Mitsuaki Obara

Mitsuaki Obara

Ph.D. student at The University of Tokyo

Talks and Presentations

  1. M. Obara, K. Sato, T. Okuno, and A. Takeda. Riemannian linear system identification with prior knowledge. Seminars on Optimization Methods and Algorithms (SOMA), Tokyo, Japan, Jun. 2022.

  2. M. Obara, T. Okuno, and A. Takeda. Extension of SQP methods to constrained optimization problems on Riemannian manifolds. The 22nd Conference of the International Federation of Operational Research Societies (IFORS), Seoul, Korea (virtual), Aug. 2021.

  3. M. Obara, T. Okuno, A. Takeda, and K. Sato. Riemannian sequential quadratic optimization method and its application to linear system identification, Advances in the theory and application of mathematical optimization, Kyoto, Japan (virtual), Aug. 2021.

  4. M. Obara, T. Okuno, and A. Takeda. Sequential quadratic optimization for nonlinear optimization problems on Riemannian manifolds. SIAM Conference on Optimization (OP), Hong Kong (virtual), Jul. 2021.

  5. M. Obara, T. Okuno, and A. Takeda. Sequential quadratic optimization for nonlinear optimization problems on Riemannian manifolds. Optimization: modeling and algorithm, Tokyo, Japan (virtual), Mar. 2021.

  6. M. Obara, T. Okuno, and A. Takeda. Sequential quadratic optimization for nonlinear optimization problems on Riemannian manifolds. The 2020 spring national conference of operations research society of Japan, Nara, Japan (cancelled due to COVID-19), Mar. 2020.

  7. M. Obara, T. Kashiyama, and Y. Sekimoto. Deep reinforcement learning approach for train rescheduling utilizing graph theory. The 39th conference of Japan society of traffic engineers, Tokyo, Japan, Aug. 2019.

  8. M. Obara and H. Hirai. Fair revenue sharing on polyhedral clinching auctions for two-sided markets. Workshop on optimization and its applications (OPTA), Tsukuba, Japan, Jun. 2019.

  9. M. Obara, T. Kashiyama, and Y. Sekimoto. Deep reinforcement learning approach for train rescheduling utilizing graph theory. Applications of big data in the transport industry – A workshop of the IEEE international conference on big data (Big Data), Seattle, USA, Dec. 2018.

  10. M. Obara, T. Kashiyama, Y. Sekimoto, and H. Omata. The analysis of public-owned vehicle use with long-term GPS data and the possibility of use optimization: A case study in a working car project. The 37th conference of Japan society of traffic engineers, Tokyo, Japan, Aug. 2017.