Preprint

1. M. Obara, T. Okuno, and A. Takeda. A primal-dual interior point trust region method for inequality-constrained optimization problems on Riemannian manifolds. arXiv:2501.15419, Jan. 2025. [arXiv]
2. S. Akiyama*, M. Obara*, and Y. Kawase. Optimal design of lottery with cumulative prospect theory. arXiv:2209.00822, Sep. 2022. (*equal contribution) [arXiv]

Journal Papers

1. M. Obara, K. Sato, H. Sakamoto, T. Okuno, and A. Takeda. Stable linear system identification with prior knowledge by Riemannian sequential quadratic optimization. IEEE Transactions on Automatic Control, 69(3), pp. 2060–2066, 2024. [Journal] [arXiv]
2. M. Obara, T. Okuno, and A. Takeda. Sequential quadratic optimization for nonlinear optimization problems on Riemannian manifolds. SIAM Journal on Optimization, 32(3), pp. 822–853, 2022. [Journal] [arXiv]
3. 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. Japan Society of Traffic Engineers Special edition, 4(1), pp.A286-A293, 2018. (in Japanese)

Proceedings

1. M. Obara, T. Kashiyama, and Y. Sekimoto. Deep reinforcement learning approach for train rescheduling utilizing graph theory. 2018 IEEE international conference on big data (Big Data), pp. 4525–4533, Seattle, USA, Dec. 2018. (workshop paper)
2. M. Obara, T. Kashiyama, Y. Sekimoto, and H. Omata. Analysis of public vehicle use with long-term GPS data and the possibility of use optimization – through working car project. The third international conference on smart portable, wearable, implantable, and disability-oriented devices and systems (SPWID 2017), Venice, Italy, Jun. 2017. (acceptance rate 26%. Best paper award)