Kyoto University - System Optimization Laboratory

※ See here for the (assit., assoc.) professor's publications.

Publications from Students and Researchers: Refereed Journal Papers

Jul, 2023 Hardik Tankaria and Nobuo Yamashita
A stochastic variance reduced gradient using Barzilai-Borwein techniques as second order information
Journal of Industrial and Management Optimization, vol. 20, no. 2, pp. 525-547, 2024
Jul, 2023 Hiroki Tanabe, Ellen H. Fukuda and Nobuo Yamashita
New merit functions for multiobjective optimization and their properties
Optimization, to appear, 2023
Jun, 2023 Yuya Yamakawa, Tetsuya Ikegami, Ellen H. Fukuda and Nobuo Yamashita
An equivalent nonlinear optimization model with triangular low-rank factorization for semidefinite programs
Optimization Methods and Software, to appear, 2023
May, 2023 Hiroki Tanabe, Ellen H. Fukuda and Nobuo Yamashita
An accelerated proximal gradient method for multiobjective optimization
Computational Optimization and Applications, vol. 86, pp. 421-455, 2023
May, 2023 Yuki Nishimura, Ellen H. Fukuda and Nobuo Yamashita
Monotonicity for multiobjective accelerated proximal gradient methods
Journal of the Operations Research Society of Japan, to appear, 2023
Jan, 2023 Kosuke Okabe, Yuya Yamakawa and Ellen H. Fukuda
A revised sequential quadratic semidefinite programming method for nonlinear semidefinite optimization
Journal of Industrial and Management Optimization, vol. 19, no. 10, pp. 7777-7794, 2023
Jan, 2023 Atsushi Hori, Yuya Yamakawa and Nobuo Yamashita
Distributionally robust expected residual minimization for stochastic variational inequality problems
Optimization Methods and Software, vol. 38, no. 4, pp. 756-780, 2023
Nov, 2022 Atsushi Hori and Nobuo Yamashita
Two-stage distributionally robust noncooperative games: Existence of Nash equilibrium and its application to Cournot–Nash competition
Journal of Industrial and Management Optimization, vol. 19, no. 9, pp. 6430-6450, 2022
Oct, 2022 Shota Yamanaka and Nobuo Yamashita
Duality of optimization problems with gauge functions
Optimization, to appear, 2022
Apr, 2022 Hiroki Tanabe, Ellen H. Fukuda and Nobuo Yamashita
Convergence rates analysis of multiobjective proximal gradient methods
Optimization Letters, vol. 17, pp. 333-350, 2023
Feb, 2022 Hardik Tankaria, Shinji Sugimoto and Nobuo Yamashita
A regularized limited memory BFGS method for large-scale unconstrained optimization and its efficient implementations
Computational Optimization and Applications, vol. 82, pp. 61-88, 2022
Sep, 2021 Yan Gu and Nobuo Yamashita
A proximal ADMM with the Broyden family for convex optimization problems
Journal of Industrial and Management Optimization, vol. 17, no. 5, pp. 2715-2732, 2021
Apr, 2021 Jumpei Goto and Hiroyuki Sato
Approximated logarithmic maps on Riemannian manifolds and their applications
JSIAM Letters, vol. 13, pp. 17-20, 2021
Mar, 2021 Yan Gu and Nobuo Yamashita
An alternating direction method of multipliers with the BFGS update for structured convex quadratic optimization
Computational and Applied Mathematics, vol. 40, no. 81, 2021
Oct, 2020 Yan Gu and Nobuo Yamashita
Alternating direction method of multipliers with variable metric indefinite proximal terms for convex optimization
Numerical Algebra, Control & Optimization, vol. 10, no. 4, pp. 487-510, 2021
Jun, 2019 Kanako Mita, Ellen H. Fukuda and Nobuo Yamashita
Nonmonotone line searches for unconstrained multiobjective optimization problems
Journal of Global Optimization, vol. 75, pp.63-90, 2019
Jun, 2019 Siti Nor Habibah Binti Hassan, Tomohiro Niimi and Nobuo Yamashita
Augmented Lagrangian method with alternating constraints for nonlinear optimization problems
Journal of Optimization Theory and Applications, vol. 181, pp.883-904, 2019
Mar, 2019 Hiroki Tanabe, Ellen H. Fukuda and Nobuo Yamashita
Proximal gradient methods for multiobjective optimization and their applications
Computational Optimization and Applications, vol. 72, pp.339-361, 2019
Feb, 2019 Yan Gu, Xingju Cai, Deren Han, David Z. W. Wang
A tri-level optimization model for a private road competition problem with traffic equilibrium constraints
European Journal of Operational Research, vol. 273, pp.190-197, 2019
Nov, 2018 Shota Yamanaka and Nobuo Yamashita
Duality of nonconvex optimization with positively homogeneous functions
Computational Optimization and Applications, vol. 71, pp.435-456, 2018
Nov, 2016 Xiaoqin Hua and Nobuo Yamashita
Block coordinate proximal gradient methods with variable Bregman functions for nonsmooth separable optimization
Mathematical Programming, vol. 160, pp.1-32, 2016

Publications from Students and Researchers: Proceedings and Technical Reports

Apr, 2019 Rafael Pedro Longhi
Strategic location model for oil spills response installations considering oil transportation
RIMS Kôkyûroku, vol. 2108, 2019
Apr, 2017 Yan Gu, Xingju Cai, Deren Han, David Z. W. Wang
A note on the new optimization model for traffic problem
RIMS Kôkyûroku, vol. 2027, pp.30-34, 2017
Mar, 2016 Yukihiro Togari, Nobuo Yamashita
A forward-backward splitting method with component-wise lazy evaluation for online structured convex optimization
Technical Report, Department of Applied Mathematics and Physics, Kyoto University, 2016
Jan, 2016 Daisuke Tsuyuguchi, Ellen H. Fukuda, Ming Hu, Masao Fukushima
平滑化法を用いたマルチリーダーフォロワーゲームの解法 (in Japanese)
RIMS Kôkyûroku, vol. 1981, pp.149-157, 2016

Submitted Papers from Students and Researchers

2023 Kangming Chen, Ellen H. Fukuda and Nobuo Yamashita
A proximal gradient method with Bregman distance in multi-objective optimization
2023 Ellen H. Fukuda and Kosuke Okabe
A second-order sequential optimality condition for nonlinear second-order cone programming problems
2023 Keigo Habara, Ellen H. Fukuda and Nobuo Yamashita
Convergence analysis and acceleration of smoothing methods for solving extensive-form games
2023 Shumpei Ariizumi, Yuya Yamakawa and Nobuo Yamashita
Convergence properties of Levenberg-Marquardt methods with generalized regularization terms
2022 Hiroki Tanabe, Ellen H. Fukuda and Nobuo Yamashita
A globally convergent fast iterative shrinkage-thresholding algorithm with a new momentum factor for single and multi-objective convex optimization