[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122463-en":3,"doc-seo-122463-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},122463,1374391974564,"Clementine","https://ap-avatar.wpscdn.com/avatar/14000253aa45c000a9e?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779874745381141002",8,"Research & Report","Bridging Learning and Optimization - Advanced Algorithms for Combinatorial and Stochastic Optimization in Power Systems and Machine Learning","The dissertation develops advanced learning-optimization algorithms for solving combinatorial and stochastic optimization problems arising in power systems and machine learning. It bridges data-driven learning with optimization structure by leveraging deep reinforcement learning and physics-informed graph learning to address large-scale decision-making tasks such as unit commitment and distribution network reconfiguration. The work emphasizes improved convergence behavior, robustness under uncertainty, and practical operational modeling for unbalanced distribution systems and voltage regulation.","UC Riverside  \nUC Riverside Electronic Theses and Dissertations  \nTitle  \nBridging Learning and Optimization: Advanced Algorithms for Combinatorial and Stochastic Optimization in Power Systems and Machine Learning  \nPermalink  \n[https://escholarship.org/uc/item/5tr630nh](https://escholarship.org/uc/item/5tr630nh)  \nISBN  \n9798263310073  \nAuthor  \nQin, Jingtao  \nPublication Date  \n2025-08-15  \nCopyright Information  \nThis work is made available under the terms of a Creative Commons Attribution License, available at [https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)  \n[Peer reviewed|Thesis/dissertation](Peer reviewed|Thesis/dissertation)  \n[eScholarship.org](eScholarship.org) Powered by the California Digital Library  \nUniversity of California  \nUNIVERSITY OF CALIFORNIA  \nRIVERSIDE  \nBridging Learning and Optimization: Advanced Algorithms for Combinatorial and Stochastic Optimization in Power Systems and Machine Learning  \nA Dissertation submitted in partial satisfaction of the requirements for the degree of  \nDoctor of Philosophy  \nin  \nElectrical Engineering  \nby  \nJingtao Qin  \nSeptember 2025  \nDissertation Committee:  \nDr. Nanpeng Yu, Chairperson  \nDr. Wei Ren  \nDr. Sheldon Tan  \nDr. Yuzhou Chen  \nCopyright by Jingtao Qin 2025  \nThe Dissertation of Jingtao Qin is approved:  \n\n|  |\n| --- |\n|  |\n|  |\n\nCommittee Chairperson  \nUniversity of California, Riverside  \nAcknowledgments  \nFirst and foremost, I extend my deepest gratitude to my advisor, Dr. Nanpeng Yu, whose unwavering support, insightful guidance, and continuous encouragement have been instrumental throughout my Ph.D. journey. His mentorship has shaped not only my academic development but also my professional outlook and personal growth. I am especially thankful for his support during my research, internship, and career planning. Under his guidance, I have gained not only a doctoral degree but also invaluable life lessons and enduring professional skills.  \nI am profoundly grateful to Dr. Mikhail Bragin for his impactful collaboration and mentorship. His exceptional expertise, unique perspective, and thoughtful feedback have significantly influenced the direction and quality of my research.  \nMy sincere thanks also go to Dr. Wei Ren and Dr. Sheldon Tan for their constructive advice and encouragement during my oral qualifying examination and for their continued support throughout the completion of my dissertation.  \nI would also like to acknowledge my mentors during my internships, Dr. Rui Yang and Dr. Hongbo Sun, for their valuable guidance and support, which have contributed greatly to my career development.  \nI am truly appreciative of the collaboration and camaraderie of my colleagues: Yuanqi Gao, Koji Yamasita, Yuanbin Cheng, Shaorong Zhang, Zhentong Shao, and Anbang Liu. Your insights and teamwork have been a vital part of my research experience.  \nMy time at the University of California, Riverside has been a deeply fulfilling chapter of my life. I am grateful to all the professors whose courses broadened my knowledge and to the fellow researchers and friends who made this journey intellectually enriching and personally memorable.  \nI also wish to thank the many individuals—too numerous to name—whose support, feedback, and encouragement have contributed to my research and academic journey.  \nFinally, and most importantly, I owe everything to my parents, Zhaowen Qin and Xinling Shui. Their unconditional love, steadfast support, and unwavering belief in me have been the foundation of all my achievements. From childhood through today, their encouragement has given me the freedom to pursue my dreams and the strength to overcome every challenge.  \nThe content of this dissertation is a reprint of the materials that appeared in the following publications:  \n• Qin, J., Yu, N. and Gao, Y., 2021, October. Solving unit commitment problems with multistep deep reinforcement learning. In 2021 IEEE international conference on communicati","cbCaihX51hJIQ1Df","https://ap.wps.com/l/cbCaihX51hJIQ1Df","pdf",5807630,1,244,"English","en",105,"# Acknowledgments\n## Research contributions and collaborations\n## Publications reprinted in the dissertation\n# ABSTRACT OF THE DISSERTATION","[{\"question\":\"What is the dissertation’s central theme?\",\"answer\":\"It bridges learning and optimization by developing advanced algorithms for combinatorial and stochastic optimization in power systems and machine learning.\"},{\"question\":\"Which methodological directions are highlighted?\",\"answer\":\"The document emphasizes deep reinforcement learning and physics-informed graph learning to tackle operational decision problems under uncertainty.\"},{\"question\":\"What application problems are mentioned as targets?\",\"answer\":\"It focuses on unit commitment and distribution network reconfiguration, including collaborative dynamic reconfiguration and voltage regulation in unbalanced distribution systems.\"}]","Bridging Learning and Optimization - 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