See the list of my co-authors here.
36. H. Li, Y. Yamakawa, E. H. Fukuda and N. Yamashita. A strong second-order sequential
optimality condition for nonlinear programming problems.
Submitted, 2024.
35. A. G. Gebrie and E. H. Fukuda. Adaptive generalized conditional gradient method
for multiobjective optimization.
Submitted, 2024. [ 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. To appear in
Journal of Optimization Theory and Applications, 2024. [ doi | pdf ]
32. K. Chen, E. H. Fukuda and N. Yamashita. A proximal gradient method with Bregman
distance in multi-objective optimization. To appear in Pacific Journal of Optimization, 2023.
31. E. H. Fukuda and K. Okabe. A second-order sequential optimality condition for
nonlinear second-order cone programming problems. Submitted, 2023.
[ 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.
To appear in Optimization, 2023.
[ doi | pdf ]
21. R. Andreani, E. H. Fukuda, G. Haeser, H. RamÃrez, 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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