Zeroth-order random subspace algorithm for non-smooth convex optimization

Ryota Nozawa, Pierre-Louis Poirion,  and Akiko Takeda:
Zeroth-Order Random Subspace Algorithm for Non-smooth Convex Optimization
Journal of Optimization Theory and Applications, 204, 53 (2025). 

• The published version is available here. 

• A preprint is available at arXiv.

• Our code is publicly available at GitHub.

A Framework for Bilevel Optimization on Riemannian Manifolds 

Andi Han, Bamdev Mishra,  Pratik  Jawanpuria, and Akiko  Takeda:
A framework for bilevel optimization on Riemannian manifolds,
The 38th Conference on Neural Information Processing Systems (NeurIPS 2024), 2024.  

• The published version is available here. 

• Our code is publicly available at GitHub.

Accelerated-gradient-based generalized Levenberg--Marquardt method with oracle complexity bound and local quadratic convergence

Naoki Marumo, Takayuki Okuno,  and Akiko Takeda:
Accelerated-gradient-based generalized Levenberg--Marquardt method with oracle complexity bound and local quadratic convergence",
Mathematical Programming, 213, pp.771-822, 2025. 

• The published version is available here. 

• Our code is publicly available at GitHub.

Majorization-minimization-based Levenberg–Marquardt method for constrained nonlinear least squares

Naoki Marumo, Takayuki Okuno,  and Akiko Takeda:
Majorization-minimization-based Levenberg–Marquardt method for constrained nonlinear least squares,
Computational Optimization and Applications, 84, pp. 833-874, 2023. 

• The published version is available here. 

• A preprint is available at arXiv.

• Our code is publicly available at GitHub.

Sequential quadratic optimization for nonlinear optimization problems on Riemannian manifolds

Mitsuaki Obara, Takayuki Okuno, and Akiko Takeda:
Sequential quadratic optimization for nonlinear optimization problems on Riemannian manifolds,
SIAM Journal on Optimization 32(2), pp.822--853, 2022.

• The published version is available here. 

• A preprint is available at arXiv.

• Our code is freely available at GitHub.

A gradient method for multilevel optimization

Ryo Sato, Mirai Tanaka, and Akiko Takeda:
A gradient method for multilevel optimization, 
Advances in Neural Information Processing Systems 34, pp. 7522--7533, 2021.

• The published version is available here.

• A preprint version is also available at arXiv.

• Our code will be available at OpenReview.

Robust densest subgraph discovery

Atsushi Miyauchi and Akiko Takeda:
Robust densest subgraph discovery,
Proceedings of the 18th IEEE International Conference on Data Mining (ICDM 2018), pp. 1188--1193.

• The published version is available here.

• A preprint is also available at arXiv.

• Our code is publicly available at GitHub.