| Project/Area Number |
21K17710
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| Research Category |
Grant-in-Aid for Early-Career Scientists
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| Allocation Type | Multi-year Fund |
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| Review Section |
Basic Section 60020:Mathematical informatics-related
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| Research Institution | Kyushu University |
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Principal Investigator |
Themelis Andreas 九州大学, システム情報科学研究院, 准教授 (50898749)
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| Project Period (FY) |
2021-04-01 – 2024-03-31
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| Project Status |
Granted (Fiscal Year 2022)
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| Budget Amount *help |
¥3,120,000 (Direct Cost: ¥2,400,000、Indirect Cost: ¥720,000)
Fiscal Year 2023: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2022: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2021: ¥780,000 (Direct Cost: ¥600,000、Indirect Cost: ¥180,000)
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| Keywords | Optimization algorithms / Nonconvex optimization / Adaptive algorithms / Autonomous driving / Decentralized control / Open-source software |
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| Outline of Research at the Start |
The project is concerned with optimization algorithms for engineering, in compliance with the application challenges: efficiency, limited power of microprocessors, and nonconvexity of the problems. The final goal is to provide efficient open-source multi-purpose software with theoretical guarantees.
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| Outline of Annual Research Achievements |
The candidate established a new collaboration with the University of British Columbia (CA), including in the discussion a PhD student under his co-supervision that resulted in the preprint [1]. In addition, he has been invited for visiting periods at the Tokyo Institute of Technology (December 2022), the University of Pisa (scheduled for May 2023), and the Chongqing Normal University (scheduled for August 2023). His research trend continues along the lines of the previous year, with additional focus on linesearch-free methods for convex optimization [2] and (convex) optimization for power-grid expansion planning [4]. As in the past, a first inquiry in the convex realm is meant to serve as foundation for potential nonconvex extensions, ultimate target of the proposal.
[1] Z Wang, AT, H Ou, and X Wang. A mirror inertial forward-reflected-backward splitting: Global convergence and linesearch extension beyond convexity and Lipschitz smoothness, arXiv:2212.01504, 2022
[2] P Latafat, AT, L Stella, and P Patrinos. Adaptive proximal algorithms for convex optimization under local Lipschitz continuity of the gradient, arXiv:2301.04431, 2023
[3] S Hardy, AT, K Yamamoto, H Ergun, and D Van Hertem. Optimal grid layouts for hybrid offshore assets in the North Sea under different market designs, arXiv:2301.00931, 2023
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| Current Status of Research Progress |
Current Status of Research Progress
2: Research has progressed on the whole more than it was originally planned.
Reason
As mentioned in the future plans section, the only promised goal that the candidate missed is the writing of an extended monograph. This change of plan was prompted by a more pressing urge to tie new international collaborations and produce novel publications, all successfully achieved in the candidate's opinion, whereas the monograph is meant to cover already established material with previous co-authors. Furthermore, the previously halted software development is now being handled by international collaborators who take care of simulations in co-authored papers and keep publicly available open-source code up to date with the most recent findings.
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| Strategy for Future Research Activity |
Differently from what intended at the beginning of last year, the candidate put on halt the writing of the extended monograph for the Foundations and Trends in Optimization and gave priority to the establishment of new collaborations and the development of novel publications. Having succeeded in publishing and submitting a considerable amount of novel material, he believes that the halted project can now resume without impediments.
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