundamental questions over combinatorial polytopes. First, we study the minimization of separable strictly convex funct
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Chen, Annie I.-An. "Large-scale optimization in online-retail inventory management." Thesis, Massachusetts Institute of Technology, 2017. http://hdl.handle.net/1721.1/111854.

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Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017.<br>This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Cataloged from student-submitted PDF version of thesis.<br>Includes bibliographical references (pages 225-232).<br>This thesis studies the related decisions of inventory placement and inventory replenishment in online retail. The placement decision focuses on the selection of fulfillment centers in which to place items, while
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Sakaue, Shinsaku. "Online, Submodular, and Polynomial Optimization with Discrete Structures." Kyoto University, 2020. http://hdl.handle.net/2433/253424.

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De, Filippo Allegra <1986&gt. "Hybrid Offline/Online Methods for Optimization Under Uncertainty." Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2020. http://amsdottorato.unibo.it/9425/1/DeFilippo_Allegra_tesi.pdf.

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This work considers multi-stage optimization problems under uncertainty. In this context, at each stage some uncertainty is revealed and some decision must be made: the need to account for multiple future developments makes stochastic optimization incredibly challenging. Due to such a complexity, the most popular approaches depend on the temporal granularity of the decisions to be made. These approaches are, in general, sampling-based methods and heuristics. Long-term strategic decisions (which are often very impactful) are typically solved via expensive, but more accurate, sampling-based appr
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Books on the topic "Online Optimization"

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Hentenryck, Pascal Van. Online stochastic combinatorial optimization. MIT Press, 2009.

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Grötschel, Martin. Online optimization of large scale systems. Springer, 2010.

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Grötschel, Martin, Sven O. Krumke, and Jörg Rambau, eds. Online Optimization of Large Scale Systems. Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-662-04331-8.

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Ahuja, Ravindra K., Rolf H. Möhring, and Christos D. Zaroliagis, eds. Robust and Online Large-Scale Optimization. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-05465-5.

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Martin, Grötschel, Krumke Sven O, and Rambau Jörg, eds. Online optimization of large scale systems. Springer, 2001.

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Borodin, Allan. Online computation and competitive analysis. Cambridge University Press, 1998.

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S, Sastry P., ed. Networks of learning automata: Techniques for online stochastic optimization. Kluwer Academic, 2004.

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Thathachar, Mandayam A. L. Networks of learning automata: Techniques for online stochastic optimization. Kluwer Academic, 2003.

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WAOA 2008 (2008 Karlesruhe, Germany). Approximation and online algorithms: 6th international workshop, WAOA 2008, Karlsruhe, Germany, September 18-19, 2008 : revised papers. Springer, 2009.

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author, Velinov Ivo, ed. Online Visual Merchandising: Structural Elements And Optimization For Apparel Web Stores. LAP LAMBERT Academic Publishing, 2014.

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Book chapters on the topic "Online Optimization"

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Tan, Zhiyi, and An Zhang. "Online and Semi-online Scheduling." In Handbook of Combinatorial Optimization. Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4419-7997-1_2.

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Ho-Nguyen, Nam. "Online Convex Optimization." In Encyclopedia of Optimization. Springer Nature Switzerland, 2024. https://doi.org/10.1007/978-3-030-54621-2_871-1.

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Henning, Bernd. "Brand Optimization." In Online Targeting und Controlling. Gabler, 2011. http://dx.doi.org/10.1007/978-3-8349-6742-8_13.

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Ascheuer, N., M. Grötschel, S. O. Krumke, and J. Rambau. "Combinatorial Online Optimization." In Operations Research Proceedings 1998. Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/978-3-642-58409-1_2.

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Kalyanasundaram, Bala, and Kirk Pruhs. "On-line network optimization problems." In Online Algorithms. Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/bfb0029573.

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Karrenbauer, Andreas, and Elizaveta Kovalevskaya. "Reading Articles Online." In Combinatorial Optimization and Applications. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-64843-5_43.

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Anthony, Barbara M., and Christine Chung. "Online Bottleneck Matching." In Combinatorial Optimization and Applications. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-31770-5_23.

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Yu, Yaoliang. "Online Learning and Optimization." In Encyclopedia of Algorithms. Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4939-2864-4_265.

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Zhou, Hao, and Kefa Cen. "Online Combustion Optimization System." In Advanced Topics in Science and Technology in China. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-7875-0_7.

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Yu, Yaoliang. "Online Learning and Optimization." In Encyclopedia of Algorithms. Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-642-27848-8_265-2.

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Conference papers on the topic "Online Optimization"

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Jiwatode, Mohit, Leon Schlecht, and Alexander Dockhorn. "Online Optimization of Curriculum Learning Schedules using Evolutionary Optimization." In 2024 IEEE Conference on Games (CoG). IEEE, 2024. http://dx.doi.org/10.1109/cog60054.2024.10645570.

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Nayak, Tanvi S., and B. N. Bharath. "Improved Bounds For Online Convex Optimization." In ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2025. https://doi.org/10.1109/icassp49660.2025.10887754.

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Li, Jianjun, Lipo Mo, Juan Shang, and Min Zuo. "Forgetting-Factor Regret for Distributed Online Optimization." In 2024 43rd Chinese Control Conference (CCC). IEEE, 2024. http://dx.doi.org/10.23919/ccc63176.2024.10662158.

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Wen, Dacheng, Yupeng Li, Xiaoxi Zhang, and Francis C. M. Lau. "Robust Decentralized Online Optimization Against Malicious Agents." In 2024 IEEE 44th International Conference on Distributed Computing Systems (ICDCS). IEEE, 2024. http://dx.doi.org/10.1109/icdcs60910.2024.00020.

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Lai, Joyce, and Peter Seiler. "Robust Online Convex Optimization for Disturbance Rejection." In 2024 IEEE 63rd Conference on Decision and Control (CDC). IEEE, 2024. https://doi.org/10.1109/cdc56724.2024.10886354.

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Salaün, Corentin, Xingchang Huang, Iliyan Georgiev, Niloy Mitra, and Gurprit Singh. "Online Importance Sampling for Stochastic Gradient Optimization." In 14th International Conference on Pattern Recognition Applications and Methods. SCITEPRESS - Science and Technology Publications, 2025. https://doi.org/10.5220/0013311100003905.

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Liu, Weiguo, Shi Zhong, Mayank Chaudhary, and Shyam Kapur. "Online Advertisement Campaign Optimization." In 2007 IEEE International Conference on Service Operations and Logistics, and Informatics. IEEE, 2007. http://dx.doi.org/10.1109/soli.2007.4383887.

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Awerbuch, Baruch, Yossi Azar, and Adam Meyerson. "Reducing truth-telling online mechanisms to online optimization." In the thirty-fifth ACM symposium. ACM Press, 2003. http://dx.doi.org/10.1145/780542.780616.

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Ahmad, Falih, and Eric O Neill. "Electromagnetic Profile Inversion through Optimization." In International Engineering Science Technology Online Conference. Cloud Publications, 2018. http://dx.doi.org/10.23953/cloud.iestoc.371.

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Wallace, Cory S., Paul M. Santi, and Gabriel Walton. "DEVELOPMENT AND OPTIMIZATION OF A LANDSLIDE RUNOUT SCORING SYSTEM." In GSA 2020 Connects Online. Geological Society of America, 2020. http://dx.doi.org/10.1130/abs/2020am-354605.

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Reports on the topic "Online Optimization"

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Raj Rajamani, Jose Delgadillo, and Vishal Duriseti. ONLINE SAG MILL PULSE MEASUREMENT AND OPTIMIZATION. Office of Scientific and Technical Information (OSTI), 2006. http://dx.doi.org/10.2172/895022.

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Raj Rajamani, Jose Delgadillo, and Vishal Duriseti. Online SAG Mill Pluse Measurement and Optimization. Office of Scientific and Technical Information (OSTI), 2007. http://dx.doi.org/10.2172/969329.

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Jaillet, Patrick. Data-Driven Online and Real-Time Combinatorial Optimization. Defense Technical Information Center, 2013. http://dx.doi.org/10.21236/ada592939.

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Garcia, Alfredo. Bio-Inspired Schemes for Global Optimization and Online Distributed Search. Defense Technical Information Center, 2012. http://dx.doi.org/10.21236/ada567710.

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Gu, Xiaofeng, T. Kanesue, M. Okamura, et al. EBIS BEAM INTENSITY ONLINE OPTIMIZATION WITH GPTUNE AND OFFLINE ANALYSIS WITH XGBOOST. Office of Scientific and Technical Information (OSTI), 2024. http://dx.doi.org/10.2172/2331235.

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Yang, Xi. Online Optimization of NSLS-II Dynamic Aperture and Injection Transient (Facility Improvement Project). Office of Scientific and Technical Information (OSTI), 2021. http://dx.doi.org/10.2172/1837223.

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Shen, Bo. Extending ORNL HPDM capabilities for design and optimization of new refrigerant blends - FY18 3rd quarterly milestone: a trial version published online for modeling and design ac units using low GWP refrigerants. Office of Scientific and Technical Information (OSTI), 2018. http://dx.doi.org/10.2172/1470863.

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Danylchuk, Hanna B., and Serhiy O. Semerikov. Advances in machine learning for the innovation economy: in the shadow of war. Криворізький державний педагогічний університет, 2023. http://dx.doi.org/10.31812/123456789/7732.

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This preface introduces the selected and revised papers presented at the 10th International Conference on Monitoring, Modeling &amp; Management of Emergent Economy (M3E2 2022), held online in Ukraine, on November 17-18, 2022. The conference aimed to bring together researchers, practitioners, and students from various fields to exchange ideas, share experiences, and discuss challenges and opportunities in applying computational intelligence and data science for the innovation economy. The innovation economy is a term that describes the emerging paradigm of economic development that is driven by
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LaRaine Ingram, Keisha. Applied Sales Predictive Analytics for Business Development. Vilnius Business College, 2024. http://dx.doi.org/10.57005/ab.2024.1.2.

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In the dynamic business environment, leveraging predictive analytics for sales optimization and business development has become crucial for achieving sustained growth. As the e-commerce landscape continues to evolve, many e-businesses must harness the power of predictive analytics to anticipate sales trends and optimize business development strategies. This paper explores the application of sales predictive analytics, focusing on its role in forecasting sales, optimizing resource allocation, and enhancing customer relationship management. The application of predictive analytics in sales foreca
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Mashingia, Jane, S. Maboko, P. I. Mbwiri, et al. Joint Medicines Regulatory Procedure in the East African Community: Registration Timelines and Way Forward. Purdue University, 2021. http://dx.doi.org/10.5703/1288284317429.

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A review of the East African Community (EAC) joint regulatory review process was conducted, registration timelines analyzed and key milestones, challenges and opportunities documented for the period of July 2015 to January 2020. A total of 113 applications were submitted for joint scientific review. Among these, 109 applications were assessed, 57 were recommended for marketing authorisation, 52 applications had queries to applicants and four applications were under review. A total median approval time for all products ranged from 53 to 102 days. The maximum time taken by a regulator to review
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