Preprints

1. M. Obara, T. Okuno, and A. Takeda. A primal-dual interior point trust region method for second-order stationary points of Riemannian inequality-constrained optimization problems. arXiv:2501.15419, May 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 Articles

1. M. Obara, T. Okuno, and A. Takeda. Local near-quadratic convergence of Riemannian interior point methods. Journal of Optimization Theory and Applications (accepted for publication). [arXiv]
2. 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]
3. 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]
4. 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)

Conference 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 3rd 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)