See the list of my co-authors here.
40. M. M. Alves, K. Chen and E. H. Fukuda.
An inertial iteratively regularized extragradient method for bilevel variational inequality problems.
Submitted, 2025.
[ pdf ]
39. X. Li, X. Yang, E. Min, Y. Tan, J. Guo, M. Shigeno, N. Chang, H. Chang and E. H. Fukuda.
Efficient hyperbolic graph transformer for collaborative filtering.
Submitted, 2025.
38. L. Chen, Q. Xiao, E. H. Fukuda, X. Chen, K. Yuan and T. Chen.
Efficient first-order optimization on the Pareto set for
multi-objective learning under preference guidance.
Proc. of International Conference on Machine Learning (ICML), spotlight - top 2.6%, 2025.
[ pdf ]
37. K. Chen and E. H. Fukuda. Riemannian conditional gradient methods for composite
optimization problems.
Submitted, 2024.
[ pdf ]
36. H. Li, Y. Yamakawa, E. H. Fukuda and N. Yamashita. A strong second-order sequential
optimality condition for nonlinear programming problems.
To appear in Pacific Journal of Optimization, 2025.
[ pdf ]
35. A. G. Gebrie and E. H. Fukuda. Adaptive generalized conditional gradient method
for multiobjective optimization.
Journal of Optimization Theory and Applications, 206:13, 2025.
[ doi | pdf ]
34. K. Chen, E. H. Fukuda and H. Sato. Nonlinear conjugate gradient method for vector
optimization on Riemannian manifolds with retraction and vector transport.
Applied Mathematics and Computation, 486:129001, 2025. [ doi | pdf ]
33. A. Hori, D. Tsuyuguchi and E. H. Fukuda. A method for multi-leader-multi-follower
games by smoothing the followers' response function.
Journal of Optimization Theory and Applications, 203:305-335, 2024.
[ doi | pdf ]
32. K. Chen, E. H. Fukuda and N. Yamashita. A proximal gradient method with Bregman
distance in multi-objective optimization. Pacific Journal of Optimization, 20(4):809-826, 2024.
[ doi ]
31. E. H. Fukuda and K. Okabe. A second-order sequential optimality condition for
nonlinear second-order cone programming problems. Computational Optimization and Applications, 90:911–939, 2025.
[ doi | pdf ]
30. K. Habara, E. H. Fukuda and N. Yamashita. Convergence analysis and acceleration of smoothing
methods for solving extensive-form games.
Submitted, 2023. [ pdf ]
29. H. Oliveira, M. Kaneko, L. Boukhatem and E. H. Fukuda.
Deep reinforcement learning-aided optimization of multi-interface allocation for short-packet communications.
IEEE Transactions on Cognitive Communications and Networking, 9(3):738-753, 2023.
[ doi | pdf ]
28. Y. Nishimura, E. H. Fukuda and N. Yamashita.
Monotonicity for multiobjective accelerated proximal gradient methods.
Journal of the Operations Research
Society of Japan, 67(1):1-17, 2024.
[ doi | pdf ]
27. H. Tanabe, E. H. Fukuda and N. Yamashita.
A globally convergent fast iterative shrinkage-thresholding algorithm
with a new momentum factor for single and multi-objective convex optimization.
Submitted, 2022. [ pdf ]
26. K. Okabe, Y. Yamakawa and E. H. Fukuda. A revised sequential
quadratic semidefinite programming method for nonlinear semidefinite
optimization. Journal of Industrial and Management Optimization, 19(10):7777-7794, 2023.
[ doi | pdf ]
25. H. Tanabe, E. H. Fukuda and N. Yamashita.
An accelerated proximal gradient method for multiobjective optimization.
Computational Optimization and Applications, 86:421-455, 2023.
[ doi | pdf ]
24. Y. Yamakawa, T. Ikegami, E. H. Fukuda and N. Yamashita.
An equivalent nonlinear optimization model with triangular low-rank factorization
for semidefinite programs.
Optimization Methods and Software, 38(6):1296-1310, 2023.
[ doi | [ pdf ]
23. H. Tanabe, E. H. Fukuda and N. Yamashita.
Convergence rates analysis of multiobjective proximal gradient method.
Optimization Letters, 17:333-350, 2023.
[ doi | pdf ]
22. H. Tanabe, E. H. Fukuda and N. Yamashita.
New merit functions for multiobjective optimization and their properties.
Optimization, 73(13):3821-3858, 2024.
[ doi | pdf ]
21. R. Andreani, E. H. Fukuda, G. Haeser, H. Ramírez, D. O. Santos, P. J. S. Silva and T. P. Silveira.
Erratum to: new constraint qualifications and optimality conditions for second order cone programs.
Set-Valued and Variational Analysis, 30:329-333, 2022.
[ doi ]
20. E. H. Fukuda, L. M. Mito and G. Haeser. On the weak second-order optimality
condition for nonlinear semidefinite and second-order cone programming.
Set-Valued and Variational Analysis, 31(15), 2023.
[ doi | pdf ]
19. E. H. Fukuda, L. M. Graña Drummond and A. M. Masuda. A conjugate
directions-type procedure for quadratic multiobjective optimization.
Optimization, 71(2):419-437, 2022.
[ doi | pdf ]
18. T. H, L. Dinh, M. Kaneko, E. H. Fukuda and L. Boukhatem. Energy efficient resource
allocation optimization in fog radio access networks with outdated channel knowledge.
IEEE Transactions on Green Communications and Networking, 5(1):146-159, 2021.
[ doi | pdf ]
17. R. Andreani, E. H. Fukuda, G. Haeser, D. O. Santos and L. D. Secchin.
On the use of Jordan algebras for improving global convergence of an augmented
Lagrangian method in nonlinear semidefinite programming. Computational
Optimization and Applications, 79:633-648, 2021.
[ doi |
pdf ]
16. R. Andreani, E. H. Fukuda, G. Haeser, D. O. Santos and L. D. Secchin.
Optimality conditions for nonlinear second-order cone programming and
symmetric cone programming. Journal of
Optimization Theory and Applications, 200:1-33, 2024.
[ doi |
pdf ]
15. L. Amichi, M. Kaneko, E. H. Fukuda, N. El Rachkidy and A. Guitton.
Joint allocation strategies of power and spreading factors with imperfect
orthogonality in LoRa networks. IEEE Transactions on Communications, 68(6):3750-3765, 2020.
[ doi | pdf ]
14. K. Mita, E. H. Fukuda and N. Yamashita.
Nonmonotone line searches for unconstrained multiobjective optimization problems.
Journal of Global Optimization, 75(1):63-90, 2019.
[ doi | pdf ]
13. E. H. Fukuda, L. M. Graña Drummond and F. M. P. Raupp.
A barrier-type method for multiobjective
optimization. Optimization, 69(11):2471-2487, 2020.
[ doi | pdf ]
12. H. Tanabe, E. H. Fukuda and N. Yamashita.
Proximal gradient methods for multiobjective
optimization and their applications. Computational
Optimization and Applications, 72(2):339-361, 2019.
[ doi ]
11. B. F. Lourenço, E. H. Fukuda and M. Fukushima. Optimality
conditions for problems over symmetric
cones and a simple augmented Lagrangian
method. Mathematics of Operations Research, 43(4):1233-1251, 2018.
[ doi |
pdf ]
10. E. H. Fukuda and B. F. Lourenço. Exact augmented
Lagrangian functions for nonlinear
semidefinite programming. Computational
Optimization and Applications, 71(2):457-482,
2018. [ doi
| pdf ]
9. B. F. Lourenço, E.
H. Fukuda and M. Fukushima. Optimality
conditions for nonlinear semidefinite
programming via squared slack
variables. Mathematical Programming,
168(1-2):177-200, 2018.
[ doi |
pdf ]
8. E. H. Fukuda and M. Fukushima. A
note on the squared slack variables
technique for nonlinear optimization.
Journal of the Operations Research
Society of Japan, 60(3):262-270, 2017.
[ doi
| pdf ]
7. E. H. Fukuda and M.
Fukushima. The use of squared slack
variables in nonlinear second-order cone
programming. Journal of
Optimization Theory and Applications,
170(2):394-418, 2016.
[ doi |
pdf ]
6. E. H. Fukuda, L. M. Graña Drummond and F. M. P. Raupp. An
external penalty-type method for
multicriteria.
TOP, 24(2):493-513, 2016.
[ doi |
pdf ]
5. E. H. Fukuda and L. M. Graña
Drummond. A survey on multiobjective
descent methods. Pesquisa
Operacional, 34(3):585-620,
2014. [ doi ]
4. E. H. Fukuda and L. M. Graña
Drummond. Inexact projected gradient method
for vector optimization. Computational
Optimization and Applications,
54(3):473-493, 2013.
[ doi |
pdf ]
3. R. Andreani, E. H. Fukuda and
P. J. S. Silva. A Gauss-Newton approach
for solving constrained optimization
problems using differentiable exact
penalties.
Journal of Optimization Theory and
Applications, 156(2):417-449, 2013.
[ doi |
pdf ]
2. E. H. Fukuda, P. J. S. Silva and
M. Fukushima. Differentiable exact
penalty functions for nonlinear
second-order cone programs. SIAM Journal
on Optimization, 22(4):1607-1633,
2012. [ doi |
pdf ]
1. E. H. Fukuda and L. M. Graña
Drummond. On the convergence of the
projected gradient method for vector
optimization.
Optimization, 60(8-9):1009-1021, 2011.
[ doi |
pdf ]
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